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You searched for subject:(Image classification). Showing records 1 – 30 of 956 total matches.

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University of Alberta

1. Xu,Bing. Deep Convolutional Networks for Image Classification.

Degree: MS, Department of Computing Science, 2016, University of Alberta

Image classification is an important problem in machine learning. Deep neural networks, particularly deep convolutional networks, have recently contributed great improvements to end-to-end learning quality… (more)

Subjects/Keywords: Deep Networks; Image Classification

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

APA (6th Edition):

Xu,Bing. (2016). Deep Convolutional Networks for Image Classification. (Masters Thesis). University of Alberta. Retrieved from https://era.library.ualberta.ca/files/cmc87pq29m

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Chicago Manual of Style (16th Edition):

Xu,Bing. “Deep Convolutional Networks for Image Classification.” 2016. Masters Thesis, University of Alberta. Accessed September 26, 2020. https://era.library.ualberta.ca/files/cmc87pq29m.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

MLA Handbook (7th Edition):

Xu,Bing. “Deep Convolutional Networks for Image Classification.” 2016. Web. 26 Sep 2020.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Vancouver:

Xu,Bing. Deep Convolutional Networks for Image Classification. [Internet] [Masters thesis]. University of Alberta; 2016. [cited 2020 Sep 26]. Available from: https://era.library.ualberta.ca/files/cmc87pq29m.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Council of Science Editors:

Xu,Bing. Deep Convolutional Networks for Image Classification. [Masters Thesis]. University of Alberta; 2016. Available from: https://era.library.ualberta.ca/files/cmc87pq29m

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete


University of Canterbury

2. Pahalawatta, Kapila Kithsiri. Image histogram features for nano-scale particle detection and classification.

Degree: PhD, Computer Science, 2015, University of Canterbury

 This research proposes a method to detect and classify the smoke particles of common household fires by analysing the image histogram features of smoke particles… (more)

Subjects/Keywords: Image Histogram Features; Particle Classification

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APA (6th Edition):

Pahalawatta, K. K. (2015). Image histogram features for nano-scale particle detection and classification. (Doctoral Dissertation). University of Canterbury. Retrieved from http://dx.doi.org/10.26021/3175

Chicago Manual of Style (16th Edition):

Pahalawatta, Kapila Kithsiri. “Image histogram features for nano-scale particle detection and classification.” 2015. Doctoral Dissertation, University of Canterbury. Accessed September 26, 2020. http://dx.doi.org/10.26021/3175.

MLA Handbook (7th Edition):

Pahalawatta, Kapila Kithsiri. “Image histogram features for nano-scale particle detection and classification.” 2015. Web. 26 Sep 2020.

Vancouver:

Pahalawatta KK. Image histogram features for nano-scale particle detection and classification. [Internet] [Doctoral dissertation]. University of Canterbury; 2015. [cited 2020 Sep 26]. Available from: http://dx.doi.org/10.26021/3175.

Council of Science Editors:

Pahalawatta KK. Image histogram features for nano-scale particle detection and classification. [Doctoral Dissertation]. University of Canterbury; 2015. Available from: http://dx.doi.org/10.26021/3175


Texas State University – San Marcos

3. Mekala, Sai Swathi. Research of Water Detection in Autonomous Vehicles.

Degree: MS, Engineering, 2019, Texas State University – San Marcos

 An autonomous car is a ground vehicle that navigates without human input. These vehicles are expected to reach $60 billion in sales by 2025. But… (more)

Subjects/Keywords: Image classification; Automated vehicles; Detectors

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APA (6th Edition):

Mekala, S. S. (2019). Research of Water Detection in Autonomous Vehicles. (Masters Thesis). Texas State University – San Marcos. Retrieved from https://digital.library.txstate.edu/handle/10877/8998

Chicago Manual of Style (16th Edition):

Mekala, Sai Swathi. “Research of Water Detection in Autonomous Vehicles.” 2019. Masters Thesis, Texas State University – San Marcos. Accessed September 26, 2020. https://digital.library.txstate.edu/handle/10877/8998.

MLA Handbook (7th Edition):

Mekala, Sai Swathi. “Research of Water Detection in Autonomous Vehicles.” 2019. Web. 26 Sep 2020.

Vancouver:

Mekala SS. Research of Water Detection in Autonomous Vehicles. [Internet] [Masters thesis]. Texas State University – San Marcos; 2019. [cited 2020 Sep 26]. Available from: https://digital.library.txstate.edu/handle/10877/8998.

Council of Science Editors:

Mekala SS. Research of Water Detection in Autonomous Vehicles. [Masters Thesis]. Texas State University – San Marcos; 2019. Available from: https://digital.library.txstate.edu/handle/10877/8998


The Ohio State University

4. Ozendi, Mustafa. Viewpoint Independent Image Classification and Retrieval.

Degree: MS, Geodetic Science and Surveying, 2010, The Ohio State University

Image retrieval has applications in different disciplines. For example, there are applications in digital painting catalogues and in security related applications Researchers from both computer… (more)

Subjects/Keywords: Computer Science; image retrieval; image classification; projective invariant; viewpoint independent retrieval; viewpoint independent image classification

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

APA (6th Edition):

Ozendi, M. (2010). Viewpoint Independent Image Classification and Retrieval. (Masters Thesis). The Ohio State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=osu1285011830

Chicago Manual of Style (16th Edition):

Ozendi, Mustafa. “Viewpoint Independent Image Classification and Retrieval.” 2010. Masters Thesis, The Ohio State University. Accessed September 26, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1285011830.

MLA Handbook (7th Edition):

Ozendi, Mustafa. “Viewpoint Independent Image Classification and Retrieval.” 2010. Web. 26 Sep 2020.

Vancouver:

Ozendi M. Viewpoint Independent Image Classification and Retrieval. [Internet] [Masters thesis]. The Ohio State University; 2010. [cited 2020 Sep 26]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1285011830.

