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You searched for subject:(image feature extraction). Showing records 1 – 30 of 129 total matches.

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Delft University of Technology

1. Roumi, M. Implementing Texture Feature Extraction Algorithms on FPGA:.

Degree: 2009, Delft University of Technology

Feature extraction is a key function in various image processing applications. A feature is an image characteristic that can capture certain visual property of the… (more)

Subjects/Keywords: image processing; feature extraction; texture; FPGA

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

APA (6th Edition):

Roumi, M. (2009). Implementing Texture Feature Extraction Algorithms on FPGA:. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:5f7ba046-3a5c-46bb-8368-896a88f5e4a7

Chicago Manual of Style (16th Edition):

Roumi, M. “Implementing Texture Feature Extraction Algorithms on FPGA:.” 2009. Masters Thesis, Delft University of Technology. Accessed February 17, 2020. http://resolver.tudelft.nl/uuid:5f7ba046-3a5c-46bb-8368-896a88f5e4a7.

MLA Handbook (7th Edition):

Roumi, M. “Implementing Texture Feature Extraction Algorithms on FPGA:.” 2009. Web. 17 Feb 2020.

Vancouver:

Roumi M. Implementing Texture Feature Extraction Algorithms on FPGA:. [Internet] [Masters thesis]. Delft University of Technology; 2009. [cited 2020 Feb 17]. Available from: http://resolver.tudelft.nl/uuid:5f7ba046-3a5c-46bb-8368-896a88f5e4a7.

Council of Science Editors:

Roumi M. Implementing Texture Feature Extraction Algorithms on FPGA:. [Masters Thesis]. Delft University of Technology; 2009. Available from: http://resolver.tudelft.nl/uuid:5f7ba046-3a5c-46bb-8368-896a88f5e4a7


NSYSU

2. 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 February 17, 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. 17 Feb 2020.

Vancouver:

Yang C. Image classification via successive core tensor selection procedure. [Internet] [Thesis]. NSYSU; 2018. [cited 2020 Feb 17]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0606118-151922.

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

Council of Science Editors:

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

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


University of New South Wales

3. Hossain, Md Ali. Subspace Detection Approaches for Hyperspectral Image Classification.

Degree: Engineering & Information Technology Canberra, 2014, University of New South Wales

 Hyperspectral data provides rich information and is very useful for a range of applications from ground-cover types identification to target detection. With many benefits they… (more)

Subjects/Keywords: Feature selection; Image classification; Feature extraction; Mutual Information; Hyperspectral image; Feature reduction

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APA (6th Edition):

Hossain, M. A. (2014). Subspace Detection Approaches for Hyperspectral Image Classification. (Doctoral Dissertation). University of New South Wales. Retrieved from http://handle.unsw.edu.au/1959.4/53507 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:12202/SOURCE02?view=true

Chicago Manual of Style (16th Edition):

Hossain, Md Ali. “Subspace Detection Approaches for Hyperspectral Image Classification.” 2014. Doctoral Dissertation, University of New South Wales. Accessed February 17, 2020. http://handle.unsw.edu.au/1959.4/53507 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:12202/SOURCE02?view=true.

MLA Handbook (7th Edition):

Hossain, Md Ali. “Subspace Detection Approaches for Hyperspectral Image Classification.” 2014. Web. 17 Feb 2020.

Vancouver:

Hossain MA. Subspace Detection Approaches for Hyperspectral Image Classification. [Internet] [Doctoral dissertation]. University of New South Wales; 2014. [cited 2020 Feb 17]. Available from: http://handle.unsw.edu.au/1959.4/53507 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:12202/SOURCE02?view=true.

