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1.
Kachouri, Rostom.
Classification multi-modèles des images dans les bases hétérogènes : Multi-model image classification in heterogeneous databases.
Degree: Docteur es, Traitement des images et du signal, 2010, Evry-Val d'Essonne; École nationale d'Ingénieurs de Sfax (Tunisie)
URL: http://www.theses.fr/2010EVRY0022 
► La reconnaissance d'images est un domaine de recherche qui a été largement étudié par la communauté scientifique. Les travaux proposés dans ce cadre s'adressent principalement…
(more)
▼ La reconnaissance d'images est un domaine de recherche qui a été largement étudié par la communauté scientifique. Les travaux proposés dans ce cadre s'adressent principalement aux diverses applications des systèmes de vision par ordinateur et à la catégorisation des images issues de plusieurs sources. Dans cette thèse, on s'intéresse particulièrement aux systèmes de reconnaissance d'images par le contenu dans les bases hétérogènes. Les images dans ce type de bases appartiennent à différents concepts et représentent un contenu hétérogène. Pour ce faire, une large description permettant d'assurer une représentation fiable est souvent requise. Cependant, les caractéristiques extraites ne sont pas nécessairement toutes appropriées pour la discrimination des différentes classes d'images qui existent dans une base donnée d'images. D'où, la nécessité de sélection des caractéristiques pertinentes selon le contenu de chaque base. Dans ce travail, une méthode originale de sélection adaptative est proposée. Cette méthode permet de considérer uniquement les caractéristiques qui sont jugées comme les mieux adaptées au contenu de la base d'image utilisée. Par ailleurs, les caractéristiques sélectionnées ne disposent pas généralement des mêmes performances. En conséquence, l'utilisation d'un algorithme de classification, qui s'adapte aux pouvoirs discriminants des différentes caractéristiques sélectionnées par rapport au contenu de la base d'images utilisée, est vivement recommandée. Dans ce contexte, l'approche d'apprentissage par noyaux multiples est étudiée et une amélioration des méthodes de pondération des noyaux est présentée. Cette approche s'avère incapable de décrire les relations non-linéaires des différents types de description. Ainsi, nous proposons une nouvelle méthode de classification hiérarchique multi-modèles permettant d'assurer une combinaison plus flexible des caractéristiques multiples. D'après les expérimentations réalisées, cette nouvelle méthode de classification assure des taux de reconnaissance très intéressants. Enfin, les performances de la méthode proposée sont mises en évidence à travers une comparaison avec un ensemble d'approches cité dans la littérature récente du domaine.
Image recognition is widely studied by the scientific community. The proposed research in this field is addressed to various applications of computer vision systems and multiple source image categorization. This PhD dissertation deals particularly with content based image recognition systems in heterogeneous databases. Images in this kind of databases belong to different concepts and represent a heterogeneous content. In this case and to ensure a reliable representation, a broad description is often required. However, the extracted features are not necessarily always suitable for the considered image database. Hence, the need of selecting relevant features based on the content of each database. In this work, an adaptive selection method is proposed. It considers only the most adapted features according to the used image…
Advisors/Committee Members: Maaref, Hichem (thesis director), Derbel, Nabil (thesis director).
Subjects/Keywords: Extraction d'attributs; Feature extraction
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APA (6th Edition):
Kachouri, R. (2010). Classification multi-modèles des images dans les bases hétérogènes : Multi-model image classification in heterogeneous databases. (Doctoral Dissertation). Evry-Val d'Essonne; École nationale d'Ingénieurs de Sfax (Tunisie). Retrieved from http://www.theses.fr/2010EVRY0022
Chicago Manual of Style (16th Edition):
Kachouri, Rostom. “Classification multi-modèles des images dans les bases hétérogènes : Multi-model image classification in heterogeneous databases.” 2010. Doctoral Dissertation, Evry-Val d'Essonne; École nationale d'Ingénieurs de Sfax (Tunisie). Accessed February 26, 2020.
http://www.theses.fr/2010EVRY0022.
MLA Handbook (7th Edition):
Kachouri, Rostom. “Classification multi-modèles des images dans les bases hétérogènes : Multi-model image classification in heterogeneous databases.” 2010. Web. 26 Feb 2020.
Vancouver:
Kachouri R. Classification multi-modèles des images dans les bases hétérogènes : Multi-model image classification in heterogeneous databases. [Internet] [Doctoral dissertation]. Evry-Val d'Essonne; École nationale d'Ingénieurs de Sfax (Tunisie); 2010. [cited 2020 Feb 26].
Available from: http://www.theses.fr/2010EVRY0022.
Council of Science Editors:
Kachouri R. Classification multi-modèles des images dans les bases hétérogènes : Multi-model image classification in heterogeneous databases. [Doctoral Dissertation]. Evry-Val d'Essonne; École nationale d'Ingénieurs de Sfax (Tunisie); 2010. Available from: http://www.theses.fr/2010EVRY0022
University of Waterloo
2.
Amelard, Robert.
High-Level Intuitive Features (HLIFs) for Melanoma Detection.
Degree: 2013, University of Waterloo
URL: http://hdl.handle.net/10012/7761
► Feature extraction of segmented skin lesions is a pivotal step for implementing accurate decision support systems. Existing feature sets combine many ad-hoc calculations and are…
(more)
▼ Feature extraction of segmented skin lesions is a pivotal step for implementing accurate decision support systems. Existing feature sets combine many ad-hoc calculations and are unable to easily provide intuitive diagnostic reasoning. This thesis presents the design and evaluation of a set of features for objectively detecting melanoma in an intuitive and accurate manner. We call these "high-level intuitive features" (HLIFs).
The current clinical standard for detecting melanoma, the deadliest form of skin cancer, is visual inspection of the skin's surface. A widely adopted rule for detecting melanoma is the "ABCD" rule, whereby the doctor identifies the presence of Asymmetry, Border irregularity, Colour patterns, and Diameter. The adoption of specialized medical devices for this cause is extremely slow due to the added temporal and financial burden. Therefore, recent research efforts have focused on detection support systems that analyse images acquired with standard consumer-grade camera images of skin lesions. The central benefit of these systems is the provision of technology with low barriers to adoption. Recently proposed skin lesion feature sets have been large sets of low-level features attempting to model the widely adopted ABCD criteria of melanoma. These result in high-dimensional feature spaces, which are computationally expensive and sparse due to the lack of available clinical data. It is difficult to convey diagnostic rationale using these feature sets due to their inherent ad-hoc mathematical nature.
This thesis presents and applies a generic framework for designing HLIFs for decision support systems relying on intuitive observations. By definition, a HLIF is designed explicitly to model a human-observable characteristic such that the feature score can be intuited by the user. Thus, along with the classification label, visual rationale can be provided to further support the prediction. This thesis applies the HLIF framework to design 10 HLIFs for skin cancer detection, following the ABCD rule. That is, HLIFs modeling asymmetry, border irregularity, and colour patterns are presented.
This thesis evaluates the effectiveness of HLIFs in a standard classification setting. Using publicly-available images obtained in unconstrained environments, the set of HLIFs is compared with and against a recently published low-level feature set. Since the focus is on evaluating the features, illumination correction and manually-defined segmentations are used, along with a linear classification scheme. The promising results indicate that HLIFs capture more relevant information than low-level features, and that concatenating the HLIFs to the low-level feature set results in improved accuracy metrics. Visual intuitive information is provided to indicate the ability of providing intuitive diagnostic reasoning to the user.
Subjects/Keywords: feature extraction; melanoma
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APA (6th Edition):
Amelard, R. (2013). High-Level Intuitive Features (HLIFs) for Melanoma Detection. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/7761
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Amelard, Robert. “High-Level Intuitive Features (HLIFs) for Melanoma Detection.” 2013. Thesis, University of Waterloo. Accessed February 26, 2020.
http://hdl.handle.net/10012/7761.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Amelard, Robert. “High-Level Intuitive Features (HLIFs) for Melanoma Detection.” 2013. Web. 26 Feb 2020.
Vancouver:
Amelard R. High-Level Intuitive Features (HLIFs) for Melanoma Detection. [Internet] [Thesis]. University of Waterloo; 2013. [cited 2020 Feb 26].
Available from: http://hdl.handle.net/10012/7761.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Amelard R. High-Level Intuitive Features (HLIFs) for Melanoma Detection. [Thesis]. University of Waterloo; 2013. Available from: http://hdl.handle.net/10012/7761
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Universitat de Valencia
3.
López Iñesta, Emilia.
Aprendizaje de similitudes entre pares de objetos mediante clasificación supervisada
.
Degree: 2017, Universitat de Valencia
URL: http://hdl.handle.net/10550/61001
► El uso de medidas de similitud, distancias o métricas se encuentra en la base del funcionamiento de numerosas técnicas estándar de clasificación, resultando además, una…
(more)
▼ El uso de medidas de similitud, distancias o métricas se encuentra en la base del funcionamiento de numerosas técnicas estándar de clasificación, resultando además, una tarea fundamental e importante en las áreas de estudio del Aprendizaje Automático (Machine Learning) y el Reconocimiento de Patrones (Pattern Recognition). Dado que el cálculo de la similitud entre dos objetos puede ser muy diferente en función del contexto, la construcción inteligente de estas medidas a partir de los datos disponibles, puede ayudar en la obtención de clasificadores más robustos y mejorar los resultados en la tarea específica que se propone resolver.
En los últimos años, el aprendizaje de métricas (Metric Learning) y medidas de similitud (Similarity Learning) ha recibido un creciente interés de la comunidad científica. Dada la información disponible en forma de ejemplos etiquetados con una categoría o clase, el objetivo del aprendizaje de métricas es aprender una distancia métrica de acuerdo al siguiente principio: las distancias entre pares similares (es decir, de la misma clase) han de ser pequeñas, mientras que las distancias entre pares diferentes (es decir, de diferentes clases) han de ser mayores. De la misma manera, el aprendizaje de similitud intenta aprender una función de similitud que asocie grandes puntuaciones (scores) a pares similares y pequeñas puntuaciones a pares diferentes. Un caso particular del aprendizaje de similitudes consiste en el empleo de métodos de clasificación para el aprendizaje de medidas de similitud (Classification-based Similarity Learning). En todos estos métodos, el rendimiento depende en gran medida de la representación de las características de los datos disponibles.Así, en esta Tesis se presenta un método de clasificación enriquecido que sigue un enfoque híbrido que combina la extracción de características (
Feature Extraction) y la ampliación de las mismas (
Feature Expansion). En particular, se propone una transformación de datos y el uso de un conjunto de distancias métricas y no métricas para complementar y enriquecer la información proporcionada por los vectores de características de los ejemplos de entrenamiento. Si bien es cierto que esto aumenta la dimensión del problema en cuestión, también supone una inyección de conocimiento adicional debido a que el uso de las medidas de distancias supone un emparejamiento implícito entre los vectores de características de dos objetos. Además, se analiza si la nueva información añadida compensa el aumento de dimensión que ello implica, así como la influencia de los diferentes formatos de datos de entrada y el tamaño de entrenamiento sobre el rendimiento del clasificador.
La propuesta se compara con métodos de aprendizaje de métricas y los resultados obtenidos muestran rendimientos comparables en favor del método propuesto en distintos contextos y empleando diferentes bases de datos.
Advisors/Committee Members: Arevalillo Herráez, Miguel (advisor).
