subject:(feature extraction)

University of Waterloo

1. Amelard, Robert. High-Level Intuitive Features (HLIFs) for Melanoma Detection.

Degree: 2013, University of Waterloo

► 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 · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th 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 (16^th Edition):

Amelard, Robert. “High-Level Intuitive Features (HLIFs) for Melanoma Detection.” 2013. Thesis, University of Waterloo. Accessed February 27, 2021. 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 (7^th Edition):

Amelard, Robert. “High-Level Intuitive Features (HLIFs) for Melanoma Detection.” 2013. Web. 27 Feb 2021.

Vancouver:

Amelard R. High-Level Intuitive Features (HLIFs) for Melanoma Detection. [Internet] [Thesis]. University of Waterloo; 2013. [cited 2021 Feb 27]. 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

2. 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 · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th 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 (16^th Edition):

López Iñesta, Emilia. “Aprendizaje de similitudes entre pares de objetos mediante clasificación supervisada .” 2017. Doctoral Dissertation, Universitat de Valencia. Accessed February 27, 2021. http://hdl.handle.net/10550/61001.

MLA Handbook (7^th Edition):

López Iñesta, Emilia. “Aprendizaje de similitudes entre pares de objetos mediante clasificación supervisada .” 2017. Web. 27 Feb 2021.

Vancouver:

López Iñesta E. Aprendizaje de similitudes entre pares de objetos mediante clasificación supervisada . [Internet] [Doctoral dissertation]. Universitat de Valencia; 2017. [cited 2021 Feb 27]. 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

University of KwaZulu-Natal

3. 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 · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th 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 (16^th Edition):

Adebanjo, Hannah Morenike. “Investigation of feature extraction algorithms and techniques for hyperspectral images.” 2017. Thesis, University of KwaZulu-Natal. Accessed February 27, 2021. 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 (7^th Edition):

Adebanjo, Hannah Morenike. “Investigation of feature extraction algorithms and techniques for hyperspectral images.” 2017. Web. 27 Feb 2021.

Vancouver:

Adebanjo HM. Investigation of feature extraction algorithms and techniques for hyperspectral images. [Internet] [Thesis]. University of KwaZulu-Natal; 2017. [cited 2021 Feb 27]. 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

University of Adelaide

4. 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 · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th 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 (16^th Edition):

Baseri Huddin, Aqilah. “An investigation of automatic feature extraction for clustered microcalcifications on digital mammograms.” 2015. Thesis, University of Adelaide. Accessed February 27, 2021. 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 (7^th Edition):

Baseri Huddin, Aqilah. “An investigation of automatic feature extraction for clustered microcalcifications on digital mammograms.” 2015. Web. 27 Feb 2021.

Vancouver:

Baseri Huddin A. An investigation of automatic feature extraction for clustered microcalcifications on digital mammograms. [Internet] [Thesis]. University of Adelaide; 2015. [cited 2021 Feb 27]. 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

Halmstad University

5. 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)

Subjects/Keywords: Object Detection; Feature Extraction; FPGA

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th 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 (16^th 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 27, 2021. 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 (7^th 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. 27 Feb 2021.

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 2021 Feb 27]. 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

Georgia Tech

6. 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)

Subjects/Keywords: Wavelets; Classification; Feature extraction

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APA (6^th 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 (16^th Edition):

Kong, Tae Woon. “Assessing self-similarity in redundant complex and quaternion wavelet domains: Theory and applications.” 2019. Doctoral Dissertation, Georgia Tech. Accessed February 27, 2021. http://hdl.handle.net/1853/61244.

MLA Handbook (7^th Edition):

Kong, Tae Woon. “Assessing self-similarity in redundant complex and quaternion wavelet domains: Theory and applications.” 2019. Web. 27 Feb 2021.

Vancouver:

Kong TW. Assessing self-similarity in redundant complex and quaternion wavelet domains: Theory and applications. [Internet] [Doctoral dissertation]. Georgia Tech; 2019. [cited 2021 Feb 27]. 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

7. 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)

Subjects/Keywords: classification; melanoma; feature extraction

11 more images…

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th 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 (16^th Edition):

Cho, Sungjoon. “Dermal Radiomics: a new approach for computer-aided melanoma screening system.” 2016. Thesis, University of Waterloo. Accessed February 27, 2021. 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 (7^th Edition):

Cho, Sungjoon. “Dermal Radiomics: a new approach for computer-aided melanoma screening system.” 2016. Web. 27 Feb 2021.

Vancouver:

Cho S. Dermal Radiomics: a new approach for computer-aided melanoma screening system. [Internet] [Thesis]. University of Waterloo; 2016. [cited 2021 Feb 27]. 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

California State University – Sacramento

8. 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)

Subjects/Keywords: NLP; Text mining; Feature extraction

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th 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 (16^th Edition):

Balasubramanian, Sinduja. “Text mining on Amazon reviews to extract feature based feedback.” 2017. Masters Thesis, California State University – Sacramento. Accessed February 27, 2021. http://hdl.handle.net/10211.3/198628.

MLA Handbook (7^th Edition):

Balasubramanian, Sinduja. “Text mining on Amazon reviews to extract feature based feedback.” 2017. Web. 27 Feb 2021.