Council of Science Editors:

Ozendi M. Viewpoint Independent Image Classification and Retrieval. [Masters Thesis]. The Ohio State University; 2010. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1285011830


New Jersey Institute of Technology

5. Banerji, Sugata. Novel color and local image descriptors for content-based image search.

Degree: PhD, Computer Science, 2013, New Jersey Institute of Technology

  Content-based image classification, search and retrieval is a rapidly-expanding research area. With the advent of inexpensive digital cameras, cheap data storage, fast computing speeds… (more)

Subjects/Keywords: Image search; Image classification; Image processing; Color features; Texture features; Scene classification; Computer Sciences

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APA (6th Edition):

Banerji, S. (2013). Novel color and local image descriptors for content-based image search. (Doctoral Dissertation). New Jersey Institute of Technology. Retrieved from https://digitalcommons.njit.edu/dissertations/358

Chicago Manual of Style (16th Edition):

Banerji, Sugata. “Novel color and local image descriptors for content-based image search.” 2013. Doctoral Dissertation, New Jersey Institute of Technology. Accessed September 26, 2020. https://digitalcommons.njit.edu/dissertations/358.

MLA Handbook (7th Edition):

Banerji, Sugata. “Novel color and local image descriptors for content-based image search.” 2013. Web. 26 Sep 2020.

Vancouver:

Banerji S. Novel color and local image descriptors for content-based image search. [Internet] [Doctoral dissertation]. New Jersey Institute of Technology; 2013. [cited 2020 Sep 26]. Available from: https://digitalcommons.njit.edu/dissertations/358.

Council of Science Editors:

Banerji S. Novel color and local image descriptors for content-based image search. [Doctoral Dissertation]. New Jersey Institute of Technology; 2013. Available from: https://digitalcommons.njit.edu/dissertations/358


Universidade Nova

6. Luu, Thi Phuong Mai. Wetland Habitat Studies using various Classification Techniques on Multi-Spectral Landsat Imagery: Case study: Tram chim National Park, Dong Thap Vietnam.

Degree: 2009, Universidade Nova

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial Technologies

Wetland is one of the most valuable… (more)

Subjects/Keywords: Image classification; Isodata; Rule based classification; Hybrid classification

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APA (6th Edition):

Luu, T. P. M. (2009). Wetland Habitat Studies using various Classification Techniques on Multi-Spectral Landsat Imagery: Case study: Tram chim National Park, Dong Thap Vietnam. (Thesis). Universidade Nova. Retrieved from http://www.rcaap.pt/detail.jsp?id=oai:run.unl.pt:10362/2634

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

Chicago Manual of Style (16th Edition):

Luu, Thi Phuong Mai. “Wetland Habitat Studies using various Classification Techniques on Multi-Spectral Landsat Imagery: Case study: Tram chim National Park, Dong Thap Vietnam.” 2009. Thesis, Universidade Nova. Accessed September 26, 2020. http://www.rcaap.pt/detail.jsp?id=oai:run.unl.pt:10362/2634.

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

MLA Handbook (7th Edition):

Luu, Thi Phuong Mai. “Wetland Habitat Studies using various Classification Techniques on Multi-Spectral Landsat Imagery: Case study: Tram chim National Park, Dong Thap Vietnam.” 2009. Web. 26 Sep 2020.

Vancouver:

Luu TPM. Wetland Habitat Studies using various Classification Techniques on Multi-Spectral Landsat Imagery: Case study: Tram chim National Park, Dong Thap Vietnam. [Internet] [Thesis]. Universidade Nova; 2009. [cited 2020 Sep 26]. Available from: http://www.rcaap.pt/detail.jsp?id=oai:run.unl.pt:10362/2634.

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

Council of Science Editors:

Luu TPM. Wetland Habitat Studies using various Classification Techniques on Multi-Spectral Landsat Imagery: Case study: Tram chim National Park, Dong Thap Vietnam. [Thesis]. Universidade Nova; 2009. Available from: http://www.rcaap.pt/detail.jsp?id=oai:run.unl.pt:10362/2634

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


University of North Texas

7. Sure, Venkata Leela. Enhanced Approach for the Classification of Ulcerative Colitis Severity in Colonoscopy Videos Using CNN.

Degree: 2019, University of North Texas

 Ulcerative colitis (UC) is a chronic inflammatory disease characterized by periods of relapses and remissions affecting more than 500,000 people in the United States. To… (more)

Subjects/Keywords: Convolutional Neural Network; Medical Image Classification

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

APA (6th Edition):

Sure, V. L. (2019). Enhanced Approach for the Classification of Ulcerative Colitis Severity in Colonoscopy Videos Using CNN. (Thesis). University of North Texas. Retrieved from https://digital.library.unt.edu/ark:/67531/metadc1538703/

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

Chicago Manual of Style (16th Edition):

Sure, Venkata Leela. “Enhanced Approach for the Classification of Ulcerative Colitis Severity in Colonoscopy Videos Using CNN.” 2019. Thesis, University of North Texas. Accessed September 26, 2020. https://digital.library.unt.edu/ark:/67531/metadc1538703/.

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

MLA Handbook (7th Edition):

Sure, Venkata Leela. “Enhanced Approach for the Classification of Ulcerative Colitis Severity in Colonoscopy Videos Using CNN.” 2019. Web. 26 Sep 2020.

Vancouver:

Sure VL. Enhanced Approach for the Classification of Ulcerative Colitis Severity in Colonoscopy Videos Using CNN. [Internet] [Thesis]. University of North Texas; 2019. [cited 2020 Sep 26]. Available from: https://digital.library.unt.edu/ark:/67531/metadc1538703/.

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

Council of Science Editors:

Sure VL. Enhanced Approach for the Classification of Ulcerative Colitis Severity in Colonoscopy Videos Using CNN. [Thesis]. University of North Texas; 2019. Available from: https://digital.library.unt.edu/ark:/67531/metadc1538703/

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


University of Pennsylvania

8. Shin, Daniel B. Novel Statistical Methodologies in Analysis of Position Emission Tomography Data: Applications in Segmentation, Normalization, and Trajectory Modeling.

Degree: 2016, University of Pennsylvania

 Position emission tomography (PET) is a powerful functional imaging modality with wide uses in fields such as oncology, cardiology, and neurology. Motivated by imaging datasets… (more)

Subjects/Keywords: Classification; Image normalization; Nonlinear models; ROC; Biostatistics

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APA (6th Edition):

Shin, D. B. (2016). Novel Statistical Methodologies in Analysis of Position Emission Tomography Data: Applications in Segmentation, Normalization, and Trajectory Modeling. (Thesis). University of Pennsylvania. Retrieved from https://repository.upenn.edu/edissertations/2010

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

Chicago Manual of Style (16th Edition):

Shin, Daniel B. “Novel Statistical Methodologies in Analysis of Position Emission Tomography Data: Applications in Segmentation, Normalization, and Trajectory Modeling.” 2016. Thesis, University of Pennsylvania. Accessed September 26, 2020. https://repository.upenn.edu/edissertations/2010.