Subjects/Keywords: distances;
feature extraction;
feature expansion;
classification
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APA (6th Edition):
López Iñesta, E. (2017). Aprendizaje de similitudes entre pares de objetos mediante clasificación supervisada
. (Doctoral Dissertation). Universitat de Valencia. Retrieved from http://hdl.handle.net/10550/61001
Chicago Manual of Style (16th Edition):
López Iñesta, Emilia. “Aprendizaje de similitudes entre pares de objetos mediante clasificación supervisada
.” 2017. Doctoral Dissertation, Universitat de Valencia. Accessed February 26, 2020.
http://hdl.handle.net/10550/61001.
MLA Handbook (7th Edition):
López Iñesta, Emilia. “Aprendizaje de similitudes entre pares de objetos mediante clasificación supervisada
.” 2017. Web. 26 Feb 2020.
Vancouver:
López Iñesta E. Aprendizaje de similitudes entre pares de objetos mediante clasificación supervisada
. [Internet] [Doctoral dissertation]. Universitat de Valencia; 2017. [cited 2020 Feb 26].
Available from: http://hdl.handle.net/10550/61001.
Council of Science Editors:
López Iñesta E. Aprendizaje de similitudes entre pares de objetos mediante clasificación supervisada
. [Doctoral Dissertation]. Universitat de Valencia; 2017. Available from: http://hdl.handle.net/10550/61001
California State University – Sacramento
4.
Balasubramanian, Sinduja.
Text mining on Amazon reviews to extract feature based feedback.
Degree: MS, Computer Science, 2017, California State University – Sacramento
URL: http://hdl.handle.net/10211.3/198628
► Analyzing the feedback on a product or a service helps to improve the quality of the product or service. That way, reviews from online shopping…
(more)
▼ Analyzing the feedback on a product or a service helps to improve the quality of the product or service. That way, reviews from online shopping sites (such as Amazon) not only help a consumer to buy a product but also can help a manufacturer or seller to know the pros and cons of their product. Amazon star ratings alone is not enough for this. One should go through the text reviews to know specifically which
feature of the product is lacking customer satisfaction. But a product may have thousands of reviews and it???s hard for a person to go through all the reviews. Hence, we need a system which can give a statistical report on the number of reviewers not satisfied with a specific
feature of a product. This project enables the user to view
feature based review for a selected category of Amazon products. For a particular product the percentage of dissatisfied reviewers for each major
feature of the product can be viewed. The dataset which includes product details and customer reviews for each product are collected from Amazon.com. The implementation of this system is achieved by using MongoDB and R. The statistical results that are generated by the system are visualized with the help of Tableau software. The Amazon reviews undergo Natural Language Processing and text mining to identify major features of the product. Then a deep sentiment analysis is made to identify the polarity (positive or negative) of each review. This project can be further developed to a user interactive web or mobile application where user can choose categories and products and have better visualization of results. Implementation required integrating both data mining and artificial intelligence techniques.
Advisors/Committee Members: Gordon, V. Scott.
Subjects/Keywords: NLP; Text mining; Feature extraction
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APA (6th Edition):
Balasubramanian, S. (2017). Text mining on Amazon reviews to extract feature based feedback. (Masters Thesis). California State University – Sacramento. Retrieved from http://hdl.handle.net/10211.3/198628
Chicago Manual of Style (16th Edition):
Balasubramanian, Sinduja. “Text mining on Amazon reviews to extract feature based feedback.” 2017. Masters Thesis, California State University – Sacramento. Accessed February 26, 2020.
http://hdl.handle.net/10211.3/198628.
MLA Handbook (7th Edition):
Balasubramanian, Sinduja. “Text mining on Amazon reviews to extract feature based feedback.” 2017. Web. 26 Feb 2020.
Vancouver:
Balasubramanian S. Text mining on Amazon reviews to extract feature based feedback. [Internet] [Masters thesis]. California State University – Sacramento; 2017. [cited 2020 Feb 26].
Available from: http://hdl.handle.net/10211.3/198628.
Council of Science Editors:
Balasubramanian S. Text mining on Amazon reviews to extract feature based feedback. [Masters Thesis]. California State University – Sacramento; 2017. Available from: http://hdl.handle.net/10211.3/198628
University of Adelaide
5.
Baseri Huddin, Aqilah.
An investigation of automatic feature extraction for clustered microcalcifications on digital mammograms.
Degree: 2015, University of Adelaide
URL: http://hdl.handle.net/2440/97912
► Mammography is a common imaging modality used for breast screening. The limitations in reading mammogram images manually by radiologists have motivated an interest to the…
(more)
▼ Mammography is a common imaging modality used for breast screening. The limitations in reading mammogram images manually by radiologists have motivated an interest to the use of computerised systems to aid the process. Computer-aided diagnosis (CAD) systems have been widely used to assist radiologists in making decision; either for detection, CADe, or for diagnosis, CADx, of the anomalies in mammograms. This thesis aims to improve the sensitivity of the CADx system by proposing novel
feature extraction techniques. Previous works have shown that multiple resolution images provide useful information for classification. The wavelet transform is one of the techniques that is commonly used to produce multiple resolution images, and is used to extract features from the produced sub-images for classification of microcalcification clusters in mammograms. However, the fixed directionality produced by the transform limit the opportunity to extract further useful features that may contain information associated with the malignancy of the clusters. This has driven the thesis to experiment on multiple orientation and multiple resolution images for providing features for microcalcification classification purposes. Extensive and original experiments are conducted to seek whether the multiple orientation and multiple resolution analysis of microcalcification clusters features are useful for classification. Results show that the proposed method achieves an accuracy of 78.3%, and outperforms the conventional wavelet transform, which achieves an accuracy of 64.9%. A
feature selection step using Principal Component Analysis (PCA) is employed to reduce the number of the features as well as the complexity of the system. The overall result shows that the accuracy of the system when 2-features from steerable pyramid filtering are used as input achieved 85.5% as opposed to 2-features from conventional wavelet transform, which achieves an accuracy of 69.9%. In addition, the effectiveness of the diagnosis system also depends on the classifier. Deep belief networks have demonstrated to be able to extract high-level of input representations. The ability of greedy learning in deep networks provide a highly non-linear mapping of the input and the output. The advantage of DBN in being able to analyse complex patterns, in this thesis, is exploited for classification of microcalcification clusters into benign or malignant sets. An extensive research experiment is conducted to use DBN in extracting features for microcalcification classification. The experiment of using DBN solely as a
feature extractor and classifier of raw pixel microcalcification images shows no significant improvement. Therefore, a novel technique using filtered images is proposed, so that a DBN will extract features from the filtered images. The analysis result shows an improvement in accuracy from 47.9% to 60.8% when the technique is applied. With these new findings, it may contribute to the identification of the microcalcification clusters in mammograms.
Advisors/Committee Members: Ng, Brian Walter (advisor), Abbott, Derek (advisor), School of Electrical and Electronic Engineering (school).
Subjects/Keywords: microcalcifications; mammograms; feature extraction
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APA (6th Edition):
Baseri Huddin, A. (2015). An investigation of automatic feature extraction for clustered microcalcifications on digital mammograms. (Thesis). University of Adelaide. Retrieved from http://hdl.handle.net/2440/97912
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Baseri Huddin, Aqilah. “An investigation of automatic feature extraction for clustered microcalcifications on digital mammograms.” 2015. Thesis, University of Adelaide. Accessed February 26, 2020.
http://hdl.handle.net/2440/97912.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Baseri Huddin, Aqilah. “An investigation of automatic feature extraction for clustered microcalcifications on digital mammograms.” 2015. Web. 26 Feb 2020.
Vancouver:
Baseri Huddin A. An investigation of automatic feature extraction for clustered microcalcifications on digital mammograms. [Internet] [Thesis]. University of Adelaide; 2015. [cited 2020 Feb 26].
Available from: http://hdl.handle.net/2440/97912.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Baseri Huddin A. An investigation of automatic feature extraction for clustered microcalcifications on digital mammograms. [Thesis]. University of Adelaide; 2015. Available from: http://hdl.handle.net/2440/97912
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
6.
Cho, Sungjoon.
Dermal Radiomics: a new approach for computer-aided melanoma screening system.
Degree: 2016, University of Waterloo
URL: http://hdl.handle.net/10012/10657
► Skin cancer is the most common form of cancer in North America, and melanoma is the most dangerous type of skin cancer. Melanoma originates from…
(more)
▼ Skin cancer is the most common form of cancer in North America, and melanoma is the most dangerous type of skin cancer. Melanoma originates from melanocytes in the epidermis and has a high tendency to develop away from the skin surface and cause metastasis through the bloodstream. Early diagnosis is known to help improve survival rates. Under the current diagnosis, the initial examination of the potential melanoma patient is done via naked eye screening or standard photographic images of the lesion. From this, the accuracy of diagnosis varies depending on the expertise of the clinician.
Radiomics is a recent cancer diagnostic tool that centers around the high throughput extraction of quantitative and mineable imaging features from medical images to identify tumor phenotypes. Radiomics focuses on optimizing a large number of features through computational approaches to develop a decision support system for improving individualized treatment selection and monitoring. While radiomics has shown great promise for screening and analyzing di erent forms of cancer such as lung cancer and prostate cancer, to the best of our knowledge, radiomics has not been previously adopted for skin cancer,
especially melanoma.
This work presents a dermal radiomics framework, which is a novel computer-aided melanoma diagnosis. While most computer-aided melanoma screening systems follow the conventional diagnostic scheme, the proposed work utilizes the physiological biomarker information. To extract physiological biomarkers, non-linear random forest inverse light-skin interaction model is proposed. The construction of dermal radiomics sequence is followed using the extracted physiological biomarkers, and the dermal radiomics framework for melanoma is completed by constructing diagnostic decision system based on random
forest classi cation algorithm.
Subjects/Keywords: classification; melanoma; feature extraction
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APA ·
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APA (6th Edition):
Cho, S. (2016). Dermal Radiomics: a new approach for computer-aided melanoma screening system. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/10657
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Cho, Sungjoon. “Dermal Radiomics: a new approach for computer-aided melanoma screening system.” 2016. Thesis, University of Waterloo. Accessed February 26, 2020.
http://hdl.handle.net/10012/10657.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Cho, Sungjoon. “Dermal Radiomics: a new approach for computer-aided melanoma screening system.” 2016. Web. 26 Feb 2020.
Vancouver:
Cho S. Dermal Radiomics: a new approach for computer-aided melanoma screening system. [Internet] [Thesis]. University of Waterloo; 2016. [cited 2020 Feb 26].
Available from: http://hdl.handle.net/10012/10657.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Cho S. Dermal Radiomics: a new approach for computer-aided melanoma screening system. [Thesis]. University of Waterloo; 2016. Available from: http://hdl.handle.net/10012/10657
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Georgia Tech
7.
Kong, Tae Woon.
Assessing Self-Similarity in Redundant Complex and Quaternion Wavelet Domains: Theory and Applications.