Vancouver:

Balasubramanian S. Text mining on Amazon reviews to extract feature based feedback. [Internet] [Masters thesis]. California State University – Sacramento; 2017. [cited 2021 Feb 27]. 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

Penn State University

9. Xia, Wentao. VEHICULAR FSO COMMUNICATION SYSTEM APPLYING REAL-TIME RECOGNITION AND TRACKING.

Degree: 2018, Penn State University

URL: https://submit-etda.libraries.psu.edu/catalog/15350wzx12

► In self-driving vehicles, vehicle-to-vehicle communication is a key to solving traffic jams, avoiding accidents, choosing a perfect routine to destination and many other problems. In… (more)

Subjects/Keywords: self-driving; feature extraction; ORB feature; contour extraction; object tracking

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Xia, W. (2018). VEHICULAR FSO COMMUNICATION SYSTEM APPLYING REAL-TIME RECOGNITION AND TRACKING. (Thesis). Penn State University. Retrieved from https://submit-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 (16^th Edition):

Xia, Wentao. “VEHICULAR FSO COMMUNICATION SYSTEM APPLYING REAL-TIME RECOGNITION AND TRACKING.” 2018. Thesis, Penn State University. Accessed February 27, 2021. https://submit-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 (7^th Edition):

Xia, Wentao. “VEHICULAR FSO COMMUNICATION SYSTEM APPLYING REAL-TIME RECOGNITION AND TRACKING.” 2018. Web. 27 Feb 2021.

Vancouver:

Xia W. VEHICULAR FSO COMMUNICATION SYSTEM APPLYING REAL-TIME RECOGNITION AND TRACKING. [Internet] [Thesis]. Penn State University; 2018. [cited 2021 Feb 27]. Available from: https://submit-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://submit-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

10. Ahn, Eun Yeong Yeong. Evolutionary-based Feature Extraction for Gesture Recognition Using a Motion Camera.

Degree: 2012, Penn State University

URL: https://submit-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… Advisors/Committee Members: John Yen, Dissertation Advisor/Co-Advisor, John Yen, Committee Chair/Co-Chair, Dinghao Wu, Committee Member, Dongwon Lee, Committee Member, Patrick M Reed, Committee Member, Tracy Mullen, Special Member.

Subjects/Keywords: Global Feature Extraction; Local Feature Extraction; DVS camera; Evolutionary Algorithm; Segmentation

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Ahn, E. Y. Y. (2012). Evolutionary-based Feature Extraction for Gesture Recognition Using a Motion Camera. (Thesis). Penn State University. Retrieved from https://submit-etda.libraries.psu.edu/catalog/14735

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Ahn, Eun Yeong Yeong. “Evolutionary-based Feature Extraction for Gesture Recognition Using a Motion Camera.” 2012. Thesis, Penn State University. Accessed February 27, 2021. https://submit-etda.libraries.psu.edu/catalog/14735.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Ahn, Eun Yeong Yeong. “Evolutionary-based Feature Extraction for Gesture Recognition Using a Motion Camera.” 2012. Web. 27 Feb 2021.

Vancouver:

Ahn EYY. Evolutionary-based Feature Extraction for Gesture Recognition Using a Motion Camera. [Internet] [Thesis]. Penn State University; 2012. [cited 2021 Feb 27]. Available from: https://submit-etda.libraries.psu.edu/catalog/14735.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Ahn EYY. Evolutionary-based Feature Extraction for Gesture Recognition Using a Motion Camera. [Thesis]. Penn State University; 2012. Available from: https://submit-etda.libraries.psu.edu/catalog/14735

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Penn State University

11. Callahan-Flintoft, Chloe. Understanding the role of visual transients and the temporal autocorrelation of features in extending and closing the attentional window.

Degree: 2019, Penn State University

URL: https://submit-etda.libraries.psu.edu/catalog/16263czc213

► Reflexive attention allows the visual system to rapidly select and prioritize important visual information. As our environments are often changing, it is important to understand… (more)