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

MLA Handbook (7th Edition):

Shin, Daniel B. “Novel Statistical Methodologies in Analysis of Position Emission Tomography Data: Applications in Segmentation, Normalization, and Trajectory Modeling.” 2016. Web. 26 Sep 2020.

Vancouver:

Shin DB. Novel Statistical Methodologies in Analysis of Position Emission Tomography Data: Applications in Segmentation, Normalization, and Trajectory Modeling. [Internet] [Thesis]. University of Pennsylvania; 2016. [cited 2020 Sep 26]. Available from: https://repository.upenn.edu/edissertations/2010.

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

Council of Science Editors:

Shin DB. Novel Statistical Methodologies in Analysis of Position Emission Tomography Data: Applications in Segmentation, Normalization, and Trajectory Modeling. [Thesis]. University of Pennsylvania; 2016. Available from: https://repository.upenn.edu/edissertations/2010

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


NSYSU

9. Yang, Cheng-Ju. Image classification via successive core tensor selection procedure.

Degree: Master, Applied Mathematics, 2018, NSYSU

 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)

Subjects/Keywords: data feature extraction; image classification; tensor decomposition

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APA (6th Edition):

Yang, C. (2018). Image classification via successive core tensor selection procedure. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0606118-151922

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

Chicago Manual of Style (16th Edition):

Yang, Cheng-Ju. “Image classification via successive core tensor selection procedure.” 2018. Thesis, NSYSU. Accessed September 26, 2020. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0606118-151922.

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

MLA Handbook (7th Edition):

Yang, Cheng-Ju. “Image classification via successive core tensor selection procedure.” 2018. Web. 26 Sep 2020.

Vancouver:

Yang C. Image classification via successive core tensor selection procedure. [Internet] [Thesis]. NSYSU; 2018. [cited 2020 Sep 26]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0606118-151922.

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

Council of Science Editors:

Yang C. Image classification via successive core tensor selection procedure. [Thesis]. NSYSU; 2018. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0606118-151922

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


Université Catholique de Louvain

10. Bernard, Guillaume. Incremental organ segmentation with machine learning techniques : application to radiotherapy.

Degree: 2014, Université Catholique de Louvain

Radiotherapy is a cancer treatment modality that can be considered as a ballistic problem where the tumour must be irradiated while sparing the surrounding healthy… (more)

Subjects/Keywords: Radiotherapy; Machine learning; Image segmentation; Incremental classification

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APA (6th Edition):

Bernard, G. (2014). Incremental organ segmentation with machine learning techniques : application to radiotherapy. (Thesis). Université Catholique de Louvain. Retrieved from http://hdl.handle.net/2078.1/153438

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

Chicago Manual of Style (16th Edition):

Bernard, Guillaume. “Incremental organ segmentation with machine learning techniques : application to radiotherapy.” 2014. Thesis, Université Catholique de Louvain. Accessed September 26, 2020. http://hdl.handle.net/2078.1/153438.

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

MLA Handbook (7th Edition):

Bernard, Guillaume. “Incremental organ segmentation with machine learning techniques : application to radiotherapy.” 2014. Web. 26 Sep 2020.

Vancouver:

Bernard G. Incremental organ segmentation with machine learning techniques : application to radiotherapy. [Internet] [Thesis]. Université Catholique de Louvain; 2014. [cited 2020 Sep 26]. Available from: http://hdl.handle.net/2078.1/153438.

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

Council of Science Editors:

Bernard G. Incremental organ segmentation with machine learning techniques : application to radiotherapy. [Thesis]. Université Catholique de Louvain; 2014. Available from: http://hdl.handle.net/2078.1/153438

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


University of Southern California

11. Kim, Eunyoung. Scalable object classification using range images.

Degree: PhD, Computer Science, 2011, University of Southern California

 Object classification using depth images has been actively studied in robotics and computer vision fields to autonomously recognize 3D objects in the scene even under… (more)

Subjects/Keywords: computer vision; object classification; range image

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APA (6th Edition):

Kim, E. (2011). Scalable object classification using range images. (Doctoral Dissertation). University of Southern California. Retrieved from http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/198401/rec/5691

Chicago Manual of Style (16th Edition):

Kim, Eunyoung. “Scalable object classification using range images.” 2011. Doctoral Dissertation, University of Southern California. Accessed September 26, 2020. http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/198401/rec/5691.

MLA Handbook (7th Edition):

Kim, Eunyoung. “Scalable object classification using range images.” 2011. Web. 26 Sep 2020.

Vancouver:

Kim E. Scalable object classification using range images. [Internet] [Doctoral dissertation]. University of Southern California; 2011. [cited 2020 Sep 26]. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/198401/rec/5691.

Council of Science Editors:

Kim E. Scalable object classification using range images. [Doctoral Dissertation]. University of Southern California; 2011. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/198401/rec/5691


Georgia Tech

12. Prabhu, Viraj Uday. Few-shot learning for dermatological disease diagnosis.

Degree: MS, Computer Science, 2019, Georgia Tech

 In this thesis, we consider the problem of clinical image classification for the purpose of aiding doctors in dermatological disease diagnosis. Diagnosis of dermatological disease… (more)

Subjects/Keywords: Image classification; Low shot learning; Automated diagnosis

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APA (6th Edition):

Prabhu, V. U. (2019). Few-shot learning for dermatological disease diagnosis. (Masters Thesis). Georgia Tech. Retrieved from http://hdl.handle.net/1853/61296

Chicago Manual of Style (16th Edition):

Prabhu, Viraj Uday. “Few-shot learning for dermatological disease diagnosis.” 2019. Masters Thesis, Georgia Tech. Accessed September 26, 2020. http://hdl.handle.net/1853/61296.

MLA Handbook (7th Edition):

Prabhu, Viraj Uday. “Few-shot learning for dermatological disease diagnosis.” 2019. Web. 26 Sep 2020.