Council of Science Editors:

Hossain MA. Subspace Detection Approaches for Hyperspectral Image Classification. [Doctoral Dissertation]. University of New South Wales; 2014. Available from: http://handle.unsw.edu.au/1959.4/53507 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:12202/SOURCE02?view=true


Virginia Tech

4. Shen, Yuan. Feature Extraction and Feasibility Study on CT Image Guided Colonoscopy.

Degree: MS, Electrical and Computer Engineering, 2010, Virginia Tech

 Computed tomographic colonography(CTC), also called virtual colonoscopy, uses CT scanning and computer post-processing to create two dimensional images and three dimensional virtual views inside of… (more)

Subjects/Keywords: Feature Extraction; Image Guided Colonoscopy; SLAM; Video Feature Tracking

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APA (6th Edition):

Shen, Y. (2010). Feature Extraction and Feasibility Study on CT Image Guided Colonoscopy. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/32275

Chicago Manual of Style (16th Edition):

Shen, Yuan. “Feature Extraction and Feasibility Study on CT Image Guided Colonoscopy.” 2010. Masters Thesis, Virginia Tech. Accessed February 17, 2020. http://hdl.handle.net/10919/32275.

MLA Handbook (7th Edition):

Shen, Yuan. “Feature Extraction and Feasibility Study on CT Image Guided Colonoscopy.” 2010. Web. 17 Feb 2020.

Vancouver:

Shen Y. Feature Extraction and Feasibility Study on CT Image Guided Colonoscopy. [Internet] [Masters thesis]. Virginia Tech; 2010. [cited 2020 Feb 17]. Available from: http://hdl.handle.net/10919/32275.

Council of Science Editors:

Shen Y. Feature Extraction and Feasibility Study on CT Image Guided Colonoscopy. [Masters Thesis]. Virginia Tech; 2010. Available from: http://hdl.handle.net/10919/32275


NSYSU

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

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

 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 (6th 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 (16th Edition):

Wu, Bo-sheng. “Acceleration of Image Feature Extraction Algorithms.” 2014. Thesis, NSYSU. Accessed February 17, 2020. 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 (7th Edition):

Wu, Bo-sheng. “Acceleration of Image Feature Extraction Algorithms.” 2014. Web. 17 Feb 2020.

Vancouver:

Wu B. Acceleration of Image Feature Extraction Algorithms. [Internet] [Thesis]. NSYSU; 2014. [cited 2020 Feb 17]. 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

6. Oguslu, Ender. Sparse Coding Based Feature Representation Method for Remote Sensing Images.

Degree: PhD, Electrical/Computer Engineering, 2016, Old Dominion University

  In this dissertation, we study sparse coding based feature representation method for the classification of multispectral and hyperspectral images (HSI). The existing feature representation… (more)

Subjects/Keywords: Feature extraction; Feature representation; Hyperspectral image; Image classification; Multispectral image; Sparse coding; Electrical and Computer Engineering; Remote Sensing

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APA (6th Edition):

Oguslu, E. (2016). Sparse Coding Based Feature Representation Method for Remote Sensing Images. (Doctoral Dissertation). Old Dominion University. Retrieved from 9781339893099 ; https://digitalcommons.odu.edu/ece_etds/4

Chicago Manual of Style (16th Edition):

Oguslu, Ender. “Sparse Coding Based Feature Representation Method for Remote Sensing Images.” 2016. Doctoral Dissertation, Old Dominion University. Accessed February 17, 2020. 9781339893099 ; https://digitalcommons.odu.edu/ece_etds/4.

MLA Handbook (7th Edition):

Oguslu, Ender. “Sparse Coding Based Feature Representation Method for Remote Sensing Images.” 2016. Web. 17 Feb 2020.

Vancouver:

Oguslu E. Sparse Coding Based Feature Representation Method for Remote Sensing Images. [Internet] [Doctoral dissertation]. Old Dominion University; 2016. [cited 2020 Feb 17]. Available from: 9781339893099 ; https://digitalcommons.odu.edu/ece_etds/4.

Council of Science Editors:

Oguslu E. Sparse Coding Based Feature Representation Method for Remote Sensing Images. [Doctoral Dissertation]. Old Dominion University; 2016. Available from: 9781339893099 ; https://digitalcommons.odu.edu/ece_etds/4

7. Cremer, Sandra. Adapting iris feature extraction and matching to the local and global quality of iris image : Comparaison des personnes par l'iris : adaptation des étapes d'extraction de caractéristiques et de comparaison à la qualité locale et globale des images d'entrées.