▼ Reflexive attention allows the visual system to rapidly select and prioritize important visual information. As our environments are often changing, it is important to understand how this selection process occurs in time. To this end, previous literature finds conflicting results. Discrete display paradigms such as Rapid Serial Visual Presentation (RSVP) find that attention is able to select information presented simultaneously with a cue (Goodbourn & Holcombe, 2015) whereas smoothly changing displays show evidence that attention lags in extraction, selecting information presented over 100 ms after a cue (Carlson, Hogendoorn & Verstraten, 2006). The attentional drag theory, a conceptual model of attention proposed in this dissertation, accounts for this difference by attributing the latency in selecting information not to a difference in the latency of attentional onset, as has been previously hypothesized, but rather to a difference in the duration of attentional engagement. The model proposes that the temporal autocorrelation of a feature (i.e. when features are perceived as smoothly changing) maintains attentional engagement longer, extending the attentional window in time, which results in a later feature selection. Conversely, a visual transient, such as a salient change in the feature of an object, closes the attentional window, allowing for fewer feature values to receive attentional amplification, and, consequently, an earlier selection. The aim of this dissertation is to test the underlying assumptions of the attentional drag theory. Firstly, the effect is demonstrated in a second feature dimension (orientation) and it is shown that the temporal autocorrelation presented after the onset of attention, rather than before, produces the selection latency increase. In examining the influence of a visual transient on attentional engagement, results showed that a transient presented in a task-irrelevant feature can trigger the disengagement of attention. However, this transient needs to be presented at the attended location in order to have such an effect. This influence of one feature’s transient on another’s sampling did not prove to be reciprocal though. When two features were extracted from the same object it was found that a transient in orientation reduced the selection latency of color but a transient in color did not have an effect on the selection latency of orientation. Finally, exploratory analyses were performed on EEG data collected during feature information extraction, but no neural correlates for the varied duration of the attentional window were found. Together the results outline how visual transients and the temporal autocorrelation of a feature affect attentional sampling and could shed important insights into how the brain parses information in time to construct coherent object representations. Advisors/Committee Members: Bradley Paul Wyble, Dissertation Advisor/Co-Advisor, Bradley Paul Wyble, Committee Chair/Co-Chair, Richard Alan Carlson, Committee Member, Frank Gerard Hillary, Committee Member, John Collins, Outside Member.

Subjects/Keywords: Attentional drag theory; attention; feature extraction; N2pc

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APA (6^th Edition):

Callahan-Flintoft, C. (2019). Understanding the role of visual transients and the temporal autocorrelation of features in extending and closing the attentional window. (Thesis). Penn State University. Retrieved from https://submit-etda.libraries.psu.edu/catalog/16263czc213

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Callahan-Flintoft, Chloe. “Understanding the role of visual transients and the temporal autocorrelation of features in extending and closing the attentional window.” 2019. Thesis, Penn State University. Accessed February 27, 2021. https://submit-etda.libraries.psu.edu/catalog/16263czc213.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Callahan-Flintoft, Chloe. “Understanding the role of visual transients and the temporal autocorrelation of features in extending and closing the attentional window.” 2019. Web. 27 Feb 2021.

Vancouver:

Callahan-Flintoft C. Understanding the role of visual transients and the temporal autocorrelation of features in extending and closing the attentional window. [Internet] [Thesis]. Penn State University; 2019. [cited 2021 Feb 27]. Available from: https://submit-etda.libraries.psu.edu/catalog/16263czc213.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Callahan-Flintoft C. Understanding the role of visual transients and the temporal autocorrelation of features in extending and closing the attentional window. [Thesis]. Penn State University; 2019. Available from: https://submit-etda.libraries.psu.edu/catalog/16263czc213

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of North Texas

12. 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)

Subjects/Keywords: keyphrase extraction; graph representation learning; feature learning

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APA (6^th Edition):

Florescu, C. A. (2019). SurfKE: A Graph-Based Feature Learning Framework for Keyphrase Extraction. (Thesis). University of North Texas. Retrieved from https://digital.library.unt.edu/ark:/67531/metadc1538730/

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Florescu, Corina Andreea. “SurfKE: A Graph-Based Feature Learning Framework for Keyphrase Extraction.” 2019. Thesis, University of North Texas. Accessed February 27, 2021. https://digital.library.unt.edu/ark:/67531/metadc1538730/.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Florescu, Corina Andreea. “SurfKE: A Graph-Based Feature Learning Framework for Keyphrase Extraction.” 2019. Web. 27 Feb 2021.

Vancouver:

Florescu CA. SurfKE: A Graph-Based Feature Learning Framework for Keyphrase Extraction. [Internet] [Thesis]. University of North Texas; 2019. [cited 2021 Feb 27]. Available from: https://digital.library.unt.edu/ark:/67531/metadc1538730/.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Florescu CA. SurfKE: A Graph-Based Feature Learning Framework for Keyphrase Extraction. [Thesis]. University of North Texas; 2019. Available from: https://digital.library.unt.edu/ark:/67531/metadc1538730/

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

NSYSU

13. 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 (6^th 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 (16^th Edition):

Yang, Cheng-Ju. “Image classification via successive core tensor selection procedure.” 2018. Thesis, NSYSU. Accessed February 27, 2021. 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 (7^th Edition):

Yang, Cheng-Ju. “Image classification via successive core tensor selection procedure.” 2018. Web. 27 Feb 2021.

Vancouver:

Yang C. Image classification via successive core tensor selection procedure. [Internet] [Thesis]. NSYSU; 2018. [cited 2021 Feb 27]. 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

14. Eroğlu, Hulisi. Tek mikrofon ile ses kaynağı uzaklığı tahmini: Audio source distance estimation via single microphone.