Vancouver:

Prabhu VU. Few-shot learning for dermatological disease diagnosis. [Internet] [Masters thesis]. Georgia Tech; 2019. [cited 2020 Sep 26]. Available from: http://hdl.handle.net/1853/61296.

Council of Science Editors:

Prabhu VU. Few-shot learning for dermatological disease diagnosis. [Masters Thesis]. Georgia Tech; 2019. Available from: http://hdl.handle.net/1853/61296


Delft University of Technology

13. Soilis, P. (author). Global Interpretation of Image Classification Models via SEmantic Feature Analysis (SEFA).

Degree: 2020, Delft University of Technology

Deep learning models have achieved state-of-the-art performance on several image classification tasks over the past years. Several studies claim to approach or even surpass human-levels… (more)

Subjects/Keywords: Global Interpretability; Deep Learning; Image Classification

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APA (6th Edition):

Soilis, P. (. (2020). Global Interpretation of Image Classification Models via SEmantic Feature Analysis (SEFA). (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:6925ccc5-55b6-4f0f-b1c8-57a227b3e25f

Chicago Manual of Style (16th Edition):

Soilis, P (author). “Global Interpretation of Image Classification Models via SEmantic Feature Analysis (SEFA).” 2020. Masters Thesis, Delft University of Technology. Accessed September 26, 2020. http://resolver.tudelft.nl/uuid:6925ccc5-55b6-4f0f-b1c8-57a227b3e25f.

MLA Handbook (7th Edition):

Soilis, P (author). “Global Interpretation of Image Classification Models via SEmantic Feature Analysis (SEFA).” 2020. Web. 26 Sep 2020.

Vancouver:

Soilis P(. Global Interpretation of Image Classification Models via SEmantic Feature Analysis (SEFA). [Internet] [Masters thesis]. Delft University of Technology; 2020. [cited 2020 Sep 26]. Available from: http://resolver.tudelft.nl/uuid:6925ccc5-55b6-4f0f-b1c8-57a227b3e25f.

Council of Science Editors:

Soilis P(. Global Interpretation of Image Classification Models via SEmantic Feature Analysis (SEFA). [Masters Thesis]. Delft University of Technology; 2020. Available from: http://resolver.tudelft.nl/uuid:6925ccc5-55b6-4f0f-b1c8-57a227b3e25f


University of Missouri – Columbia

14. Kanawong, Ratchadaporn. Computer-aided tongue image diagnosis and analysis.

Degree: 2012, University of Missouri – Columbia

 This work focuses on computer-aided tongue image analysis, specifically, as it relates to Traditional Chinese Medicine (TCM). Computerized tongue diagnosis aid medical practitioners capture quantitative… (more)

Subjects/Keywords: diagnostic imaging; image segmentation algorithm; ZHENG classification

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APA (6th Edition):

Kanawong, R. (2012). Computer-aided tongue image diagnosis and analysis. (Thesis). University of Missouri – Columbia. Retrieved from http://hdl.handle.net/10355/35187

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

Chicago Manual of Style (16th Edition):

Kanawong, Ratchadaporn. “Computer-aided tongue image diagnosis and analysis.” 2012. Thesis, University of Missouri – Columbia. Accessed September 26, 2020. http://hdl.handle.net/10355/35187.

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

MLA Handbook (7th Edition):

Kanawong, Ratchadaporn. “Computer-aided tongue image diagnosis and analysis.” 2012. Web. 26 Sep 2020.

Vancouver:

Kanawong R. Computer-aided tongue image diagnosis and analysis. [Internet] [Thesis]. University of Missouri – Columbia; 2012. [cited 2020 Sep 26]. Available from: http://hdl.handle.net/10355/35187.

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

Council of Science Editors:

Kanawong R. Computer-aided tongue image diagnosis and analysis. [Thesis]. University of Missouri – Columbia; 2012. Available from: http://hdl.handle.net/10355/35187

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

15. Nguyen, Vu Lam. Approches complémentaires pour une classification efficace des textures : Complementary Approaches for Efficient Texture Classification.

Degree: Docteur es, STIC (Sciences et Technologies de l'Information et de la Communication) - ED EM2PSI, 2018, Cergy-Pontoise

 Dans cette thèse, nous nous intéressons à la classification des images de textures avec aucune connaissance a priori sur les conditions de numérisation. Cette classification(more)

Subjects/Keywords: Image classification; Texture classification; Représentation; Normalized-Convolution; Feature; Image classification; Feature; Descriptor; Normalized-Convolution; Texture classification

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

APA (6th Edition):

Nguyen, V. L. (2018). Approches complémentaires pour une classification efficace des textures : Complementary Approaches for Efficient Texture Classification. (Doctoral Dissertation). Cergy-Pontoise. Retrieved from http://www.theses.fr/2018CERG0974

Chicago Manual of Style (16th Edition):

Nguyen, Vu Lam. “Approches complémentaires pour une classification efficace des textures : Complementary Approaches for Efficient Texture Classification.” 2018. Doctoral Dissertation, Cergy-Pontoise. Accessed September 26, 2020. http://www.theses.fr/2018CERG0974.

MLA Handbook (7th Edition):

Nguyen, Vu Lam. “Approches complémentaires pour une classification efficace des textures : Complementary Approaches for Efficient Texture Classification.” 2018. Web. 26 Sep 2020.

Vancouver:

Nguyen VL. Approches complémentaires pour une classification efficace des textures : Complementary Approaches for Efficient Texture Classification. [Internet] [Doctoral dissertation]. Cergy-Pontoise; 2018. [cited 2020 Sep 26]. Available from: http://www.theses.fr/2018CERG0974.

Council of Science Editors:

Nguyen VL. Approches complémentaires pour une classification efficace des textures : Complementary Approaches for Efficient Texture Classification. [Doctoral Dissertation]. Cergy-Pontoise; 2018. Available from: http://www.theses.fr/2018CERG0974

16. GONG TIANXIA. Automatic Annotation, Classification and Retrieval of Traumatic Brain Injury CT Images.

Degree: 2011, National University of Singapore

Subjects/Keywords: image annotation; image classification; image retrieval

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

APA (6th Edition):

TIANXIA, G. (2011). Automatic Annotation, Classification and Retrieval of Traumatic Brain Injury CT Images. (Thesis). National University of Singapore. Retrieved from http://scholarbank.nus.edu.sg/handle/10635/30296

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

Chicago Manual of Style (16th Edition):

TIANXIA, GONG. “Automatic Annotation, Classification and Retrieval of Traumatic Brain Injury CT Images.” 2011. Thesis, National University of Singapore. Accessed September 26, 2020. http://scholarbank.nus.edu.sg/handle/10635/30296.