Degree: Docteur es, Informatique, 2012, Evry, Institut national des télécommunications

La reconnaissance d'iris est un des systèmes biométriques les plus fiables et les plus précis. Cependant sa robustesse aux dégradations des images d'entrées est limitée.… (more)

Subjects/Keywords: Iris; Extraction de caractéristiques; Comparaison; Qualité d'image; Biométrique; Iris; Feature extraction; Matching; Image quality; Biometrics

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APA (6th Edition):

Cremer, S. (2012). Adapting iris feature extraction and matching to the local and global quality of iris image : Comparaison des personnes par l'iris : adaptation des étapes d'extraction de caractéristiques et de comparaison à la qualité locale et globale des images d'entrées. (Doctoral Dissertation). Evry, Institut national des télécommunications. Retrieved from http://www.theses.fr/2012TELE0026

Chicago Manual of Style (16th Edition):

Cremer, Sandra. “Adapting iris feature extraction and matching to the local and global quality of iris image : Comparaison des personnes par l'iris : adaptation des étapes d'extraction de caractéristiques et de comparaison à la qualité locale et globale des images d'entrées.” 2012. Doctoral Dissertation, Evry, Institut national des télécommunications. Accessed February 17, 2020. http://www.theses.fr/2012TELE0026.

MLA Handbook (7th Edition):

Cremer, Sandra. “Adapting iris feature extraction and matching to the local and global quality of iris image : Comparaison des personnes par l'iris : adaptation des étapes d'extraction de caractéristiques et de comparaison à la qualité locale et globale des images d'entrées.” 2012. Web. 17 Feb 2020.

Vancouver:

Cremer S. Adapting iris feature extraction and matching to the local and global quality of iris image : Comparaison des personnes par l'iris : adaptation des étapes d'extraction de caractéristiques et de comparaison à la qualité locale et globale des images d'entrées. [Internet] [Doctoral dissertation]. Evry, Institut national des télécommunications; 2012. [cited 2020 Feb 17]. Available from: http://www.theses.fr/2012TELE0026.

Council of Science Editors:

Cremer S. Adapting iris feature extraction and matching to the local and global quality of iris image : Comparaison des personnes par l'iris : adaptation des étapes d'extraction de caractéristiques et de comparaison à la qualité locale et globale des images d'entrées. [Doctoral Dissertation]. Evry, Institut national des télécommunications; 2012. Available from: http://www.theses.fr/2012TELE0026

8. KHOBRAGADE, KAVITA ANANDRAO. IRIS IMAGE FEATURE EXTRACTION: A MULTIWAVELET APPROACH;.

Degree: 2015, Dr. Babasaheb Ambedkar Marathwada University

newline

Advisors/Committee Members: KALE, KARBHARI V..

Subjects/Keywords: IRIS; IMAGE FEATURE; EXTRACTION; MULTIWAVELET

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APA (6th Edition):

KHOBRAGADE, K. A. (2015). IRIS IMAGE FEATURE EXTRACTION: A MULTIWAVELET APPROACH;. (Thesis). Dr. Babasaheb Ambedkar Marathwada University. Retrieved from http://shodhganga.inflibnet.ac.in/handle/10603/54407

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):

KHOBRAGADE, KAVITA ANANDRAO. “IRIS IMAGE FEATURE EXTRACTION: A MULTIWAVELET APPROACH;.” 2015. Thesis, Dr. Babasaheb Ambedkar Marathwada University. Accessed February 17, 2020. http://shodhganga.inflibnet.ac.in/handle/10603/54407.

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

MLA Handbook (7th Edition):

KHOBRAGADE, KAVITA ANANDRAO. “IRIS IMAGE FEATURE EXTRACTION: A MULTIWAVELET APPROACH;.” 2015. Web. 17 Feb 2020.