Degree: Dil ve Tarih-Coğrafya Fakültesi, 2018, University of Ankara

URL: http://hdl.handle.net/20.500.12575/68858

► Bu tez çalışmasında, geleneksel yöntemler (öznitelik çıkarımı ve sınıflandırma) ve derin ağlar ile öğrenme olmak üzere iki ayrı yöntem, tek mikrofon ile ses kaynağı mesafesi… (more)

▼ Bu tez çalışmasında, geleneksel yöntemler (öznitelik çıkarımı ve sınıflandırma) ve derin ağlar ile öğrenme olmak üzere iki ayrı yöntem, tek mikrofon ile ses kaynağı mesafesi tahmini problemine çözüm olarak sunulmuştur. Veri seti olarak kullanılmak üzere öncelikle 1 metre, 2 metre, 3 metre ve 4 metrelik uzaklıklardan ses kayıtları toplanmıştır. Problem ilk olarak geleneksel yöntemler ile üç adımda çözülmüştür. İlk adımda ses aktivatörü kullanılarak sadece konuşma içeren ses sinyali alınmıştır ve Hanning pencereleme uygulanmıştır. İkinci adımda ise bu sinyalden öznitelikler çıkartılmıştır. Son adımda ise çıkartılan bu öznitelikler k-nn (k-nearest neighbour, k-en yakın komşuluk) sınıflandırıcı ile sınıflandırılıp mesafeye karar verilmiştir. İkinci yöntem olarak ise derin sinir ağları kullanılarak mesafeye karar verilmiştir. Derin sinir ağ yapısı, 1 giriş, 7 konvolüsyönel katman ve 1 çıkış katmanından oluşturulmuştur. Geleneksel yönteme göre derin sinir ağları ile elde edilen başarımda % 14'lük bir artış gözlenmiştir. In this thesis, traditional methods (feature extraction and classification) and deep neural networks, are presented as a solution to the sound source distance estimation problem via single microphone. As dataset, 1 meter, 2 meter, 3 meter and 4 meter recordings were used. The problem is solved in three steps by conventional methods. In the first step, a VAD (voice activity detector) and Hanning windowing are applied to speech signal. In the second step, features are extracted from this signal. In the last step, these extracted features are classified with k-nn (k-nearest neighborhood) classifier. As a second method, it has been decided to use deep neural networks. The deep network structure is composed of 1 input, 7 convolutional layers and 1 output layer. Compared with the conventional method, deep networks increased the performance of the overall system by 14 %. Advisors/Committee Members: İlk, Hakkı Gökhan (advisor).

Subjects/Keywords: Sınıflandırma; Öznitelik çıkarımı; Classification; Feature extraction

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Eroğlu, H. (2018). Tek mikrofon ile ses kaynağı uzaklığı tahmini: Audio source distance estimation via single microphone. (Masters Thesis). University of Ankara. Retrieved from http://hdl.handle.net/20.500.12575/68858

Chicago Manual of Style (16^th Edition):

Eroğlu, Hulisi. “Tek mikrofon ile ses kaynağı uzaklığı tahmini: Audio source distance estimation via single microphone.” 2018. Masters Thesis, University of Ankara. Accessed February 27, 2021. http://hdl.handle.net/20.500.12575/68858.

MLA Handbook (7^th Edition):

Eroğlu, Hulisi. “Tek mikrofon ile ses kaynağı uzaklığı tahmini: Audio source distance estimation via single microphone.” 2018. Web. 27 Feb 2021.

Vancouver:

Eroğlu H. Tek mikrofon ile ses kaynağı uzaklığı tahmini: Audio source distance estimation via single microphone. [Internet] [Masters thesis]. University of Ankara; 2018. [cited 2021 Feb 27]. Available from: http://hdl.handle.net/20.500.12575/68858.

Council of Science Editors:

Eroğlu H. Tek mikrofon ile ses kaynağı uzaklığı tahmini: Audio source distance estimation via single microphone. [Masters Thesis]. University of Ankara; 2018. Available from: http://hdl.handle.net/20.500.12575/68858

15. 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)

Subjects/Keywords: Agriculture; Characteristic; Data Visualization; Feature extraction; Plant

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APA (6^th 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 (16^th 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 27, 2021. 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 (7^th 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. 27 Feb 2021.

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 2021 Feb 27]. 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

16. 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 (6^th 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 (16^th Edition):

Tate, Anne Rosemary. “Pattern recognition analysis of in vivo magnetic resonance spectra.” 1996. Doctoral Dissertation, University of Sussex. Accessed February 27, 2021. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.307732.

MLA Handbook (7^th Edition):

Tate, Anne Rosemary. “Pattern recognition analysis of in vivo magnetic resonance spectra.” 1996. Web. 27 Feb 2021.

Vancouver:

Tate AR. Pattern recognition analysis of in vivo magnetic resonance spectra. [Internet] [Doctoral dissertation]. University of Sussex; 1996. [cited 2021 Feb 27]. 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

Linköping University

17. 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)

Subjects/Keywords: linear feature extraction; line detection; LiDAR; ditches

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APA (6^th 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 (16^th Edition):

Wasell, Richard. “Automatisk detektering av diken i LiDAR-data.” 2011. Thesis, Linköping University. Accessed February 27, 2021. 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 (7^th Edition):

Wasell, Richard. “Automatisk detektering av diken i LiDAR-data.” 2011. Web. 27 Feb 2021.