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

MLA Handbook (7th Edition):

TIANXIA, GONG. “Automatic Annotation, Classification and Retrieval of Traumatic Brain Injury CT Images.” 2011. Web. 26 Sep 2020.

Vancouver:

TIANXIA G. Automatic Annotation, Classification and Retrieval of Traumatic Brain Injury CT Images. [Internet] [Thesis]. National University of Singapore; 2011. [cited 2020 Sep 26]. Available from: http://scholarbank.nus.edu.sg/handle/10635/30296.

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

Council of Science Editors:

TIANXIA G. Automatic Annotation, Classification and Retrieval of Traumatic Brain Injury CT Images. [Thesis]. National University of Singapore; 2011. Available from: http://scholarbank.nus.edu.sg/handle/10635/30296

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


University of Sydney

17. Zhang, Fan. Content-based Medical Image Classification and Retrieval .

Degree: 2016, University of Sydney

 Content-based medical image classification and retrieval are the tasks finding medical images, e.g., of the same category of healthy or abnormal organs, for disease diagnosis… (more)

Subjects/Keywords: Content-based Medical Image Analysis; Medical Image Classification and Retrieval

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

APA (6th Edition):

Zhang, F. (2016). Content-based Medical Image Classification and Retrieval . (Thesis). University of Sydney. Retrieved from http://hdl.handle.net/2123/15531

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

Chicago Manual of Style (16th Edition):

Zhang, Fan. “Content-based Medical Image Classification and Retrieval .” 2016. Thesis, University of Sydney. Accessed September 26, 2020. http://hdl.handle.net/2123/15531.

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

MLA Handbook (7th Edition):

Zhang, Fan. “Content-based Medical Image Classification and Retrieval .” 2016. Web. 26 Sep 2020.

Vancouver:

Zhang F. Content-based Medical Image Classification and Retrieval . [Internet] [Thesis]. University of Sydney; 2016. [cited 2020 Sep 26]. Available from: http://hdl.handle.net/2123/15531.

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

Council of Science Editors:

Zhang F. Content-based Medical Image Classification and Retrieval . [Thesis]. University of Sydney; 2016. Available from: http://hdl.handle.net/2123/15531

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


Penn State University

18. Srinivas, Umamahesh. Discriminative models for robust image classification.

Degree: 2013, Penn State University

 A variety of real-world tasks involve the classification of images into pre-determined categories. Designing image classification algorithms that exhibit robustness to acquisition noise and image(more)

Subjects/Keywords: Robust image classification; image processing; graphical models; sparse signal representations.

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

APA (6th Edition):

Srinivas, U. (2013). Discriminative models for robust image classification. (Thesis). Penn State University. Retrieved from https://submit-etda.libraries.psu.edu/catalog/19014

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

Chicago Manual of Style (16th Edition):

Srinivas, Umamahesh. “Discriminative models for robust image classification.” 2013. Thesis, Penn State University. Accessed September 26, 2020. https://submit-etda.libraries.psu.edu/catalog/19014.

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

MLA Handbook (7th Edition):

Srinivas, Umamahesh. “Discriminative models for robust image classification.” 2013. Web. 26 Sep 2020.

Vancouver:

Srinivas U. Discriminative models for robust image classification. [Internet] [Thesis]. Penn State University; 2013. [cited 2020 Sep 26]. Available from: https://submit-etda.libraries.psu.edu/catalog/19014.

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

Council of Science Editors:

Srinivas U. Discriminative models for robust image classification. [Thesis]. Penn State University; 2013. Available from: https://submit-etda.libraries.psu.edu/catalog/19014

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

19. Kalra, Shivam. Content-based Image Retrieval of Gigapixel Histopathology Scans: A Comparative Study of Convolution Neural Network, Local Binary Pattern, and Bag of visual Words.

Degree: 2018, University of Waterloo

 The state-of-the-art image analysis algorithms offer a unique opportunity to extract semantically meaningful features from medical images. The advantage of this approach is automation in… (more)

Subjects/Keywords: Content-based Image Retrieval; Digital Pathology; Deep Learning; Image Classification

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

APA (6th Edition):

Kalra, S. (2018). Content-based Image Retrieval of Gigapixel Histopathology Scans: A Comparative Study of Convolution Neural Network, Local Binary Pattern, and Bag of visual Words. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/13226

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

Chicago Manual of Style (16th Edition):

Kalra, Shivam. “Content-based Image Retrieval of Gigapixel Histopathology Scans: A Comparative Study of Convolution Neural Network, Local Binary Pattern, and Bag of visual Words.” 2018. Thesis, University of Waterloo. Accessed September 26, 2020. http://hdl.handle.net/10012/13226.

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

MLA Handbook (7th Edition):

Kalra, Shivam. “Content-based Image Retrieval of Gigapixel Histopathology Scans: A Comparative Study of Convolution Neural Network, Local Binary Pattern, and Bag of visual Words.” 2018. Web. 26 Sep 2020.

Vancouver:

Kalra S. Content-based Image Retrieval of Gigapixel Histopathology Scans: A Comparative Study of Convolution Neural Network, Local Binary Pattern, and Bag of visual Words. [Internet] [Thesis]. University of Waterloo; 2018. [cited 2020 Sep 26]. Available from: http://hdl.handle.net/10012/13226.