Degree: PhD, Industrial and Systems Engineering, 2019, Georgia Tech
URL: http://hdl.handle.net/1853/61244
► Theoretical self-similar processes have been an essential tool for modeling a wide range of real-world signals or images that describe phenomena in engineering, physics, medicine,…
(more)
▼ Theoretical self-similar processes have been an essential tool for modeling a wide range of real-world signals or images that describe phenomena in engineering, physics, medicine, biology, economics, geology, chemistry, and so on. However, it is often difficult for general modeling methods to quantify a self-similarity due to irregularities in the signals or images. Wavelet-based spectral tools have become standard solutions for such problems in signal and image processing and achieved outstanding performances in real applications.
This thesis proposes three novel wavelet-based spectral tools to improve the assessment of self-similarity.
First, we propose spectral tools based on non-decimated complex wavelet transforms implemented by their matrix formulation. A structural redundancy in non-decimated wavelets and a componential redundancy in complex wavelets act in a synergy when extracting wavelet-based informative descriptors.
Next, we step into the quaternion domain and propose a matrix-formulation for non-decimated quaternion wavelet transforms and define spectral tools for use in machine learning tasks. We define non-decimated quaternion wavelet spectra based on the modulus and three phase-dependent statistics as low-dimensional summaries for 1-D signals or 2-D images.
Finally, we suggest a dual wavelet spectra based on non-decimated wavelet transform in real, complex, and quaternion domains. This spectra is derived from a new perspective that draws on the link of energies of the signal with the temporal or spatial scales in the multiscale representations.
Advisors/Committee Members: Vidakovic, Brani (advisor), Mei, Yajun (committee member), Paynabar, Kamran (committee member), Kang, Sung Ha (committee member), Lee, Kichun (committee member).
Subjects/Keywords: Wavelets; Classification; Feature Extraction
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APA ·
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APA (6th Edition):
Kong, T. W. (2019). Assessing Self-Similarity in Redundant Complex and Quaternion Wavelet Domains: Theory and Applications. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/61244
Chicago Manual of Style (16th Edition):
Kong, Tae Woon. “Assessing Self-Similarity in Redundant Complex and Quaternion Wavelet Domains: Theory and Applications.” 2019. Doctoral Dissertation, Georgia Tech. Accessed February 26, 2020.
http://hdl.handle.net/1853/61244.
MLA Handbook (7th Edition):
Kong, Tae Woon. “Assessing Self-Similarity in Redundant Complex and Quaternion Wavelet Domains: Theory and Applications.” 2019. Web. 26 Feb 2020.
Vancouver:
Kong TW. Assessing Self-Similarity in Redundant Complex and Quaternion Wavelet Domains: Theory and Applications. [Internet] [Doctoral dissertation]. Georgia Tech; 2019. [cited 2020 Feb 26].
Available from: http://hdl.handle.net/1853/61244.
Council of Science Editors:
Kong TW. Assessing Self-Similarity in Redundant Complex and Quaternion Wavelet Domains: Theory and Applications. [Doctoral Dissertation]. Georgia Tech; 2019. Available from: http://hdl.handle.net/1853/61244
Halmstad University
8.
Mühlfellner, Peter.
Selection, Analysis and Implementationof Image-based Feature Extraction Approaches for a Heterogenous, Modular and FPGA-based Architecture for Camera-based Driver Assistance Systems.
Degree: Intelligent systems (IS-lab), 2011, Halmstad University
URL: http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-16377
► We propose a scalable and fexible hardware architecture for the extraction of image features, used in conjunction with an attentional cascade classifier for appearance-based…
(more)
▼ We propose a scalable and fexible hardware architecture for the extraction of image features, used in conjunction with an attentional cascade classifier for appearance-based object detection. Individual feature processors calculate feature-values in parallel, using parameter-sets and image data that is distributed via BRAM buffers. This approach can provide high utilization- and throughput-rates for a cascade classifier. Unlike previous hardware implementations, we are able to flexibly assign feature processors to either work on a single- or multiple image windows in parallel, depending on the complexity of the current cascade stage. The core of the architecture was implemented in the form of a streaming based FPGA design, and validated in simulation, synthesis, as well as via the use of a Logic Analyser for the verification of the on-chip functionality. For the given implementation, we focused on the design of Haar-like feature processors, but feature processors for a variety of heterogenous feature types, such as Gabor-like features, can also be accomodated by the proposed hardware architecture.
Subjects/Keywords: Object Detection; Feature Extraction; FPGA
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APA (6th Edition):
Mühlfellner, P. (2011). Selection, Analysis and Implementationof Image-based Feature Extraction Approaches for a Heterogenous, Modular and FPGA-based Architecture for Camera-based Driver Assistance Systems. (Thesis). Halmstad University. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-16377
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Mühlfellner, Peter. “Selection, Analysis and Implementationof Image-based Feature Extraction Approaches for a Heterogenous, Modular and FPGA-based Architecture for Camera-based Driver Assistance Systems.” 2011. Thesis, Halmstad University. Accessed February 26, 2020.
http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-16377.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Mühlfellner, Peter. “Selection, Analysis and Implementationof Image-based Feature Extraction Approaches for a Heterogenous, Modular and FPGA-based Architecture for Camera-based Driver Assistance Systems.” 2011. Web. 26 Feb 2020.
Vancouver:
Mühlfellner P. Selection, Analysis and Implementationof Image-based Feature Extraction Approaches for a Heterogenous, Modular and FPGA-based Architecture for Camera-based Driver Assistance Systems. [Internet] [Thesis]. Halmstad University; 2011. [cited 2020 Feb 26].
Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-16377.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Mühlfellner P. Selection, Analysis and Implementationof Image-based Feature Extraction Approaches for a Heterogenous, Modular and FPGA-based Architecture for Camera-based Driver Assistance Systems. [Thesis]. Halmstad University; 2011. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-16377
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
University of KwaZulu-Natal
9.
Adebanjo, Hannah Morenike.
Investigation of feature extraction algorithms and techniques for hyperspectral images.
Degree: 2017, University of KwaZulu-Natal
URL: https://researchspace.ukzn.ac.za/handle/10413/16758
► Hyperspectral images (HSIs) are remote-sensed images that are characterized by very high spatial and spectral dimensions and nd applications, for example, in land cover classi…
(more)
▼ Hyperspectral images (HSIs) are remote-sensed images that are characterized
by very high spatial and spectral dimensions and nd applications, for example,
in land cover classi cation, urban planning and management, security and food
processing. Unlike conventional three bands RGB images, their high
dimensional data space creates a challenge for traditional image processing
techniques which are usually based on the assumption that there exists
su cient training samples in order to increase the likelihood of high
classi cation accuracy. However, the high cost and di culty of obtaining
ground truth of hyperspectral data sets makes this assumption unrealistic and
necessitates the introduction of alternative methods for their processing.
Several techniques have been developed in the exploration of the rich spectral
and spatial information in HSIs. Speci cally,
feature extraction (FE)
techniques are introduced in the processing of HSIs as a necessary step before
classi cation. They are aimed at transforming the high dimensional data of the
HSI into one of a lower dimension while retaining as much spatial and/or
spectral information as possible. In this research, we develop semi-supervised
FE techniques which combine features of supervised and unsupervised
techniques into a single framework for the processing of HSIs. Firstly, we
developed a
feature extraction algorithm known as Semi-Supervised Linear
Embedding (SSLE) for the
extraction of features in HSI. The algorithm
combines supervised Linear Discriminant Analysis (LDA) and unsupervised
Local Linear Embedding (LLE) to enhance class discrimination while also
preserving the properties of classes of interest. The technique was developed
based on the fact that LDA extracts features from HSIs by discriminating
between classes of interest and it can only extract C 1 features provided there
are C classes in the image by extracting features that are equivalent to the
number of classes in the HSI. Experiments show that the SSLE algorithm
overcomes the limitation of LDA and extracts features that are equivalent to
ii
iii
the number of classes in HSIs. Secondly, a graphical manifold dimension
reduction (DR) algorithm known as Graph Clustered Discriminant Analysis
(GCDA) is developed. The algorithm is developed to dynamically select labeled
samples from the pool of available unlabeled samples in order to complement
the few available label samples in HSIs. The selection is achieved by entwining
K-means clustering with a semi-supervised manifold discriminant analysis.
Using two HSI data sets, experimental results show that GCDA extracts
features that are equivalent to the number of classes with high classi cation
accuracy when compared with other state-of-the-art techniques. Furthermore,
we develop a window-based partitioning approach to preserve the spatial
properties of HSIs when their features are being extracted. In this approach,
the HSI is partitioned along its spatial dimension into n windows and the
covariance matrices of each…
Advisors/Committee Members: Tapamo, Jules-Raymond. (advisor).
Subjects/Keywords: Hyperspectral images.; Feature extraction algorithms.
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APA (6th Edition):
Adebanjo, H. M. (2017). Investigation of feature extraction algorithms and techniques for hyperspectral images. (Thesis). University of KwaZulu-Natal. Retrieved from https://researchspace.ukzn.ac.za/handle/10413/16758
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Adebanjo, Hannah Morenike. “Investigation of feature extraction algorithms and techniques for hyperspectral images.” 2017. Thesis, University of KwaZulu-Natal. Accessed February 26, 2020.
https://researchspace.ukzn.ac.za/handle/10413/16758.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Adebanjo, Hannah Morenike. “Investigation of feature extraction algorithms and techniques for hyperspectral images.” 2017. Web. 26 Feb 2020.
Vancouver:
Adebanjo HM. Investigation of feature extraction algorithms and techniques for hyperspectral images. [Internet] [Thesis]. University of KwaZulu-Natal; 2017. [cited 2020 Feb 26].
Available from: https://researchspace.ukzn.ac.za/handle/10413/16758.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Adebanjo HM. Investigation of feature extraction algorithms and techniques for hyperspectral images. [Thesis]. University of KwaZulu-Natal; 2017. Available from: https://researchspace.ukzn.ac.za/handle/10413/16758
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Penn State University
10.
Xia, Wentao.
VEHICULAR FSO COMMUNICATION SYSTEM APPLYING REAL-TIME RECOGNITION AND TRACKING.
Degree: 2018, Penn State University
URL: https://etda.libraries.psu.edu/catalog/15350wzx12
► In self-driving vehicles, vehicle-to-vehicle communication is a key to solvingtraffic jams, avoiding accidents, choosing a perfect routine to destination and manyother problems. In vehicle-to-vehicle scenarios,…
(more)
▼ In self-driving vehicles, vehicle-to-vehicle communication is a key to solvingtraffic jams, avoiding accidents, choosing a perfect routine to destination and manyother problems. In vehicle-to-vehicle scenarios, people usually use microwavecommunication which works well but is
subject to times of low SNR in clutteredradio frequency(RF) environment and cost is high, so optical communication can beused to improve SNR. While laser transmission has direction restriction. Trackingtechniques can be deployed to narrow the transmitting angle of the laser, enhancingthe intensity level at receiver.If the transmitter can be tracked under vehicularenvironments, the performance of free space optical(FSO) communication will beenhanced to a large extent. According to frames caught by camera on car, we tryusing
feature identifying and
extraction techniques to locate the positions of multiplemarkers on other cars from caught frame and using object tracking algorithm topredict the moving of markers to guide the direction of lasers transmitting.
Advisors/Committee Members: Tim Kane, Thesis Advisor.