Vancouver:

Wasell R. Automatisk detektering av diken i LiDAR-data. [Internet] [Thesis]. Linköping University; 2011. [cited 2021 Feb 27]. 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

Delft University of Technology

18. Kreuk, Laura (author). Sentiment Analysis: a comparison of feature sets for social data and reviews.

Degree: 2018, Delft University of Technology

URL: http://resolver.tudelft.nl/uuid:eca6e7b5-a846-424b-ba44-84c060c29d97

►

Consumers share their experiences or opinion about products or brands in various channels nowadays, for example on review websites or social media. Sentiment analysis is… (more)

▼

Consumers share their experiences or opinion about products or brands in various channels nowadays, for example on review websites or social media. Sentiment analysis is used to predict the sentiment of text from consumers about these products or brands in order to understand the tone of customers towards these products or brands. This thesis addresses sentiment analysis in the product domain on sentence level. In this thesis three data types are used which are collected by Unilever, review data which is text that contains the opinion of a customer towards a specific product. Social data, which can be tweets, Facebook messages, Instagram messages etc. and phone data which is a summary of a phone call of a customer about a specific product. When conducting sentiment analysis one solution is to extract features from the data which can be given to a machine learning algorithm together with sentiment labels given by human annotators. The machine learning algorithm will generate a classifier which can predict a label for sentences. In sentiment analysis literature it is often not clear why certain features are chosen or for which data type certain features will work well. In this research we compare the differences when using several feature sets for the different data types. We propose three feature sets for review data and three feature sets for social data. We focus on two aspects, comparing the different feature sets and comparing the data types. In our results we do not find significant differences in performance between the feature sets. The results suggest there might be feature sets which can improve sentiment analysis specifically for the data type, but a general feature set with standard features can be comparable to that result.

Computer Science | Data Science and Technology | Information Architecture

Advisors/Committee Members: Tintarev, Nava (mentor), Houben, Geert-Jan (graduation committee), Urbano Merino, Julian (graduation committee), Delft University of Technology (degree granting institution).

Subjects/Keywords: Sentiment Analysis; feature extraction; reviews; social data

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APA (6^th Edition):

Kreuk, L. (. (2018). Sentiment Analysis: a comparison of feature sets for social data and reviews. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:eca6e7b5-a846-424b-ba44-84c060c29d97

Chicago Manual of Style (16^th Edition):

Kreuk, Laura (author). “Sentiment Analysis: a comparison of feature sets for social data and reviews.” 2018. Masters Thesis, Delft University of Technology. Accessed February 27, 2021. http://resolver.tudelft.nl/uuid:eca6e7b5-a846-424b-ba44-84c060c29d97.

MLA Handbook (7^th Edition):

Kreuk, Laura (author). “Sentiment Analysis: a comparison of feature sets for social data and reviews.” 2018. Web. 27 Feb 2021.

Vancouver:

Kreuk L(. Sentiment Analysis: a comparison of feature sets for social data and reviews. [Internet] [Masters thesis]. Delft University of Technology; 2018. [cited 2021 Feb 27]. Available from: http://resolver.tudelft.nl/uuid:eca6e7b5-a846-424b-ba44-84c060c29d97.

Council of Science Editors:

Kreuk L(. Sentiment Analysis: a comparison of feature sets for social data and reviews. [Masters Thesis]. Delft University of Technology; 2018. Available from: http://resolver.tudelft.nl/uuid:eca6e7b5-a846-424b-ba44-84c060c29d97

University of Illinois – Urbana-Champaign

19. 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)

Subjects/Keywords: clothing; retrieval; landmarks; keypoints; feature extraction

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th 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 (16^th Edition):

Jain, Shubham. “Landmarks for clothing retrieval.” 2019. Thesis, University of Illinois – Urbana-Champaign. Accessed February 27, 2021. 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 (7^th Edition):

Jain, Shubham. “Landmarks for clothing retrieval.” 2019. Web. 27 Feb 2021.

Vancouver:

Jain S. Landmarks for clothing retrieval. [Internet] [Thesis]. University of Illinois – Urbana-Champaign; 2019. [cited 2021 Feb 27]. 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

University of Waterloo

20. 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)

Subjects/Keywords: remote sensing; classification; segmentation; feature extraction.

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th 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 (16^th Edition):

Li, Fan. “Automated Remote Sensing Image Interpretation with Limited Labeled Training Data.” 2015. Thesis, University of Waterloo. Accessed February 27, 2021. 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 (7^th Edition):

Li, Fan. “Automated Remote Sensing Image Interpretation with Limited Labeled Training Data.” 2015. Web. 27 Feb 2021.

Vancouver:

Li F. Automated Remote Sensing Image Interpretation with Limited Labeled Training Data. [Internet] [Thesis]. University of Waterloo; 2015. [cited 2021 Feb 27]. 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

Louisiana State University

21. 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 · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th 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 (16^th Edition):

Chen, Jing. “Vocal Fold Analysis From High Speed Videoendoscopic Data.” 2014. Doctoral Dissertation, Louisiana State University. Accessed February 27, 2021. etd-05152014-135123 ; https://digitalcommons.lsu.edu/gradschool_dissertations/664.

MLA Handbook (7^th Edition):

Chen, Jing. “Vocal Fold Analysis From High Speed Videoendoscopic Data.” 2014. Web. 27 Feb 2021.