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

Council of Science Editors:

Kalra S. Content-based Image Retrieval of Gigapixel Histopathology Scans: A Comparative Study of Convolution Neural Network, Local Binary Pattern, and Bag of visual Words. [Thesis]. University of Waterloo; 2018. Available from: http://hdl.handle.net/10012/13226

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


Texas State University – San Marcos

20. Ring, Patrick D. Movement Classification and Analysis from RGB – D Video Data.

Degree: MS, Computer Science, 2019, Texas State University – San Marcos

 The aim of this thesis is to develop and evaluate methods of human movement classification using motion tracking data captured using a RGB-D sensor. As… (more)

Subjects/Keywords: Kinect; Classification; Computer science; Image processing – Digital techniques; Image analysis

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

APA (6th Edition):

Ring, P. D. (2019). Movement Classification and Analysis from RGB – D Video Data. (Masters Thesis). Texas State University – San Marcos. Retrieved from https://digital.library.txstate.edu/handle/10877/8325

Chicago Manual of Style (16th Edition):

Ring, Patrick D. “Movement Classification and Analysis from RGB – D Video Data.” 2019. Masters Thesis, Texas State University – San Marcos. Accessed September 26, 2020. https://digital.library.txstate.edu/handle/10877/8325.

MLA Handbook (7th Edition):

Ring, Patrick D. “Movement Classification and Analysis from RGB – D Video Data.” 2019. Web. 26 Sep 2020.

Vancouver:

Ring PD. Movement Classification and Analysis from RGB – D Video Data. [Internet] [Masters thesis]. Texas State University – San Marcos; 2019. [cited 2020 Sep 26]. Available from: https://digital.library.txstate.edu/handle/10877/8325.

Council of Science Editors:

Ring PD. Movement Classification and Analysis from RGB – D Video Data. [Masters Thesis]. Texas State University – San Marcos; 2019. Available from: https://digital.library.txstate.edu/handle/10877/8325


Brunel University

21. Kounalakis, Tsampikos. Depth-adaptive methodologies for 3D image caregorization.

Degree: PhD, 2015, Brunel University

Image classification is an active topic of computer vision research. This topic deals with the learning of patterns in order to allow efficient classification of… (more)

Subjects/Keywords: 006.3; 3D imaging; Image classification; Object recognition; Image representation; Deep learning

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

APA (6th Edition):

Kounalakis, T. (2015). Depth-adaptive methodologies for 3D image caregorization. (Doctoral Dissertation). Brunel University. Retrieved from http://bura.brunel.ac.uk/handle/2438/11531 ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.669124

Chicago Manual of Style (16th Edition):

Kounalakis, Tsampikos. “Depth-adaptive methodologies for 3D image caregorization.” 2015. Doctoral Dissertation, Brunel University. Accessed September 26, 2020. http://bura.brunel.ac.uk/handle/2438/11531 ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.669124.

MLA Handbook (7th Edition):

Kounalakis, Tsampikos. “Depth-adaptive methodologies for 3D image caregorization.” 2015. Web. 26 Sep 2020.

Vancouver:

Kounalakis T. Depth-adaptive methodologies for 3D image caregorization. [Internet] [Doctoral dissertation]. Brunel University; 2015. [cited 2020 Sep 26]. Available from: http://bura.brunel.ac.uk/handle/2438/11531 ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.669124.

Council of Science Editors:

Kounalakis T. Depth-adaptive methodologies for 3D image caregorization. [Doctoral Dissertation]. Brunel University; 2015. Available from: http://bura.brunel.ac.uk/handle/2438/11531 ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.669124


University of Sydney

22. Ahn, Euijoon. Sparse Coding for Medical Image Analysis: Applications to Image Segmentation and Classification .

Degree: 2016, University of Sydney

 Medical imaging is a fundamental and invaluable tool in modern healthcare. The use of medical imaging has greatly increased, and these massive image archives provide… (more)

Subjects/Keywords: Sparsing Coding; Saliency Detection; Convolutional Neural Networks; Image Segmentation; Image Classification

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

APA (6th Edition):

Ahn, E. (2016). Sparse Coding for Medical Image Analysis: Applications to Image Segmentation and Classification . (Thesis). University of Sydney. Retrieved from http://hdl.handle.net/2123/14971

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

Chicago Manual of Style (16th Edition):

Ahn, Euijoon. “Sparse Coding for Medical Image Analysis: Applications to Image Segmentation and Classification .” 2016. Thesis, University of Sydney. Accessed September 26, 2020. http://hdl.handle.net/2123/14971.

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

MLA Handbook (7th Edition):

Ahn, Euijoon. “Sparse Coding for Medical Image Analysis: Applications to Image Segmentation and Classification .” 2016. Web. 26 Sep 2020.

Vancouver:

Ahn E. Sparse Coding for Medical Image Analysis: Applications to Image Segmentation and Classification . [Internet] [Thesis]. University of Sydney; 2016. [cited 2020 Sep 26]. Available from: http://hdl.handle.net/2123/14971.

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

Council of Science Editors:

Ahn E. Sparse Coding for Medical Image Analysis: Applications to Image Segmentation and Classification . [Thesis]. University of Sydney; 2016. Available from: http://hdl.handle.net/2123/14971

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


Halmstad University

23. Hameed, Tariq; Ashfaq, Ahsan. Intelligent Sensor.

Degree: Computer and Electrical Engineering (IDE), 2012, Halmstad University

  The task is to build an intelligent sensor that can instruct a Lego robot to perform certain tasks. The sensor is mounted on the… (more)

Subjects/Keywords: Intelligent sensor; FPGA; image processing; color image segmentation; classification; histogram

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

APA (6th Edition):

Hameed, Tariq; Ashfaq, A. (2012). Intelligent Sensor. (Thesis). Halmstad University. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-17310

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

Chicago Manual of Style (16th Edition):

Hameed, Tariq; Ashfaq, Ahsan. “Intelligent Sensor.” 2012. Thesis, Halmstad University. Accessed September 26, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-17310.

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

MLA Handbook (7th Edition):

Hameed, Tariq; Ashfaq, Ahsan. “Intelligent Sensor.” 2012. Web. 26 Sep 2020.

Vancouver:

Hameed, Tariq; Ashfaq A. Intelligent Sensor. [Internet] [Thesis]. Halmstad University; 2012. [cited 2020 Sep 26]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-17310.