Subjects/Keywords: self-driving; feature extraction; ORB feature; contour extraction; object tracking
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APA (6th Edition):
Xia, W. (2018). VEHICULAR FSO COMMUNICATION SYSTEM APPLYING REAL-TIME RECOGNITION AND TRACKING. (Thesis). Penn State University. Retrieved from https://etda.libraries.psu.edu/catalog/15350wzx12
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Xia, Wentao. “VEHICULAR FSO COMMUNICATION SYSTEM APPLYING REAL-TIME RECOGNITION AND TRACKING.” 2018. Thesis, Penn State University. Accessed February 26, 2020.
https://etda.libraries.psu.edu/catalog/15350wzx12.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Xia, Wentao. “VEHICULAR FSO COMMUNICATION SYSTEM APPLYING REAL-TIME RECOGNITION AND TRACKING.” 2018. Web. 26 Feb 2020.
Vancouver:
Xia W. VEHICULAR FSO COMMUNICATION SYSTEM APPLYING REAL-TIME RECOGNITION AND TRACKING. [Internet] [Thesis]. Penn State University; 2018. [cited 2020 Feb 26].
Available from: https://etda.libraries.psu.edu/catalog/15350wzx12.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Xia W. VEHICULAR FSO COMMUNICATION SYSTEM APPLYING REAL-TIME RECOGNITION AND TRACKING. [Thesis]. Penn State University; 2018. Available from: https://etda.libraries.psu.edu/catalog/15350wzx12
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Penn State University
11.
Ahn, Eun Yeong Yeong.
Evolutionary-based Feature Extraction for Gesture
Recognition Using a Motion Camera.
Degree: PhD, Information Sciences and Technology, 2012, Penn State University
URL: https://etda.libraries.psu.edu/catalog/14735
► Gesture recognition systems have garnered increasing interest for their potential to support more natural human-computer interactions. However, compared to other human-computer interaction technologies such as…
(more)
▼ Gesture recognition systems have garnered increasing
interest for their potential to support more natural human-computer
interactions. However, compared to other human-computer interaction
technologies such as speech recognition, gesture recognition has
not been actively applied to personal devices such as mobile phones
or laptops due to the spatial requirements when performing gestures
as well as sensitivity to background noise. My research first
devises a problem of recognizing speed sensitive finger gestures
using a novel camera called Dynamic Vision Sensor camera, which
detects the temporal luminance difference for each pixel at
microsecond-level granularity and outputs a stream of on-events
(brighter) and off-events (darker) to the hardware. As with other
machine learning problems, the performance of a gesture
classification task depends on how well the representative features
are extracted. Thus the feature extraction process must consider
device-specific data properties to maximize the feature recognition
abilities while minimizing computational cost. My research studies
two feature extraction methods, namely local and global feature
extractions, which are designed to maximize the performance of the
DVS camera-based gesture recognition system. First, the local
feature extraction method aims to extract a smaller number of
representative features from a long sequence of the raw gesture
events detected by the DVS camera using segmentation. This approach
is called the local feature extraction, since the features are
extracted by considering neighboring events only. Specifically, I
propose bottom-up segmentation methods, where the sequence of
events are first divided into segments having the same time
interval, called the time-based, or the same number of events,
called the event-based, and the segments are repeatedly augmented
based on the event distributions of the neighboring segments. The
experimental results show that the event-based initial segmentation
outperforms the time-based across different classifiers, and is
more robust to noise. I also found that Bayesian network classifier
is more accurate than hidden Markov model when features are well
extracted using the event-based segmentation. Second, the global
feature extraction method aims to construct higher level compound
features by transforming the locally extracted features.
Specifically, an evolutionary algorithm is employed to find a good
set of simple and compound features. This is a challenging task due
to the large search space and the risks of overfitting. I define
problem-specific representation, genetic operators, and evaluation
methods, and analyze how the specified mutation and crossover
operator controls the individual’s search space. The experimental
results show that the proposed EA can extract a good set of
compound features that can enhance the performance accuracy with a
smaller number of features. Finally, I show how my
evolutionary-based feature extraction approach can serve as a
knowledge discovery process in the context of gesture…
Subjects/Keywords: Global Feature Extraction; Local Feature Extraction; DVS
camera; Evolutionary Algorithm; Segmentation
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APA ·
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MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Ahn, E. Y. Y. (2012). Evolutionary-based Feature Extraction for Gesture
Recognition Using a Motion Camera. (Doctoral Dissertation). Penn State University. Retrieved from https://etda.libraries.psu.edu/catalog/14735
Chicago Manual of Style (16th Edition):
Ahn, Eun Yeong Yeong. “Evolutionary-based Feature Extraction for Gesture
Recognition Using a Motion Camera.” 2012. Doctoral Dissertation, Penn State University. Accessed February 26, 2020.
https://etda.libraries.psu.edu/catalog/14735.
MLA Handbook (7th Edition):
Ahn, Eun Yeong Yeong. “Evolutionary-based Feature Extraction for Gesture
Recognition Using a Motion Camera.” 2012. Web. 26 Feb 2020.
Vancouver:
Ahn EYY. Evolutionary-based Feature Extraction for Gesture
Recognition Using a Motion Camera. [Internet] [Doctoral dissertation]. Penn State University; 2012. [cited 2020 Feb 26].
Available from: https://etda.libraries.psu.edu/catalog/14735.
Council of Science Editors:
Ahn EYY. Evolutionary-based Feature Extraction for Gesture
Recognition Using a Motion Camera. [Doctoral Dissertation]. Penn State University; 2012. Available from: https://etda.libraries.psu.edu/catalog/14735
Texas A&M University
12.
Fields, Matthew James.
Facilitation of visual pattern recognition by extraction of relevant features from microscopic traffic data.
Degree: 2009, Texas A&M University
URL: http://hdl.handle.net/1969.1/ETD-TAMU-2036
► An experimental approach to traffic flow analysis is presented in which methodology from pattern recognition is applied to a specific dataset to examine its utility…
(more)
▼ An experimental approach to traffic flow analysis is presented in which methodology
from pattern recognition is applied to a specific dataset to examine its utility in
determining traffic patterns. The selected dataset for this work, taken from a 1985 study
by JHK and Associates (traffic research) for the Federal Highway Administration,
covers an hour long time period over a quarter mile section and includes nine different
identifying features for traffic at any given time. The initial step is to select the most
pertinent of these features as a target for
extraction and local storage during the
experiment. The tools created for this approach, a two-level hierarchical group of
operators, are used to extract features from the dataset to create a
feature space; this is
done to minimize the experimental set to a matrix of desirable attributes from the
vehicles on the roadway. The application is to identify if this data can be readily parsed
into four distinct traffic states; in this case, the state of a vehicle is defined by its velocity
and acceleration at a selected timestamp. A three-dimensional plot is used, with color as
the third dimension and seen from a top-down perspective, to initially identify vehicle
states in a section of roadway over a selected section of time. This is followed by
applying k-means clustering, in this case with k=4 to match the four distinct traffic states, to the
feature space to examine its viability in determining the states of vehicles in
a time section. The method?s accuracy is viewed through silhouette plots. Finally, a
group of experiments run through a decision-tree architecture is compared to the kmeans
clustering approach. Each decision-tree format uses sets of predefined values for
velocity and acceleration to parse the data into the four states; modifications are made to
acceleration and deceleration values to examine different results.
The three-dimensional plots provide a visual example of congested traffic for use
in performing visual comparisons of the clustering results. The silhouette plot results of
the k-means experiments show inaccuracy for certain clusters; on the other hand, the
decision-tree work shows promise for future work.
Advisors/Committee Members: Nelson, Paul (advisor), Gutierrez-Osuna, Ricardo (committee member), Hawkins, Gene (committee member).
Subjects/Keywords: Pattern Recognition; Feature Extraction; Microscopic Traffic
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Manager
APA (6th Edition):
Fields, M. J. (2009). Facilitation of visual pattern recognition by extraction of relevant features from microscopic traffic data. (Thesis). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/ETD-TAMU-2036
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Fields, Matthew James. “Facilitation of visual pattern recognition by extraction of relevant features from microscopic traffic data.” 2009. Thesis, Texas A&M University. Accessed February 26, 2020.
http://hdl.handle.net/1969.1/ETD-TAMU-2036.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Fields, Matthew James. “Facilitation of visual pattern recognition by extraction of relevant features from microscopic traffic data.” 2009. Web. 26 Feb 2020.
Vancouver:
Fields MJ. Facilitation of visual pattern recognition by extraction of relevant features from microscopic traffic data. [Internet] [Thesis]. Texas A&M University; 2009. [cited 2020 Feb 26].
Available from: http://hdl.handle.net/1969.1/ETD-TAMU-2036.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Fields MJ. Facilitation of visual pattern recognition by extraction of relevant features from microscopic traffic data. [Thesis]. Texas A&M University; 2009. Available from: http://hdl.handle.net/1969.1/ETD-TAMU-2036
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Louisiana State University
13.
Chen, Jing.
Vocal Fold Analysis From High Speed Videoendoscopic Data.
Degree: PhD, Electrical and Computer Engineering, 2014, Louisiana State University
URL: etd-05152014-135123
;
https://digitalcommons.lsu.edu/gradschool_dissertations/664
► High speed videoendoscopy (HSV) of the larynx far surpasses the limits of videostroboscopy in evaluating the vocal fold vibratory behavior by providing much higher frame…
(more)
▼ High speed videoendoscopy (HSV) of the larynx far surpasses the limits of videostroboscopy in evaluating the vocal fold vibratory behavior by providing much higher frame rate. HSV enables the visualization of vocal fold vibratory pattern within an actual glottic cycle. This very detailed infor-mation on vocal fold vibratory characteristics could provide valuable information for the assessment of vocal fold vibratory function in disordered voices and the treatments effects of the behavioral, medical and surgical treatment procedures. In this work, we aim at addressing the problem of classi-fying voice disorders with varying etiology by following four steps described shortly. Our method-ology starts with glottis segmentation. Given a HSV data, the contour of the glottal opening area in each frame should be acquired. These contours record the vibration track of the vocal fold. After this, we obtain a reliable glottal axis that is necessary for getting certain vibratory features. The third step is the feature extraction on HSV data. In the last step, we complete the classification based on the features obtained from step 3. In this study, we first propose a novel glottis segmentation method based on simplified dynam-ic programming, which proves to be efficient and accurate. In addition, we introduce a new ap-proach for calculating the glottal axis. By comparing the proposed glottal axis determination meth-ods (modified linear regression) against state-of-the-art techniques, we demonstrate that our tech-nique is more reliable. After that, the concentration shifts to feature extraction and classification schemes. Eighteen different features are extracted and their discrimination is evaluated based on principal component analysis. Support vector machine and neural network are implemented to achieve the classification among three different types of vocal folds(normal vocal fold, unilateral vocal fold polyp, and unilateral vocal fold paralysis). The result demonstrates that the classification rates of four different tasks are all above 80%.
Subjects/Keywords: vocal fold; segmentaion; glottal axis; feature extraction
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APA ·
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Export
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Manager
APA (6th Edition):
Chen, J. (2014). Vocal Fold Analysis From High Speed Videoendoscopic Data. (Doctoral Dissertation). Louisiana State University. Retrieved from etd-05152014-135123 ; https://digitalcommons.lsu.edu/gradschool_dissertations/664
Chicago Manual of Style (16th Edition):
Chen, Jing. “Vocal Fold Analysis From High Speed Videoendoscopic Data.” 2014. Doctoral Dissertation, Louisiana State University. Accessed February 26, 2020.
etd-05152014-135123 ; https://digitalcommons.lsu.edu/gradschool_dissertations/664.