Vancouver:

Chen J. Vocal Fold Analysis From High Speed Videoendoscopic Data. [Internet] [Doctoral dissertation]. Louisiana State University; 2014. [cited 2021 Feb 27]. 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

University of New South Wales

22. 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 (6^th 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 (16^th Edition):

Li, Zelin. “Feature learning, selection and representation for object detection and recognition.” 2017. Doctoral Dissertation, University of New South Wales. Accessed February 27, 2021. http://handle.unsw.edu.au/1959.4/58813 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:47626/SOURCE02?view=true.

MLA Handbook (7^th Edition):

Li, Zelin. “Feature learning, selection and representation for object detection and recognition.” 2017. Web. 27 Feb 2021.

Vancouver:

Li Z. Feature learning, selection and representation for object detection and recognition. [Internet] [Doctoral dissertation]. University of New South Wales; 2017. [cited 2021 Feb 27]. 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

University of New South Wales

23. Wang, Yunqi. Detection and Classification of Disturbances in Islanded Micro-grid.

Degree: Electrical Engineering & Telecommunications, 2018, University of New South Wales

URL: http://handle.unsw.edu.au/1959.4/61517 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:56398/SOURCE02?view=true

► Micro-grids are envisaged as single plug-and-play electrical systems that can operate either in grid-connected or islanded modes. Their main sources of power are renewable and… (more)

▼ Micro-grids are envisaged as single plug-and-play electrical systems that can operate either in grid-connected or islanded modes. Their main sources of power are renewable and are usually integrated through power electronic devices. These present new challenges to reliable operation of the system due to their inherent incapability to regulate the system. Power quality (PQ) disturbances in the micro-grid make it further vulnerable, especially when the micro-grid operates in an islanded mode. The PQ disturbances only occur for a few cycles and cannot be easily detected directly in the time domain.This thesis utilizes wavelet transformation (WT) method to detect and analyse the disturbance signals in the islanded micro-grid. The signals are collected from both simulation and experimental setup. The wavelet-based and time domain based feature extraction algorithms are investigated and compared. Factors that can affect the performance of these methods are discussed. Furthermore, three different types of wavelet-based feature extraction methods are also tested and two of them – normilized Renyi entropy with signal energy and normalized Renyi entropy with distribution volume, are algorithms which have not been applied into the power system before.The results show that the disturbances created by using simulated data are successfully identified using wavelet transformation. The variation of wavelet coefficients helps in identifying the time and duration of the disturbances. However, when data from the micro-grid laboratory setup is used, the performance of the WT is not satisfactory due to presence of noise and harmonics. Although, single disturbances such as voltage sag, swell and interruption can still be identified and classified, the WT is unable to detect and classify multiple disturbances, especially when non-linear loads are involved. The presence of non-linear loads in islanded micro-grids, increases the fluctuations of the WT coefficients, thus making the disturbance signals to be buried.To overcome this issue, the Renyi entropy is used to improve the performance of WT. A comparison is made between the time-domain Renyi and wavelet-based Renyi. The results show that the wavelet-based Renyi has improved efficiency. Two normalized Renyi entropies are also investigated and compared with the normal Renyi entropy, to overcome the problem whereby Renyi entropy fails to detect zero-mean cross-terms in some cases when odd  is used, as well as to reduce the distribution to the unity signal energy case. These two algorithms are found to show higher accuracy and efficiency for classifying multiple disturbances in an islanded micro-grid. Advisors/Committee Members: Ravishankar, Jayashri, Electrical Engineering & Telecommunications, Faculty of Engineering, UNSW, Phung, Toan, Electrical Engineering & Telecommunications, Faculty of Engineering, UNSW.

Subjects/Keywords: Feature extraction; Power quality; Micro-grid

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Wang, Y. (2018). Detection and Classification of Disturbances in Islanded Micro-grid. (Doctoral Dissertation). University of New South Wales. Retrieved from http://handle.unsw.edu.au/1959.4/61517 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:56398/SOURCE02?view=true

Chicago Manual of Style (16^th Edition):

Wang, Yunqi. “Detection and Classification of Disturbances in Islanded Micro-grid.” 2018. Doctoral Dissertation, University of New South Wales. Accessed February 27, 2021. http://handle.unsw.edu.au/1959.4/61517 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:56398/SOURCE02?view=true.

MLA Handbook (7^th Edition):

Wang, Yunqi. “Detection and Classification of Disturbances in Islanded Micro-grid.” 2018. Web. 27 Feb 2021.

Vancouver:

Wang Y. Detection and Classification of Disturbances in Islanded Micro-grid. [Internet] [Doctoral dissertation]. University of New South Wales; 2018. [cited 2021 Feb 27]. Available from: http://handle.unsw.edu.au/1959.4/61517 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:56398/SOURCE02?view=true.