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

Council of Science Editors:

Hameed, Tariq; Ashfaq A. Intelligent Sensor. [Thesis]. Halmstad University; 2012. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-17310

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

24. Karmakar, Priyabrata. Effective and efficient kernel-based image representations for classification and retrieval.

Degree: PhD, 2018, Federation University Australia

Image representation is a challenging task. In particular, in order to obtain better performances in different image processing applications such as video surveillance, autonomous driving,… (more)

Subjects/Keywords: Image representation methods; Kernal-based image representations; Classification; Retrieval

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APA (6th Edition):

Karmakar, P. (2018). Effective and efficient kernel-based image representations for classification and retrieval. (Doctoral Dissertation). Federation University Australia. Retrieved from http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/165515 ; https://library.federation.edu.au/record=b2790194

Chicago Manual of Style (16th Edition):

Karmakar, Priyabrata. “Effective and efficient kernel-based image representations for classification and retrieval.” 2018. Doctoral Dissertation, Federation University Australia. Accessed September 26, 2020. http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/165515 ; https://library.federation.edu.au/record=b2790194.

MLA Handbook (7th Edition):

Karmakar, Priyabrata. “Effective and efficient kernel-based image representations for classification and retrieval.” 2018. Web. 26 Sep 2020.

Vancouver:

Karmakar P. Effective and efficient kernel-based image representations for classification and retrieval. [Internet] [Doctoral dissertation]. Federation University Australia; 2018. [cited 2020 Sep 26]. Available from: http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/165515 ; https://library.federation.edu.au/record=b2790194.

Council of Science Editors:

Karmakar P. Effective and efficient kernel-based image representations for classification and retrieval. [Doctoral Dissertation]. Federation University Australia; 2018. Available from: http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/165515 ; https://library.federation.edu.au/record=b2790194


Kansas State University

25. Ghimire, Santosh. Classification of image pixels based on minimum distance and hypothesis testing.

Degree: MS, Department of Statistics, 2011, Kansas State University

 We introduce a new classification method that is applicable to classify image pixels. This work was motivated by the test-based classification (TBC) introduced by Liao… (more)

Subjects/Keywords: Hypothesis testing; minimum distance; image processing; image classification; Statistics (0463)

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

APA (6th Edition):

Ghimire, S. (2011). Classification of image pixels based on minimum distance and hypothesis testing. (Masters Thesis). Kansas State University. Retrieved from http://hdl.handle.net/2097/8547

Chicago Manual of Style (16th Edition):

Ghimire, Santosh. “Classification of image pixels based on minimum distance and hypothesis testing.” 2011. Masters Thesis, Kansas State University. Accessed September 26, 2020. http://hdl.handle.net/2097/8547.

MLA Handbook (7th Edition):

Ghimire, Santosh. “Classification of image pixels based on minimum distance and hypothesis testing.” 2011. Web. 26 Sep 2020.

Vancouver:

Ghimire S. Classification of image pixels based on minimum distance and hypothesis testing. [Internet] [Masters thesis]. Kansas State University; 2011. [cited 2020 Sep 26]. Available from: http://hdl.handle.net/2097/8547.

Council of Science Editors:

Ghimire S. Classification of image pixels based on minimum distance and hypothesis testing. [Masters Thesis]. Kansas State University; 2011. Available from: http://hdl.handle.net/2097/8547


University of North Texas

26. Kumara, Muthukudage Jayantha. Automated Real-time Objects Detection in Colonoscopy Videos for Quality Measurements.

Degree: 2013, University of North Texas

 The effectiveness of colonoscopy depends on the quality of the inspection of the colon. There was no automated measurement method to evaluate the quality of… (more)

Subjects/Keywords: Image classification; convex hull; objects detection; medical image analysis

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APA (6th Edition):

Kumara, M. J. (2013). Automated Real-time Objects Detection in Colonoscopy Videos for Quality Measurements. (Thesis). University of North Texas. Retrieved from https://digital.library.unt.edu/ark:/67531/metadc283843/

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

Chicago Manual of Style (16th Edition):

Kumara, Muthukudage Jayantha. “Automated Real-time Objects Detection in Colonoscopy Videos for Quality Measurements.” 2013. Thesis, University of North Texas. Accessed September 26, 2020. https://digital.library.unt.edu/ark:/67531/metadc283843/.

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

MLA Handbook (7th Edition):

Kumara, Muthukudage Jayantha. “Automated Real-time Objects Detection in Colonoscopy Videos for Quality Measurements.” 2013. Web. 26 Sep 2020.

Vancouver:

Kumara MJ. Automated Real-time Objects Detection in Colonoscopy Videos for Quality Measurements. [Internet] [Thesis]. University of North Texas; 2013. [cited 2020 Sep 26]. Available from: https://digital.library.unt.edu/ark:/67531/metadc283843/.

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

Council of Science Editors:

Kumara MJ. Automated Real-time Objects Detection in Colonoscopy Videos for Quality Measurements. [Thesis]. University of North Texas; 2013. Available from: https://digital.library.unt.edu/ark:/67531/metadc283843/

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


University of Georgia

27. Ashish, Dev. Land-use classification of aerial images using artificial neural networks.

Degree: 2014, University of Georgia

 This thesis describes the study of Artificial Neural Network (ANN) based techniques for the classification of aerial images for various types of land-use. In this… (more)

Subjects/Keywords: Aerial remote sensing; artificial neural networks; image classification; image processing.

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APA (6th Edition):

Ashish, D. (2014). Land-use classification of aerial images using artificial neural networks. (Thesis). University of Georgia. Retrieved from http://hdl.handle.net/10724/29481

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

Chicago Manual of Style (16th Edition):

Ashish, Dev. “Land-use classification of aerial images using artificial neural networks.” 2014. Thesis, University of Georgia. Accessed September 26, 2020. http://hdl.handle.net/10724/29481.

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

MLA Handbook (7th Edition):

Ashish, Dev. “Land-use classification of aerial images using artificial neural networks.” 2014. Web. 26 Sep 2020.

Vancouver:

Ashish D. Land-use classification of aerial images using artificial neural networks. [Internet] [Thesis]. University of Georgia; 2014. [cited 2020 Sep 26]. Available from: http://hdl.handle.net/10724/29481.

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

Council of Science Editors:

Ashish D. Land-use classification of aerial images using artificial neural networks. [Thesis]. University of Georgia; 2014. Available from: http://hdl.handle.net/10724/29481

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

28. Bannour, Hichem. Building and Using Knowledge Models for Semantic Image Annotation : Construction et utilisation de modèles à base de connaissance pour l’annotation sémantique des images.