MLA Handbook (7th Edition):
Chen, Jing. “Vocal Fold Analysis From High Speed Videoendoscopic Data.” 2014. Web. 26 Feb 2020.
Vancouver:
Chen J. Vocal Fold Analysis From High Speed Videoendoscopic Data. [Internet] [Doctoral dissertation]. Louisiana State University; 2014. [cited 2020 Feb 26].
Available from: etd-05152014-135123 ; https://digitalcommons.lsu.edu/gradschool_dissertations/664.
Council of Science Editors:
Chen J. Vocal Fold Analysis From High Speed Videoendoscopic Data. [Doctoral Dissertation]. Louisiana State University; 2014. Available from: etd-05152014-135123 ; https://digitalcommons.lsu.edu/gradschool_dissertations/664
Kansas State University
14.
Erande, Abhijit.
Automatic
detection of significant features and event timeline construction
from temporally tagged data.
Degree: MS, Department of Computing and
Information Sciences, 2009, Kansas State University
URL: http://hdl.handle.net/2097/1675
► The goal of my project is to summarize large volumes of data and help users to visualize how events have unfolded over time. I address…
(more)
▼ The goal of my project is to summarize large volumes of
data and help users to visualize how events have unfolded over
time. I address the problem of extracting overview terms from a
time-tagged corpus of data and discuss some previous work conducted
in this area. I use a statistical approach to automatically extract
key terms, form groupings of related terms, and display the
resultant groups on a timeline. I use a static corpus composed of
news stories, as opposed to an on-line setting where continual
additions to the corpus are being made. Terms are extracted using a
Named Entity Recognizer, and importance of a term is determined
using the [superscript]X[superscript]2 measure. My approach does
not address the problem of associating time and date stamps with
data, and is restricted to corpora that been explicitly tagged. The
quality of results obtained is gauged subjectively and objectively
by measuring the degree to which events known to exist in the
corpus were identified by the system.
Advisors/Committee Members: William H. Hsu.
Subjects/Keywords: Timeline
generation; Feature
extraction; Computer Science (0984)
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APA ·
Chicago ·
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Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Erande, A. (2009). Automatic
detection of significant features and event timeline construction
from temporally tagged data. (Masters Thesis). Kansas State University. Retrieved from http://hdl.handle.net/2097/1675
Chicago Manual of Style (16th Edition):
Erande, Abhijit. “Automatic
detection of significant features and event timeline construction
from temporally tagged data.” 2009. Masters Thesis, Kansas State University. Accessed February 26, 2020.
http://hdl.handle.net/2097/1675.
MLA Handbook (7th Edition):
Erande, Abhijit. “Automatic
detection of significant features and event timeline construction
from temporally tagged data.” 2009. Web. 26 Feb 2020.
Vancouver:
Erande A. Automatic
detection of significant features and event timeline construction
from temporally tagged data. [Internet] [Masters thesis]. Kansas State University; 2009. [cited 2020 Feb 26].
Available from: http://hdl.handle.net/2097/1675.
Council of Science Editors:
Erande A. Automatic
detection of significant features and event timeline construction
from temporally tagged data. [Masters Thesis]. Kansas State University; 2009. Available from: http://hdl.handle.net/2097/1675
Delft University of Technology
15.
Roumi, M.
Implementing Texture Feature Extraction Algorithms on FPGA:.
Degree: 2009, Delft University of Technology
URL: http://resolver.tudelft.nl/uuid:5f7ba046-3a5c-46bb-8368-896a88f5e4a7
► Feature extraction is a key function in various image processing applications. A feature is an image characteristic that can capture certain visual property of the…
(more)
▼ Feature extraction is a key function in various image processing applications. A
feature is an image characteristic that can capture certain visual property of the image. Texture is an important
feature of many image types, which is the pattern of information or arrangement of the structure found in a picture. Texture features are used in different applications such as image processing, remote sensing and content-based image retrieval. These features can be extracted in several ways. The most common way is using a Gray Level Co-occurrence Matrix (GLCM). GLCM contains the second-order statistical information of neighboring pixels of an image. Textural properties can be calculated from GLCM to understand the details about the image content. However, the calculation of GLCM is very computationally intensive. In this thesis, an FPGA accelerator for fast calculation of GLCM is designed and implemented.
We propose an FPGA-based architecture for parallel computation of symmetric co-occurrence matrices. Experimental results show that our approach improves 2x up to 4x the processing time for simultaneous computation of sixteen co-occurrence matrices.
Advisors/Committee Members: Gaydadjiev, G., Shahbahrami, A..
Subjects/Keywords: image processing; feature extraction; texture; FPGA
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APA ·
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Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Roumi, M. (2009). Implementing Texture Feature Extraction Algorithms on FPGA:. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:5f7ba046-3a5c-46bb-8368-896a88f5e4a7
Chicago Manual of Style (16th Edition):
Roumi, M. “Implementing Texture Feature Extraction Algorithms on FPGA:.” 2009. Masters Thesis, Delft University of Technology. Accessed February 26, 2020.
http://resolver.tudelft.nl/uuid:5f7ba046-3a5c-46bb-8368-896a88f5e4a7.
MLA Handbook (7th Edition):
Roumi, M. “Implementing Texture Feature Extraction Algorithms on FPGA:.” 2009. Web. 26 Feb 2020.
Vancouver:
Roumi M. Implementing Texture Feature Extraction Algorithms on FPGA:. [Internet] [Masters thesis]. Delft University of Technology; 2009. [cited 2020 Feb 26].
Available from: http://resolver.tudelft.nl/uuid:5f7ba046-3a5c-46bb-8368-896a88f5e4a7.
Council of Science Editors:
Roumi M. Implementing Texture Feature Extraction Algorithms on FPGA:. [Masters Thesis]. Delft University of Technology; 2009. Available from: http://resolver.tudelft.nl/uuid:5f7ba046-3a5c-46bb-8368-896a88f5e4a7
Mississippi State University
16.
Mitzev, Ivan Stefanov.
A PATTERN RECOGNITION APPROACH TO ELECTROMYOGRAPHY DATA.
Degree: MS, Electrical and Computer Engineering, 2010, Mississippi State University
URL: http://sun.library.msstate.edu/ETD-db/theses/available/etd-07012010-235314/
;
► EMG classification is widely used in electric control of mechanically developed prosthesis, robots development, clinical application etc. It has been evaluated for years, but…
(more)
▼ EMG classification is widely used in electric control of mechanically developed
prosthesis, robots development, clinical application etc. It has been evaluated for years,
but the main goal of this research is to develop an easy to implement and fast to execute
pattern recognition method for classifying signals used for human gait analysis. This
method is based on adding two new temporal features (form factor and standard
deviation) for EMG signal recognition and using them along with several popular
features (area under the curve, wavelength function-pathway and zero crossing rate) to
come up with a low complexity suitable
feature extraction. Results are presented for
EMG data and a comparison with existing methods is made to validate the applicability
of the foregoing method. It is shown that the best combination in terms of accuracy and
time performance is given by spectral and temporal
extraction features along with neural
network recognition (NN) algorithm.
Advisors/Committee Members: Nicolas H. Younan (chair), Jenny Q. Du (committee member), Pan Li (committee member).
Subjects/Keywords: feature extraction; Mahalanobis distance; electromyography; pattern recognition
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APA (6th Edition):
Mitzev, I. S. (2010). A PATTERN RECOGNITION APPROACH TO ELECTROMYOGRAPHY DATA. (Masters Thesis). Mississippi State University. Retrieved from http://sun.library.msstate.edu/ETD-db/theses/available/etd-07012010-235314/ ;
Chicago Manual of Style (16th Edition):
Mitzev, Ivan Stefanov. “A PATTERN RECOGNITION APPROACH TO ELECTROMYOGRAPHY DATA.” 2010. Masters Thesis, Mississippi State University. Accessed February 26, 2020.
http://sun.library.msstate.edu/ETD-db/theses/available/etd-07012010-235314/ ;.
MLA Handbook (7th Edition):
Mitzev, Ivan Stefanov. “A PATTERN RECOGNITION APPROACH TO ELECTROMYOGRAPHY DATA.” 2010. Web. 26 Feb 2020.
Vancouver:
Mitzev IS. A PATTERN RECOGNITION APPROACH TO ELECTROMYOGRAPHY DATA. [Internet] [Masters thesis]. Mississippi State University; 2010. [cited 2020 Feb 26].
Available from: http://sun.library.msstate.edu/ETD-db/theses/available/etd-07012010-235314/ ;.
Council of Science Editors:
Mitzev IS. A PATTERN RECOGNITION APPROACH TO ELECTROMYOGRAPHY DATA. [Masters Thesis]. Mississippi State University; 2010. Available from: http://sun.library.msstate.edu/ETD-db/theses/available/etd-07012010-235314/ ;
NSYSU
17.
Yang, Cheng-Ju.
Image classification via successive core tensor selection procedure.
Degree: Master, Applied Mathematics, 2018, NSYSU
URL: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0606118-151922
► In the field of artificial intelligence, high-order tensor data have been studied and analyzed, such as the automated optical inspection and MRI. Therefore, tensor decompositions…
(more)
▼ In the field of artificial intelligence, high-order tensor data have been studied and analyzed, such as the automated optical inspection and MRI. Therefore, tensor decompositions and classification algorithms have become an important research topic.
In a traditional neural network or machine learning method, the classification algorithm inputs training data in the form of vectors, and the trained model can identify and classify the testing data. In order to conform the input constraints, high-order tensor data are often expanded into high-dimensional vectors. However, it also leads to the loss of spatially related information adjacent to different orders, thus damages the performance of the classification.
This thesis proposes a classification model combining non-negative Tucker decomposition and high-order tensors principal component analysis, and extracts
feature core tensors successively to improve the accuracy of classification. Comparing with to neural network classifiers, we replace affine transformations with tensor transformations, which optimizes tensor projections to avoid missing information representing the spatial relationships in different orders, so that it extracts more complete features. For signal processing and medical image fields, data will lose its physical significance at negative values. So many non-negative decomposition and analysis methods have also become important research issues. The non-negative Tucker decomposition referred in this paper is one of them, and it is also one of the classic high-order extensions of non-negative matrix factorization. In the classification model, non-negative Tucker decomposition can not only maintain the non-negative physical meaning, but also can ignore the difference between same class, which makes the classification accuracy increase.
This study explores the computational time cost and classification accuracy of the model. In the experiment of image recognition, the training time of the high-order tensor principal component analysis was reduced to half after combining non-negative Tucker decomposition. In terms of accuracy, the smaller the number of training data, the more pronounced the lead of our model is.
Advisors/Committee Members: Yueh-Cheng Kuo (chair), Tzon-Tzer Lu (chair), Chieh-Sen Huang (chair), Tsung-Lin Lee (committee member).
Subjects/Keywords: data feature extraction; image classification; tensor decomposition
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APA (6th Edition):
Yang, C. (2018). Image classification via successive core tensor selection procedure. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0606118-151922
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Yang, Cheng-Ju. “Image classification via successive core tensor selection procedure.” 2018. Thesis, NSYSU. Accessed February 26, 2020.
http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0606118-151922.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Yang, Cheng-Ju. “Image classification via successive core tensor selection procedure.” 2018. Web. 26 Feb 2020.