Council of Science Editors:

Wang Y. Detection and Classification of Disturbances in Islanded Micro-grid. [Doctoral Dissertation]. University of New South Wales; 2018. Available from: http://handle.unsw.edu.au/1959.4/61517 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:56398/SOURCE02?view=true

24. Wan, Huan. Cluster-based supervised classification.

Degree: PhD, 2020, Ulster University

URL: https://pure.ulster.ac.uk/en/studentTheses/7e984d07-2ce4-4959-8063-32f8ba233958 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.821060

► Supervised classification is one of our fundamental approaches to understanding the world, and is studied in many research areas. Feature extraction and classification learning are… (more)

▼ Supervised classification is one of our fundamental approaches to understanding the world, and is studied in many research areas. Feature extraction and classification learning are two key processes, which significantly influence the performance of supervised classification. Although impressive progress has been made in supervised classification due to the development of feature extraction methods and classifiers, there are still unsolved problems in supervised classification, such as the class imbalance problem and the few-shot classification problem. In this thesis, we focus on the complex boundary problem — it is hard to obtain high classification accuracy for problems with complex decision boundaries due to the existence of subclass structures. We propose a cluster-based approach to supervised classification and develop cluster-based feature extraction methods and cluster-based classification learning methods. For feature extraction, to find out the importance of considering within-class multimodality for feature extraction, we conduct a study on within-class multimodal data distribution and classification under such a distribution. This study is guided by five important questions about within-class multimodal data. Systematic experiments using a variety of artificial and real data are conducted to answer the five questions, which further lead to some useful findings. In the second study, a new feature extraction method is proposed, called global subclass discriminant analysis (GSDA). To extract discriminative features, GSDA first obtains clusters in a global way by clustering the whole data set and derives class-specific clusters based on these global clusters. Then it seeks to maximise interclass distance and minimise intraclass distance based on these class-specific clusters. GSDA is extensively evaluated on a wide range of data through comparison with the closely related and state-of-the-art feature extraction methods. Experimental results demonstrate GSDA’s superiority in terms of accuracy and run time. For classification learning, in the third study, we propose a cluster-based data relabelling (CBDR) method for improving the classification performance of existing classifiers on nonlinear data. CBDR aims to impel classifiers to find cluster-based decision boundaries rather than class-based decision boundaries. Extensive experimentations demonstrate that CBDR dramatically boosts the classification performance of classifiers on nonlinear data, especially for linear classifiers. In the final study, a novel Gaussian mixture model (GMM) classifier is proposed, called separability criterion based GMM (SC-GMM) classifier. In SC-GMM, the separability criterion is employed to find the optimal number of Gaussian components for GMM. Experiments have been carried out on various classification tasks. Experimental results demonstrate the superiority of the SC-GMM classifier.

Subjects/Keywords: Clustering; Classification; LDA; Feature extraction; Subclass

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Wan, H. (2020). Cluster-based supervised classification. (Doctoral Dissertation). Ulster University. Retrieved from https://pure.ulster.ac.uk/en/studentTheses/7e984d07-2ce4-4959-8063-32f8ba233958 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.821060

Chicago Manual of Style (16^th Edition):

Wan, Huan. “Cluster-based supervised classification.” 2020. Doctoral Dissertation, Ulster University. Accessed February 27, 2021. https://pure.ulster.ac.uk/en/studentTheses/7e984d07-2ce4-4959-8063-32f8ba233958 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.821060.

MLA Handbook (7^th Edition):

Wan, Huan. “Cluster-based supervised classification.” 2020. Web. 27 Feb 2021.

Vancouver:

Wan H. Cluster-based supervised classification. [Internet] [Doctoral dissertation]. Ulster University; 2020. [cited 2021 Feb 27]. Available from: https://pure.ulster.ac.uk/en/studentTheses/7e984d07-2ce4-4959-8063-32f8ba233958 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.821060.

Council of Science Editors:

Wan H. Cluster-based supervised classification. [Doctoral Dissertation]. Ulster University; 2020. Available from: https://pure.ulster.ac.uk/en/studentTheses/7e984d07-2ce4-4959-8063-32f8ba233958 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.821060

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)

Subjects/Keywords: birdcall retrieval; feature extraction; spectral ridge feature; event representation; environmental recording

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th 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 (16^th Edition):

Dong, Xueyan. “Content-based birdcall retrieval from environmental audio.” 2016. Thesis, Queensland University of Technology. Accessed February 27, 2021. 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 (7^th Edition):

Dong, Xueyan. “Content-based birdcall retrieval from environmental audio.” 2016. Web. 27 Feb 2021.

Vancouver:

Dong X. Content-based birdcall retrieval from environmental audio. [Internet] [Thesis]. Queensland University of Technology; 2016. [cited 2021 Feb 27]. 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

Delft University of Technology

26. Wang, Yuyang (author). An automated ECG signal quality assessment method with supervised learning algorithm.

Degree: 2018, Delft University of Technology

URL: http://resolver.tudelft.nl/uuid:41eab0f1-05a5-440c-a43b-f2f12b15a8aa

► Wearable health has become a striking area in our daily life. Electrocardiogram (ECG) is one of the biomedical signals collected by the wearable or portable… (more)

Subjects/Keywords: ECG signal; quality assessment; Supervised Learning; Feature extraction; Feature selection

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APA (6^th Edition):

Wang, Y. (. (2018). An automated ECG signal quality assessment method with supervised learning algorithm. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:41eab0f1-05a5-440c-a43b-f2f12b15a8aa

Chicago Manual of Style (16^th Edition):

Wang, Yuyang (author). “An automated ECG signal quality assessment method with supervised learning algorithm.” 2018. Masters Thesis, Delft University of Technology. Accessed February 27, 2021. http://resolver.tudelft.nl/uuid:41eab0f1-05a5-440c-a43b-f2f12b15a8aa.