Degree: Docteur es, Informatique, 2013, Châtenay-Malabry, Ecole centrale de Paris

Cette thèse propose une nouvelle méthodologie pour la construction et l’utilisation de modèles à base de connaissances pour l'annotation automatique d'images. Plus précisément, nous proposons… (more)

Subjects/Keywords: Annotation d'images; Classification hiérarchique d'images; Ontologies multimédia; Image Annotation; Hierarchical Image Classification; Multimedia Ontologies

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

APA (6th Edition):

Bannour, H. (2013). Building and Using Knowledge Models for Semantic Image Annotation : Construction et utilisation de modèles à base de connaissance pour l’annotation sémantique des images. (Doctoral Dissertation). Châtenay-Malabry, Ecole centrale de Paris. Retrieved from http://www.theses.fr/2013ECAP0027

Chicago Manual of Style (16th Edition):

Bannour, Hichem. “Building and Using Knowledge Models for Semantic Image Annotation : Construction et utilisation de modèles à base de connaissance pour l’annotation sémantique des images.” 2013. Doctoral Dissertation, Châtenay-Malabry, Ecole centrale de Paris. Accessed September 26, 2020. http://www.theses.fr/2013ECAP0027.

MLA Handbook (7th Edition):

Bannour, Hichem. “Building and Using Knowledge Models for Semantic Image Annotation : Construction et utilisation de modèles à base de connaissance pour l’annotation sémantique des images.” 2013. Web. 26 Sep 2020.

Vancouver:

Bannour H. Building and Using Knowledge Models for Semantic Image Annotation : Construction et utilisation de modèles à base de connaissance pour l’annotation sémantique des images. [Internet] [Doctoral dissertation]. Châtenay-Malabry, Ecole centrale de Paris; 2013. [cited 2020 Sep 26]. Available from: http://www.theses.fr/2013ECAP0027.

Council of Science Editors:

Bannour H. Building and Using Knowledge Models for Semantic Image Annotation : Construction et utilisation de modèles à base de connaissance pour l’annotation sémantique des images. [Doctoral Dissertation]. Châtenay-Malabry, Ecole centrale de Paris; 2013. Available from: http://www.theses.fr/2013ECAP0027

29. Znaidia, Amel. Handling imperfections for multimodal image annotation : Gestion des imperfections pour l’annotation multimodale d’images.

Degree: Docteur es, Computer science, 2014, Châtenay-Malabry, Ecole centrale de Paris

La présente thèse s’intéresse à l’annotation multimodale d’images dans le contexte des médias sociaux. Notre objectif est de combiner les modalités visuelles et textuelles (tags)… (more)

Subjects/Keywords: Annotation multimodale d’images; Classification supervisée d’images; Imperfections; Multimodal image annotation; Supervised image classification; Tag imperfections

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APA (6th Edition):

Znaidia, A. (2014). Handling imperfections for multimodal image annotation : Gestion des imperfections pour l’annotation multimodale d’images. (Doctoral Dissertation). Châtenay-Malabry, Ecole centrale de Paris. Retrieved from http://www.theses.fr/2014ECAP0017

Chicago Manual of Style (16th Edition):

Znaidia, Amel. “Handling imperfections for multimodal image annotation : Gestion des imperfections pour l’annotation multimodale d’images.” 2014. Doctoral Dissertation, Châtenay-Malabry, Ecole centrale de Paris. Accessed September 26, 2020. http://www.theses.fr/2014ECAP0017.

MLA Handbook (7th Edition):

Znaidia, Amel. “Handling imperfections for multimodal image annotation : Gestion des imperfections pour l’annotation multimodale d’images.” 2014. Web. 26 Sep 2020.

Vancouver:

Znaidia A. Handling imperfections for multimodal image annotation : Gestion des imperfections pour l’annotation multimodale d’images. [Internet] [Doctoral dissertation]. Châtenay-Malabry, Ecole centrale de Paris; 2014. [cited 2020 Sep 26]. Available from: http://www.theses.fr/2014ECAP0017.

Council of Science Editors:

Znaidia A. Handling imperfections for multimodal image annotation : Gestion des imperfections pour l’annotation multimodale d’images. [Doctoral Dissertation]. Châtenay-Malabry, Ecole centrale de Paris; 2014. Available from: http://www.theses.fr/2014ECAP0017


University of Guelph

30. Khan, Omar Irfan. A Hybrid Unsupervised Classification Technique for Automatic Brain MRI Tumor Recognition.

Degree: MS, School of Computer Science, 2020, University of Guelph

 Cancer is a prevalent disease with a rising incidence worldwide. The most common misdiagnosed type of cancers are brain tumors. Patients in early stages of… (more)

Subjects/Keywords: Brain tumor, MRI, clustering, Medical; Image segmentation, image classification, unsupervised learning; automatic classification

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

APA (6th Edition):

Khan, O. I. (2020). A Hybrid Unsupervised Classification Technique for Automatic Brain MRI Tumor Recognition. (Masters Thesis). University of Guelph. Retrieved from https://atrium.lib.uoguelph.ca/xmlui/handle/10214/17779

Chicago Manual of Style (16th Edition):

Khan, Omar Irfan. “A Hybrid Unsupervised Classification Technique for Automatic Brain MRI Tumor Recognition.” 2020. Masters Thesis, University of Guelph. Accessed September 26, 2020. https://atrium.lib.uoguelph.ca/xmlui/handle/10214/17779.

MLA Handbook (7th Edition):

Khan, Omar Irfan. “A Hybrid Unsupervised Classification Technique for Automatic Brain MRI Tumor Recognition.” 2020. Web. 26 Sep 2020.

Vancouver:

Khan OI. A Hybrid Unsupervised Classification Technique for Automatic Brain MRI Tumor Recognition. [Internet] [Masters thesis]. University of Guelph; 2020. [cited 2020 Sep 26]. Available from: https://atrium.lib.uoguelph.ca/xmlui/handle/10214/17779.

Council of Science Editors:

Khan OI. A Hybrid Unsupervised Classification Technique for Automatic Brain MRI Tumor Recognition. [Masters Thesis]. University of Guelph; 2020. Available from: https://atrium.lib.uoguelph.ca/xmlui/handle/10214/17779

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