Vancouver:
Yang C. Image classification via successive core tensor selection procedure. [Internet] [Thesis]. NSYSU; 2018. [cited 2020 Feb 26].
Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0606118-151922.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Yang C. Image classification via successive core tensor selection procedure. [Thesis]. NSYSU; 2018. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0606118-151922
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
University of Waterloo
18.
Li, Fan.
Automated Remote Sensing Image Interpretation with Limited Labeled Training Data.
Degree: 2015, University of Waterloo
URL: http://hdl.handle.net/10012/9571
► Automated remote sensing image interpretation has been investigated for more than a decade. In early years, most work was based on the assumption that there…
(more)
▼ Automated remote sensing image interpretation has been investigated for more than a decade. In early years, most work was based on the assumption that there are sufficient labeled samples to be used for training. However, ground-truth collection is a very tedious and time-consuming task and sometimes very expensive, especially in the field of remote sensing that usually relies on field surveys to collect ground truth. In recent years, as the development of advanced machine learning techniques, remote sensing image interpretation with limited ground-truth has caught the attention of researchers in the fields of both remote sensing and computer science.
Three approaches that focus on different aspects of the interpretation process, i.e., feature extraction, classification, and segmentation, are proposed to deal with the limited ground truth problem. First, feature extraction techniques, which usually serve as a pre-processing step for remote sensing image classification are explored. Instead of only focusing on feature extraction, a joint feature extraction and classification framework is proposed based on ensemble local manifold learning. Second, classifiers in the case of limited labeled training data are investigated, and an enhanced ensemble learning method that outperforms state-of-the-art classification methods is proposed. Third, image segmentation techniques are investigated, with the aid of unlabeled samples and spatial information. A semi-supervised self-training method is proposed, which is capable of expanding the number of training samples by its own and hence improving classification performance iteratively. Experiments show that the proposed approaches outperform state-of-the-art techniques in terms of classification accuracy on benchmark remote sensing datasets.
Subjects/Keywords: remote sensing; classification; segmentation; feature extraction.
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to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Li, F. (2015). Automated Remote Sensing Image Interpretation with Limited Labeled Training Data. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/9571
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Li, Fan. “Automated Remote Sensing Image Interpretation with Limited Labeled Training Data.” 2015. Thesis, University of Waterloo. Accessed February 26, 2020.
http://hdl.handle.net/10012/9571.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Li, Fan. “Automated Remote Sensing Image Interpretation with Limited Labeled Training Data.” 2015. Web. 26 Feb 2020.
Vancouver:
Li F. Automated Remote Sensing Image Interpretation with Limited Labeled Training Data. [Internet] [Thesis]. University of Waterloo; 2015. [cited 2020 Feb 26].
Available from: http://hdl.handle.net/10012/9571.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Li F. Automated Remote Sensing Image Interpretation with Limited Labeled Training Data. [Thesis]. University of Waterloo; 2015. Available from: http://hdl.handle.net/10012/9571
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
University of New South Wales
19.
Li, Zelin.
Feature learning, selection and representation for object detection and recognition.
Degree: Computer Science & Engineering, 2017, University of New South Wales
URL: http://handle.unsw.edu.au/1959.4/58813
;
https://unsworks.unsw.edu.au/fapi/datastream/unsworks:47626/SOURCE02?view=true
► The recent years have seen the increasing popularity of a wide range of applications in Computer Vision. Object Recognition is critical in determining the success…
(more)
▼ The recent years have seen the increasing popularity of a wide range of applications in Computer Vision. Object Recognition is critical in determining the success of these applications. In general, object recognition consists of three major components: imagery acquisition,
feature extraction and classification. Imagery acquisition is the process by which hardware is used to obtain information from a scene.
Feature extraction is the process by which an object within the image is described either explicitly or implicitly. Finally, classification attempts to use the extracted features as an input to generate classifiers, thereby distinguishing different objects. Therefore,
feature extraction and its derived representations play a vital role in improving the accuracy of object recognition.In traditional hand-crafted
feature representation methods, such as Histogram of Gradient, the representative and discriminative capabilities of these methods are highly vulnerable to various factors, such as image noise. Therefore, in this study, novel
feature descriptors are proposed to tackle the issues arising from
feature representation in object recognition. Two kernel-based
feature descriptors are developed: Steering Kernel Regression Weight Matrix and Long-Axis of Local Adaptive Steering Kernel; both of which are derived from Steering Kernel Regression. The proposed
feature descriptors facilitate the ability of Steering Kernel Regression to capture the local structure around an object and tolerate the noise that occurs in the images. Subsequently covariance techniques are employed to form a robust
feature representation for an object. To alleviate the complexity arising from covariance, Long-Axis of Local Adaptive Steering Kernel
feature is proposed to simplify the computation required.Additionally, to further reduce computation complexity, a sparse representation based
feature descriptor is developed for object detection. Sparse representation has proven its superior capabilities in image classification, but not in object detection, i.e. isolating objects within a complicated environment. Therefore, in this study, the reconstruction error from sparse representation is utilised as a
feature, and a Bayesian model is applied to use learned atoms to reinforce the discriminative power of reconstruction error that may have been 'polluted' by noise.Computer Vision applications not only work with static images, but also with videos. A motion-based approach is developed to tackle the problem caused by appearance-based features in video-based Person Re-identification. Fine-grained displacement along dense trajectories is applied to describe a specific human motion. Such motion information encodes numerous distinctive biometric cues to discriminate between the walking styles of different people. Utilising Dissimilarity based Sparse Subset Learning, the proposed approach demonstrates increased robustness in long term Person Re-identification applications.
Advisors/Committee Members: Chen, Fang, Computer Science & Engineering, Faculty of Engineering, UNSW.
Subjects/Keywords: Feature Extraction; Computer Vision; Pattern Recognition
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APA ·
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CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Li, Z. (2017). Feature learning, selection and representation for object detection and recognition. (Doctoral Dissertation). University of New South Wales. Retrieved from http://handle.unsw.edu.au/1959.4/58813 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:47626/SOURCE02?view=true
Chicago Manual of Style (16th Edition):
Li, Zelin. “Feature learning, selection and representation for object detection and recognition.” 2017. Doctoral Dissertation, University of New South Wales. Accessed February 26, 2020.
http://handle.unsw.edu.au/1959.4/58813 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:47626/SOURCE02?view=true.
MLA Handbook (7th Edition):
Li, Zelin. “Feature learning, selection and representation for object detection and recognition.” 2017. Web. 26 Feb 2020.
Vancouver:
Li Z. Feature learning, selection and representation for object detection and recognition. [Internet] [Doctoral dissertation]. University of New South Wales; 2017. [cited 2020 Feb 26].
Available from: http://handle.unsw.edu.au/1959.4/58813 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:47626/SOURCE02?view=true.
Council of Science Editors:
Li Z. Feature learning, selection and representation for object detection and recognition. [Doctoral Dissertation]. University of New South Wales; 2017. Available from: http://handle.unsw.edu.au/1959.4/58813 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:47626/SOURCE02?view=true
20.
Tate, Anne Rosemary.
Pattern recognition analysis of in vivo magnetic resonance spectra.
Degree: PhD, 1996, University of Sussex
URL: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.307732
Subjects/Keywords: 621.3994; Feature extraction
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APA (6th Edition):
Tate, A. R. (1996). Pattern recognition analysis of in vivo magnetic resonance spectra. (Doctoral Dissertation). University of Sussex. Retrieved from https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.307732
Chicago Manual of Style (16th Edition):
Tate, Anne Rosemary. “Pattern recognition analysis of in vivo magnetic resonance spectra.” 1996. Doctoral Dissertation, University of Sussex. Accessed February 26, 2020.
https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.307732.
MLA Handbook (7th Edition):
Tate, Anne Rosemary. “Pattern recognition analysis of in vivo magnetic resonance spectra.” 1996. Web. 26 Feb 2020.
Vancouver:
Tate AR. Pattern recognition analysis of in vivo magnetic resonance spectra. [Internet] [Doctoral dissertation]. University of Sussex; 1996. [cited 2020 Feb 26].
Available from: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.307732.
Council of Science Editors:
Tate AR. Pattern recognition analysis of in vivo magnetic resonance spectra. [Doctoral Dissertation]. University of Sussex; 1996. Available from: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.307732
University of North Texas
21.
Florescu, Corina Andreea.
SurfKE: A Graph-Based Feature Learning Framework for Keyphrase Extraction.
Degree: 2019, University of North Texas
URL: https://digital.library.unt.edu/ark:/67531/metadc1538730/
► Current unsupervised approaches for keyphrase extraction compute a single importance score for each candidate word by considering the number and quality of its associated words…
(more)
▼ Current unsupervised approaches for keyphrase
extraction compute a single importance score for each candidate word by considering the number and quality of its associated words in the graph and they are not flexible enough to incorporate multiple types of information. For instance, nodes in a network may exhibit diverse connectivity patterns which are not captured by the graph-based ranking methods. To address this, we present a new approach to keyphrase
extraction that represents the document as a word graph and exploits its structure in order to reveal underlying explanatory factors hidden in the data that may distinguish keyphrases from non-keyphrases. Experimental results show that our model, which uses phrase graph representations in a supervised probabilistic framework, obtains remarkable improvements in performance over previous supervised and unsupervised keyphrase
extraction systems.
Advisors/Committee Members: Jin, Wei, Nielsen, Rodney, Huang, Yan, Fu, Song, Blanco, Eduardo.
Subjects/Keywords: keyphrase extraction; graph representation learning; feature learning
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University of Illinois – Urbana-Champaign
22.
Jain, Shubham.
Landmarks for clothing retrieval.
Degree: MS, Computer Science, 2019, University of Illinois – Urbana-Champaign
URL: http://hdl.handle.net/2142/104891
► Clothing Retrieval is a task that is increasingly becoming popular with the rise of online shopping and social media’s popularity. We propose to solve the…
(more)
▼ Clothing Retrieval is a task that is increasingly becoming popular with the rise of online shopping and social media’s popularity. We propose to solve the clothing retrieval problem using landmarks based on the clothing type and features surrounding the landmarks to get a more ingrained view of the design. We compare this method with other models most of which use the whole image as inputs and show the superiority of the model which gives importance to the crucial parts of the images. For the blouses sub-set from of the Deep Fashion dataset[1], we get an 16% increase in the accuracy for the top 3, 14% in top 5 and 11% top 10 retrieval results using the keypoints
extraction methods combined with whole images compared to whole images as inputs. We also observe that the clothes retrieved are more similar in terms or design as well as high level properties like sleeve sizes, folded v/s non-folded sleeves etc.
Advisors/Committee Members: Lazebnik, Svetlana (advisor).
Subjects/Keywords: clothing; retrieval; landmarks; keypoints; feature extraction
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APA ·
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MLA ·
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CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Jain, S. (2019). Landmarks for clothing retrieval. (Thesis). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/104891
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Jain, Shubham. “Landmarks for clothing retrieval.” 2019. Thesis, University of Illinois – Urbana-Champaign. Accessed February 26, 2020.
http://hdl.handle.net/2142/104891.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Jain, Shubham. “Landmarks for clothing retrieval.” 2019. Web. 26 Feb 2020.
Vancouver:
Jain S. Landmarks for clothing retrieval. [Internet] [Thesis]. University of Illinois – Urbana-Champaign; 2019. [cited 2020 Feb 26].