MLA Handbook (7^th Edition):

Wang, Yuyang (author). “An automated ECG signal quality assessment method with supervised learning algorithm.” 2018. Web. 27 Feb 2021.

Vancouver:

Wang Y(. An automated ECG signal quality assessment method with supervised learning algorithm. [Internet] [Masters thesis]. Delft University of Technology; 2018. [cited 2021 Feb 27]. Available from: http://resolver.tudelft.nl/uuid:41eab0f1-05a5-440c-a43b-f2f12b15a8aa.

Council of Science Editors:

Wang Y(. An automated ECG signal quality assessment method with supervised learning algorithm. [Masters Thesis]. Delft University of Technology; 2018. Available from: http://resolver.tudelft.nl/uuid:41eab0f1-05a5-440c-a43b-f2f12b15a8aa

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 · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th 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 (16^th Edition):

Luo, Jiajia. “Feature Extraction and Recognition for Human Action Recognition.” 2014. Doctoral Dissertation, University of Tennessee – Knoxville. Accessed February 27, 2021. https://trace.tennessee.edu/utk_graddiss/2710.

MLA Handbook (7^th Edition):

Luo, Jiajia. “Feature Extraction and Recognition for Human Action Recognition.” 2014. Web. 27 Feb 2021.

Vancouver:

Luo J. Feature Extraction and Recognition for Human Action Recognition. [Internet] [Doctoral dissertation]. University of Tennessee – Knoxville; 2014. [cited 2021 Feb 27]. 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

Arizona State University

28. 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 · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th 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 (16^th Edition):

Yamak, Didem. “Characterization of Coronary Atherosclerotic Plaques by Dual Energy Computed Tomography.” 2013. Doctoral Dissertation, Arizona State University. Accessed February 27, 2021. http://repository.asu.edu/items/18027.

MLA Handbook (7^th Edition):

Yamak, Didem. “Characterization of Coronary Atherosclerotic Plaques by Dual Energy Computed Tomography.” 2013. Web. 27 Feb 2021.

Vancouver:

Yamak D. Characterization of Coronary Atherosclerotic Plaques by Dual Energy Computed Tomography. [Internet] [Doctoral dissertation]. Arizona State University; 2013. [cited 2021 Feb 27]. 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

29. Liu, Ren-jia. A Self-Constructing Fuzzy Feature Clustering for Text Categorization.

Degree: Master, Electrical Engineering, 2009, NSYSU

URL: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0826109-151344

► Feature clustering is a powerful method to reduce the dimensionality of feature vectors for text classification. In this paper, we propose a fuzzy similarity-based self-constructing… (more)

Subjects/Keywords: text classification; feature reduction; feature clustering; feature extraction; fuzzy clustering; fuzzy similarity

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Liu, R. (2009). A Self-Constructing Fuzzy Feature Clustering for Text Categorization. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0826109-151344

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Liu, Ren-jia. “A Self-Constructing Fuzzy Feature Clustering for Text Categorization.” 2009. Thesis, NSYSU. Accessed February 27, 2021. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0826109-151344.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Liu, Ren-jia. “A Self-Constructing Fuzzy Feature Clustering for Text Categorization.” 2009. Web. 27 Feb 2021.

Vancouver:

Liu R. A Self-Constructing Fuzzy Feature Clustering for Text Categorization. [Internet] [Thesis]. NSYSU; 2009. [cited 2021 Feb 27]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0826109-151344.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Liu R. A Self-Constructing Fuzzy Feature Clustering for Text Categorization. [Thesis]. NSYSU; 2009. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0826109-151344

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

NSYSU

30. Wu, Bo-sheng. Acceleration of Image Feature Extraction Algorithms.

Degree: Master, Computer Science and Engineering, 2014, NSYSU

URL: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0810114-020324

► The description of local features of images has been successfully applied to many areas, including wide baseline matching, object recognition, texture recognition, image retrieval, robot… (more)

Subjects/Keywords: scale-invariant feature transform; Speeded-Up Robust Feature; hardware acceleration; image feature extraction; OpenCL; GPGPU

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❌

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Wu, B. (2014). Acceleration of Image Feature Extraction Algorithms. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0810114-020324

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Wu, Bo-sheng. “Acceleration of Image Feature Extraction Algorithms.” 2014. Thesis, NSYSU. Accessed February 27, 2021. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0810114-020324.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Wu, Bo-sheng. “Acceleration of Image Feature Extraction Algorithms.” 2014. Web. 27 Feb 2021.

Vancouver:

Wu B. Acceleration of Image Feature Extraction Algorithms. [Internet] [Thesis]. NSYSU; 2014. [cited 2021 Feb 27]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0810114-020324.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Wu B. Acceleration of Image Feature Extraction Algorithms. [Thesis]. NSYSU; 2014. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0810114-020324

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

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Open Access Theses and Dissertations