Vancouver:

KHOBRAGADE KA. IRIS IMAGE FEATURE EXTRACTION: A MULTIWAVELET APPROACH;. [Internet] [Thesis]. Dr. Babasaheb Ambedkar Marathwada University; 2015. [cited 2020 Feb 17]. Available from: http://shodhganga.inflibnet.ac.in/handle/10603/54407.

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

Council of Science Editors:

KHOBRAGADE KA. IRIS IMAGE FEATURE EXTRACTION: A MULTIWAVELET APPROACH;. [Thesis]. Dr. Babasaheb Ambedkar Marathwada University; 2015. Available from: http://shodhganga.inflibnet.ac.in/handle/10603/54407

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


The Ohio State University

9. Doo, Seung Ho. Analysis, Modeling & Exploitation of Variability in Radar Images.

Degree: PhD, Electrical and Computer Engineering, 2016, The Ohio State University

 This dissertation explores the variability in radar measurements that arises due to small changes in target aspect angle, proposes a target modeling approach with augmented… (more)

Subjects/Keywords: Electrical Engineering; Radar, Variability, Target Classification, Feature Extraction, Image Processing

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APA (6th Edition):

Doo, S. H. (2016). Analysis, Modeling & Exploitation of Variability in Radar Images. (Doctoral Dissertation). The Ohio State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=osu1461256996

Chicago Manual of Style (16th Edition):

Doo, Seung Ho. “Analysis, Modeling & Exploitation of Variability in Radar Images.” 2016. Doctoral Dissertation, The Ohio State University. Accessed February 17, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1461256996.

MLA Handbook (7th Edition):

Doo, Seung Ho. “Analysis, Modeling & Exploitation of Variability in Radar Images.” 2016. Web. 17 Feb 2020.

Vancouver:

Doo SH. Analysis, Modeling & Exploitation of Variability in Radar Images. [Internet] [Doctoral dissertation]. The Ohio State University; 2016. [cited 2020 Feb 17]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1461256996.

Council of Science Editors:

Doo SH. Analysis, Modeling & Exploitation of Variability in Radar Images. [Doctoral Dissertation]. The Ohio State University; 2016. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1461256996


Tokyo Institute of Technology / 東京工業大学

10. Boonsiri, Oranit. Computational Pathology Image Analysis Based on Nuclear Characteristics : Computational Pathology Image Analysis Based on Nuclear Characteristics.

Degree: 博士(工学), 2016, Tokyo Institute of Technology / 東京工業大学

 Cancer is a significant health problems around the world. Pathology is a microscopic study of tissue structure to examine disease. The nuclear properties have significant… (more)

Subjects/Keywords: histopathology image; computer aided diagnosis system; feature extraction

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APA (6th Edition):

Boonsiri, O. (2016). Computational Pathology Image Analysis Based on Nuclear Characteristics : Computational Pathology Image Analysis Based on Nuclear Characteristics. (Thesis). Tokyo Institute of Technology / 東京工業大学. Retrieved from http://t2r2.star.titech.ac.jp/cgi-bin/publicationinfo.cgi?q_publication_content_number=CTT100708142

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):

Boonsiri, Oranit. “Computational Pathology Image Analysis Based on Nuclear Characteristics : Computational Pathology Image Analysis Based on Nuclear Characteristics.” 2016. Thesis, Tokyo Institute of Technology / 東京工業大学. Accessed February 17, 2020. http://t2r2.star.titech.ac.jp/cgi-bin/publicationinfo.cgi?q_publication_content_number=CTT100708142.

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

MLA Handbook (7th Edition):

Boonsiri, Oranit. “Computational Pathology Image Analysis Based on Nuclear Characteristics : Computational Pathology Image Analysis Based on Nuclear Characteristics.” 2016. Web. 17 Feb 2020.