Available from: http://hdl.handle.net/2142/104891.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Jain S. Landmarks for clothing retrieval. [Thesis]. University of Illinois – Urbana-Champaign; 2019. Available from: http://hdl.handle.net/2142/104891
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Linköping University
23.
Wasell, Richard.
Automatisk detektering av diken i LiDAR-data.
Degree: Computer Vision, 2011, Linköping University
URL: http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-72066
► Den här rapporten har utrett möjligheten att automatiskt identifiera diken frånflygburet insamlat LiDAR-data. Den metod för identifiering som har valts harförst skapat en höjdbild…
(more)
▼ Den här rapporten har utrett möjligheten att automatiskt identifiera diken frånflygburet insamlat LiDAR-data. Den metod för identifiering som har valts harförst skapat en höjdbild från LiDAR-data. Därefter har den tagit fram kandidatertill diken genom att vektorisera resultatet från en linjedetektering. Egenskaper-na för dikeskandidaterna har sedan beräknats genom en analys av höjdprofilerför varje enskild kandidat, där höjdprofilerna skapats utifrån ursprungliga data.Genom att filtrera kandidaterna efter deras egenskaper kan dikeskartor med an-vändarspecificerade mått på diken presenteras i ett vektorformat som underlättarvidare användning. Rapporten beskriver hur algoritmen har implementerats ochpresenterar också exempel på resultat. Efter en analys av algoritmen samt förslagpå förbättringar presenteras den viktigaste behållningen av rapporten; Att det ärmöjligt med automatisk detektering av diken.
This Master’s thesis is investigating the possibility of automatically identifyingditches in airborne collected LiDAR data. The chosen approach to identificationcommences by creating an elevation picture from the LiDAR data. Then it usesthe result of a line detection to exhibit candidates for ditches. The properties forthe various candidates are calculated through an analysis of the elevation profile forthe candidates, where the elevation profiles are created from the original data. Byfiltering the candidates according to their calculated properties, maps with ditchesconforming to user-specified limits are created and presented in vector format.This thesis describes how the algorithm is implemented and gives examples ofresults. After an analysis of the algorithm and a proposal for improvements, itis suggested that automatic detection of ditches in LiDAR collected data is anachievable objective.
Subjects/Keywords: linear feature extraction; line detection; LiDAR; ditches
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APA (6th Edition):
Wasell, R. (2011). Automatisk detektering av diken i LiDAR-data. (Thesis). Linköping University. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-72066
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Wasell, Richard. “Automatisk detektering av diken i LiDAR-data.” 2011. Thesis, Linköping University. Accessed February 26, 2020.
http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-72066.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Wasell, Richard. “Automatisk detektering av diken i LiDAR-data.” 2011. Web. 26 Feb 2020.
Vancouver:
Wasell R. Automatisk detektering av diken i LiDAR-data. [Internet] [Thesis]. Linköping University; 2011. [cited 2020 Feb 26].
Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-72066.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Wasell R. Automatisk detektering av diken i LiDAR-data. [Thesis]. Linköping University; 2011. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-72066
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
24.
Pham, Thanh Van.
Detection of plant characteristics and a comparison of effectiveness between 2d and 3d data visualization in supporting human perception of plant characteristics
.
Degree: 2018, Texas A&M University – Corpus Christi
URL: https://tamucc-ir.tdl.org/handle/1969.6/87018
► Efficient agriculture requires the assessment of plant characteristics. A higher crop yield can be achieved with good quality plant characteristic data. In this research, a…
(more)
▼ Efficient agriculture requires the assessment of plant characteristics. A higher crop yield can be achieved with good quality plant characteristic data. In this research, a system was developed using the algorithms presented here to automatically extract plant characteristics. The automatically extracted values were compared with ground-truth data to evaluate the accuracy of the system. As well, the effectiveness of using 2 or 3-dimensional data visualization for determining these characteristics is studied. An experiment was conducted to investigate how effective plant characteristics are evaluated when using 2D or 3D data visualizations. Participants were presented with either plant pictures (2D) or 3D plant models and tasked with identifying plant height and the number of leaves. Task completion times and accuracy rates were gathered for performance analysis.
Advisors/Committee Members: King, Scott A (advisor), Lee, Byung Cheol (committeeMember), Sheta, Alaa (committeeMember).
Subjects/Keywords: Agriculture;
Characteristic;
Data Visualization;
Feature extraction;
Plant
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APA ·
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APA (6th Edition):
Pham, T. V. (2018). Detection of plant characteristics and a comparison of effectiveness between 2d and 3d data visualization in supporting human perception of plant characteristics
. (Thesis). Texas A&M University – Corpus Christi. Retrieved from https://tamucc-ir.tdl.org/handle/1969.6/87018
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Pham, Thanh Van. “Detection of plant characteristics and a comparison of effectiveness between 2d and 3d data visualization in supporting human perception of plant characteristics
.” 2018. Thesis, Texas A&M University – Corpus Christi. Accessed February 26, 2020.
https://tamucc-ir.tdl.org/handle/1969.6/87018.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Pham, Thanh Van. “Detection of plant characteristics and a comparison of effectiveness between 2d and 3d data visualization in supporting human perception of plant characteristics
.” 2018. Web. 26 Feb 2020.
Vancouver:
Pham TV. Detection of plant characteristics and a comparison of effectiveness between 2d and 3d data visualization in supporting human perception of plant characteristics
. [Internet] [Thesis]. Texas A&M University – Corpus Christi; 2018. [cited 2020 Feb 26].
Available from: https://tamucc-ir.tdl.org/handle/1969.6/87018.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Pham TV. Detection of plant characteristics and a comparison of effectiveness between 2d and 3d data visualization in supporting human perception of plant characteristics
. [Thesis]. Texas A&M University – Corpus Christi; 2018. Available from: https://tamucc-ir.tdl.org/handle/1969.6/87018
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Queensland University of Technology
25.
Dong, Xueyan.
Content-based birdcall retrieval from environmental audio.
Degree: 2016, Queensland University of Technology
URL: http://eprints.qut.edu.au/94402/
► This research investigates techniques to analyse long duration acoustic recordings to help ecologists monitor birdcall activities. It designs a generalized algorithm to identify a broad…
(more)
▼ This research investigates techniques to analyse long duration acoustic recordings to help ecologists monitor birdcall activities. It designs a generalized algorithm to identify a broad range of bird species. It allows ecologists to search for arbitrary birdcalls of interest, rather than restricting them to just a very limited number of species on which the recogniser is trained. The algorithm can help ecologists find sounds of interest more efficiently by filtering out large volumes of unwanted sounds and only focusing on birdcalls.
Subjects/Keywords: birdcall retrieval; feature extraction; spectral ridge feature; event representation; environmental recording
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APA (6th Edition):
Dong, X. (2016). Content-based birdcall retrieval from environmental audio. (Thesis). Queensland University of Technology. Retrieved from http://eprints.qut.edu.au/94402/
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Dong, Xueyan. “Content-based birdcall retrieval from environmental audio.” 2016. Thesis, Queensland University of Technology. Accessed February 26, 2020.
http://eprints.qut.edu.au/94402/.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Dong, Xueyan. “Content-based birdcall retrieval from environmental audio.” 2016. Web. 26 Feb 2020.
Vancouver:
Dong X. Content-based birdcall retrieval from environmental audio. [Internet] [Thesis]. Queensland University of Technology; 2016. [cited 2020 Feb 26].
Available from: http://eprints.qut.edu.au/94402/.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Dong X. Content-based birdcall retrieval from environmental audio. [Thesis]. Queensland University of Technology; 2016. Available from: http://eprints.qut.edu.au/94402/
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Ohio University
26.
Venable, Donald T.
Implementation of a 3D Imaging Sensor Aided Inertial
Measurement Unit Navigation System.
Degree: MS, Electrical Engineering (Engineering and
Technology), 2008, Ohio University
URL: http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1218764810
► This thesis describes an implementation of a 3D imaging sensor aided Inertial Measurement Unit navigation system. The thesis describes previous efforts in the area…
(more)
▼ This thesis describes an implementation of a
3D imaging sensor aided Inertial Measurement Unit navigation
system. The thesis describes previous efforts in the area of
alternative navigation, and describes current 3D imaging navigation
techniques. The thesis then describes a method of extracting planar
surfaces from acquired 3D data. Also described
is methodology describing the association of planar surfaces
between 3D camera frames. The method of calculating a position
solution from extracted and associated planar surfaces is
described, along with the implementation of a complimentary Kalman
filter to provide a tightly integrated navigation solution between
the 3D imaging sensor and the Inertial Measurement Unit. Two tests
were performed with the proposed methodology, and an accurate
navigation solution was demonstrated to be
obtainable.
Advisors/Committee Members: Uijt de Haag, Maarten (Advisor).
Subjects/Keywords: Electrical Engineering; Flash Lidar; histogramming; navigation; feature extraction; feature association
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APA (6th Edition):
Venable, D. T. (2008). Implementation of a 3D Imaging Sensor Aided Inertial
Measurement Unit Navigation System. (Masters Thesis). Ohio University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1218764810
Chicago Manual of Style (16th Edition):
Venable, Donald T. “Implementation of a 3D Imaging Sensor Aided Inertial
Measurement Unit Navigation System.” 2008. Masters Thesis, Ohio University. Accessed February 26, 2020.
http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1218764810.
MLA Handbook (7th Edition):
Venable, Donald T. “Implementation of a 3D Imaging Sensor Aided Inertial
Measurement Unit Navigation System.” 2008. Web. 26 Feb 2020.
Vancouver:
Venable DT. Implementation of a 3D Imaging Sensor Aided Inertial
Measurement Unit Navigation System. [Internet] [Masters thesis]. Ohio University; 2008. [cited 2020 Feb 26].
Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1218764810.
Council of Science Editors:
Venable DT. Implementation of a 3D Imaging Sensor Aided Inertial
Measurement Unit Navigation System. [Masters Thesis]. Ohio University; 2008. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1218764810
University of Tennessee – Knoxville
27.
Luo, Jiajia.
Feature Extraction and Recognition for Human Action Recognition.
Degree: 2014, University of Tennessee – Knoxville
URL: https://trace.tennessee.edu/utk_graddiss/2710
► How to automatically label videos containing human motions is the task of human action recognition. Traditional human action recognition algorithms use the RGB videos as…
(more)
▼ How to automatically label videos containing human motions is the task of human action recognition. Traditional human action recognition algorithms use the RGB videos as input, and it is a challenging task because of the large intra-class variations of actions, cluttered background, possible camera movement, and illumination variations. Recently, the introduction of cost-effective depth cameras provides a new possibility to address difficult issues. However, it also brings new challenges such as noisy depth maps and time alignment. In this dissertation, effective and computationally efficient feature extraction and recognition algorithms are proposed for human action recognition.
At the feature extraction step, two novel spatial-temporal feature descriptors are proposed which can be combined with local feature detectors. The first proposed descriptor is the Shape and Motion Local Ternary Pattern (SMltp) descriptor which can dramatically reduced the number of features generated by dense sampling without sacrificing the accuracy. In addition, the Center-Symmetric Motion Local Ternary Pattern (CS-Mltp) descriptor is proposed, which describes the spatial and temporal gradients-like features. Both descriptors (SMltp and CS-Mltp) take advantage of the Local Binary Pattern (LBP) texture operator in terms of tolerance to illumination change, robustness in homogeneous region and computational efficiency.
For better feature representation, this dissertation presents a new Dictionary Learning (DL) method to learn an overcomplete set of representative vectors (atoms) so that any input feature can be approximated by a linear combination of these atoms with minimum reconstruction error. Instead of simultaneously learning one overcomplete dictionary for all classes, we learn class-specific sub-dictionaries to increase the discrimination. In addition, the group sparsity and the geometry constraint are added to the learning process to further increase the discriminative power, so that features are well reconstructed by atoms from the same class and features from the same class with high similarity will be forced to have similar coefficients.
To evaluate the proposed algorithms, three applications including single view action recognition, distributed multi-view action recognition, and RGB-D action recognition have been explored. Experimental results on benchmark datasets and comparative analyses with the state-of-the-art methods show the effectiveness and merits of the proposed algorithms.
Subjects/Keywords: feature extraction; feature representation; dictionary learning; sparse coding; Other Computer Engineering
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APA (6th Edition):
Luo, J. (2014). Feature Extraction and Recognition for Human Action Recognition. (Doctoral Dissertation). University of Tennessee – Knoxville. Retrieved from https://trace.tennessee.edu/utk_graddiss/2710
Chicago Manual of Style (16th Edition):
Luo, Jiajia. “Feature Extraction and Recognition for Human Action Recognition.” 2014. Doctoral Dissertation, University of Tennessee – Knoxville. Accessed February 26, 2020.
https://trace.tennessee.edu/utk_graddiss/2710.
MLA Handbook (7th Edition):
Luo, Jiajia. “Feature Extraction and Recognition for Human Action Recognition.” 2014. Web. 26 Feb 2020.
Vancouver:
Luo J. Feature Extraction and Recognition for Human Action Recognition. [Internet] [Doctoral dissertation]. University of Tennessee – Knoxville; 2014. [cited 2020 Feb 26].
Available from: https://trace.tennessee.edu/utk_graddiss/2710.
Council of Science Editors:
Luo J. Feature Extraction and Recognition for Human Action Recognition. [Doctoral Dissertation]. University of Tennessee – Knoxville; 2014. Available from: https://trace.tennessee.edu/utk_graddiss/2710
Virginia Tech
28.
Shen, Yuan.
Feature Extraction and Feasibility Study on CT Image Guided Colonoscopy.
Degree: MS, Electrical and Computer Engineering, 2010, Virginia Tech
URL: http://hdl.handle.net/10919/32275
► Computed tomographic colonography(CTC), also called virtual colonoscopy, uses CT scanning and computer post-processing to create two dimensional images and three dimensional virtual views inside of…
(more)
▼ Computed tomographic colonography(CTC), also called virtual colonoscopy, uses CT scanning and computer post-processing to create two dimensional images and three dimensional virtual views inside of the colon. Computer-aided polyp detection(CAPD) automatically detects colonic polyps and presents them to the user in either a first or second reader paradigm, with a goal reducing examination time while increasing the detection sensitivity. During colonoscopy, the endoscopists use the colonoscope inside of a patient's colon to target potential polyps and validate CAPD found ones. However, there is no direct information linking between CT images and the real-time optical colonoscopy(OC) video provided during the operation, thus endoscopists need to rely largely on their past experience to locate and remove polyps. The goal of this research project is to study the feasibility of developing an image guided colonoscopy(IGC) system that combines CTC images, real-time colonoscope position measurements, and video stream to validate and guide the removal of polyps found in CAPD. System would ease polyp level validation of CTC and improve the accuracy and efficiency of guiding the endoscopist to the target polyps. In this research project, a centerline based matching algorithm has been designed to estimate, in real time, the relative location of the colonoscope in the virtual colonoscopy environment. Furthermore, the feasibility of applying online simultaneous localization and mapping(SLAM) into CT image guided colonoscopy has been evaluated to further improve the performance of localizing and removing the pre-defined target polyps. A colon phantom is used to provide a testing setup to assess the performance of the proposed algorithms.
Advisors/Committee Members: Wyatt, Christopher L. (committeechair), Baumann, William T. (committee member), Stilwell, Daniel J. (committee member).
Subjects/Keywords: Feature Extraction; Image Guided Colonoscopy; SLAM; Video Feature Tracking
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APA (6th Edition):
Shen, Y. (2010). Feature Extraction and Feasibility Study on CT Image Guided Colonoscopy. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/32275
Chicago Manual of Style (16th Edition):
Shen, Yuan. “Feature Extraction and Feasibility Study on CT Image Guided Colonoscopy.” 2010. Masters Thesis, Virginia Tech. Accessed February 26, 2020.
http://hdl.handle.net/10919/32275.
MLA Handbook (7th Edition):
Shen, Yuan. “Feature Extraction and Feasibility Study on CT Image Guided Colonoscopy.” 2010. Web. 26 Feb 2020.
Vancouver:
Shen Y. Feature Extraction and Feasibility Study on CT Image Guided Colonoscopy. [Internet] [Masters thesis]. Virginia Tech; 2010. [cited 2020 Feb 26].
Available from: http://hdl.handle.net/10919/32275.
Council of Science Editors:
Shen Y. Feature Extraction and Feasibility Study on CT Image Guided Colonoscopy. [Masters Thesis]. Virginia Tech; 2010. Available from: http://hdl.handle.net/10919/32275
Arizona State University
29.
Yamak, Didem.
Characterization of Coronary Atherosclerotic Plaques by Dual
Energy Computed Tomography.
Degree: PhD, Bioengineering, 2013, Arizona State University
URL: http://repository.asu.edu/items/18027
► Coronary heart disease (CHD) is the most prevalent cause of death worldwide. Atherosclerosis which is the condition of plaque buildup on the inside of the…
(more)
▼ Coronary heart disease (CHD) is the most prevalent
cause of death worldwide. Atherosclerosis which is the condition of
plaque buildup on the inside of the coronary artery wall is the
main cause of CHD. Rupture of unstable atherosclerotic coronary
plaque is known to be the cause of acute coronary syndrome. The
composition of plaque is important for detection of plaque
vulnerability. Due to prognostic importance of early stage
identification, non-invasive assessment of plaque characterization
is necessary. Computed tomography (CT) has emerged as a
non-invasive alternative to coronary angiography. Recently, dual
energy CT (DECT) coronary angiography has been performed
clinically. DECT scanners use two different X-ray energies in order
to determine the energy dependency of tissue attenuation values for
each voxel. They generate virtual monochromatic energy images, as
well as material basis pair images. The characterization of plaque
components by DECT is still an active research topic since overlap
between the CT attenuations measured in plaque components and
contrast material shows that the single mean density might not be
an appropriate measure for characterization. This dissertation
proposes feature extraction, feature selection and learning
strategies for supervised characterization of coronary
atherosclerotic plaques. In my first study, I proposed an approach
for calcium quantification in contrast-enhanced examinations of the
coronary arteries, potentially eliminating the need for an extra
non-contrast X-ray acquisition. The ambiguity of separation of
calcium from contrast material was solved by using virtual
non-contrast images. Additional attenuation data provided by DECT
provides valuable information for separation of lipid from fibrous
plaque since the change of their attenuation as the energy level
changes is different. My second study proposed these as the input
to supervised learners for a more precise classification of lipid
and fibrous plaques. My last study aimed at automatic segmentation
of coronary arteries characterizing plaque components and lumen on
contrast enhanced monochromatic X-ray images. This required
extraction of features from regions of interests. This study
proposed feature extraction strategies and selection of important
ones. The results show that supervised learning on the proposed
features provides promising results for automatic characterization
of coronary atherosclerotic plaques by DECT.
Subjects/Keywords: Biomedical engineering; Atherosclerosis; Dual Energy Computed Tomography; feature extraction; feature selection
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APA ·
Chicago ·
MLA ·
Vancouver ·
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Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Yamak, D. (2013). Characterization of Coronary Atherosclerotic Plaques by Dual
Energy Computed Tomography. (Doctoral Dissertation). Arizona State University. Retrieved from http://repository.asu.edu/items/18027
Chicago Manual of Style (16th Edition):
Yamak, Didem. “Characterization of Coronary Atherosclerotic Plaques by Dual
Energy Computed Tomography.” 2013. Doctoral Dissertation, Arizona State University. Accessed February 26, 2020.
http://repository.asu.edu/items/18027.
MLA Handbook (7th Edition):
Yamak, Didem. “Characterization of Coronary Atherosclerotic Plaques by Dual
Energy Computed Tomography.” 2013. Web. 26 Feb 2020.
Vancouver:
Yamak D. Characterization of Coronary Atherosclerotic Plaques by Dual
Energy Computed Tomography. [Internet] [Doctoral dissertation]. Arizona State University; 2013. [cited 2020 Feb 26].
Available from: http://repository.asu.edu/items/18027.
Council of Science Editors:
Yamak D. Characterization of Coronary Atherosclerotic Plaques by Dual
Energy Computed Tomography. [Doctoral Dissertation]. Arizona State University; 2013. Available from: http://repository.asu.edu/items/18027
NSYSU
30.
Fu, Wei-hao.
Enhancement of Flower Classification with the Profile Feature.
Degree: Master, Computer Science and Engineering, 2018, NSYSU
URL: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0326118-050537
► In this thesis, we propose an elegant method, based on machine learning, for the flower classification. There are three steps in our method. The process…
(more)
▼ In this thesis, we propose an elegant method, based on machine learning, for the flower classification. There are three steps in our method. The process begins with segmenting the flower images and removing their backgrounds. We utilize the GrabCut approach to the do segmentation, because it provides a good performance; then, we extract the features from the foreground, including color features and texture features; finally, we train the SVM (support vector machine) models and Adaboost models with several
feature combinations. The experimental material comes from the Oxford-102 category flower dataset. Our proposed
feature, the profile
feature, improves about 2% accuracy in the SVM model and about 3% in our ensemble model. However, if we combine the inception-v3 model into our model, the profile
feature only improves about 1% accuracy. Our best result is 83.57% in accuracy which is obtained by aggregating several classification models and it outperforms all methods without deep-learning.
Advisors/Committee Members: Chang-biau Yang (committee member), Hsing-yen An (chair), Yung-hsing Peng (chair), Chiou-yi Hor (chair).
Subjects/Keywords: inception-v3 model; Adaboost; flower classification; SVM; texture feature; color feature; segmentation; feature extraction
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APA ·
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Manager
APA (6th Edition):
Fu, W. (2018). Enhancement of Flower Classification with the Profile Feature. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0326118-050537
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Fu, Wei-hao. “Enhancement of Flower Classification with the Profile Feature.” 2018. Thesis, NSYSU. Accessed February 26, 2020.
http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0326118-050537.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Fu, Wei-hao. “Enhancement of Flower Classification with the Profile Feature.” 2018. Web. 26 Feb 2020.
Vancouver:
Fu W. Enhancement of Flower Classification with the Profile Feature. [Internet] [Thesis]. NSYSU; 2018. [cited 2020 Feb 26].
Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0326118-050537.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Fu W. Enhancement of Flower Classification with the Profile Feature. [Thesis]. NSYSU; 2018. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0326118-050537
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
◁ [1] [2] [3] [4] [5] … [20] ▶
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Open Access Theses and Dissertations