Vancouver:

Boonsiri O. Computational Pathology Image Analysis Based on Nuclear Characteristics : Computational Pathology Image Analysis Based on Nuclear Characteristics. [Internet] [Thesis]. Tokyo Institute of Technology / 東京工業大学; 2016. [cited 2020 Feb 17]. Available from: http://t2r2.star.titech.ac.jp/cgi-bin/publicationinfo.cgi?q_publication_content_number=CTT100708142.

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

Council of Science Editors:

Boonsiri O. Computational Pathology Image Analysis Based on Nuclear Characteristics : Computational Pathology Image Analysis Based on Nuclear Characteristics. [Thesis]. Tokyo Institute of Technology / 東京工業大学; 2016. Available from: http://t2r2.star.titech.ac.jp/cgi-bin/publicationinfo.cgi?q_publication_content_number=CTT100708142

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


McMaster University

11. Emami Abarghouei, Shadi. MODEL-BASED DEFORMABLE REGISTRATION OF MRI BREAST IMAGES WITH ENHANCED FEATURE SELECTION.

Degree: MASc, 2015, McMaster University

This thesis is concerned with model-based non-rigid registration of single-modality magnetic resonance images of compressed and uncompressed breast tissue in breast cancer diagnostic/interventional imaging. First,… (more)

Subjects/Keywords: Image Registration; Deformable Model; Feature Extraction; Finite Element Model

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

APA (6th Edition):

Emami Abarghouei, S. (2015). MODEL-BASED DEFORMABLE REGISTRATION OF MRI BREAST IMAGES WITH ENHANCED FEATURE SELECTION. (Masters Thesis). McMaster University. Retrieved from http://hdl.handle.net/11375/18242

Chicago Manual of Style (16th Edition):

Emami Abarghouei, Shadi. “MODEL-BASED DEFORMABLE REGISTRATION OF MRI BREAST IMAGES WITH ENHANCED FEATURE SELECTION.” 2015. Masters Thesis, McMaster University. Accessed February 17, 2020. http://hdl.handle.net/11375/18242.

MLA Handbook (7th Edition):

Emami Abarghouei, Shadi. “MODEL-BASED DEFORMABLE REGISTRATION OF MRI BREAST IMAGES WITH ENHANCED FEATURE SELECTION.” 2015. Web. 17 Feb 2020.

Vancouver:

Emami Abarghouei S. MODEL-BASED DEFORMABLE REGISTRATION OF MRI BREAST IMAGES WITH ENHANCED FEATURE SELECTION. [Internet] [Masters thesis]. McMaster University; 2015. [cited 2020 Feb 17]. Available from: http://hdl.handle.net/11375/18242.

Council of Science Editors:

Emami Abarghouei S. MODEL-BASED DEFORMABLE REGISTRATION OF MRI BREAST IMAGES WITH ENHANCED FEATURE SELECTION. [Masters Thesis]. McMaster University; 2015. Available from: http://hdl.handle.net/11375/18242


Cal Poly

12. Choi, Hyunjong. Medical Image Registration Using Artificial Neural Network.

Degree: MS, Electrical Engineering, 2015, Cal Poly

Image registration is the transformation of different sets of images into one coordinate system in order to align and overlay multiple images. Image registration… (more)

Subjects/Keywords: Medical Image Registration; Neural Network; Curvelet transform; Feature extraction; Signal Processing

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APA (6th Edition):

Choi, H. (2015). Medical Image Registration Using Artificial Neural Network. (Masters Thesis). Cal Poly. Retrieved from https://digitalcommons.calpoly.edu/theses/1523 ; 10.15368/theses.2015.171

Chicago Manual of Style (16th Edition):

Choi, Hyunjong. “Medical Image Registration Using Artificial Neural Network.” 2015. Masters Thesis, Cal Poly. Accessed February 17, 2020. https://digitalcommons.calpoly.edu/theses/1523 ; 10.15368/theses.2015.171.

MLA Handbook (7th Edition):

Choi, Hyunjong. “Medical Image Registration Using Artificial Neural Network.” 2015. Web. 17 Feb 2020.

Vancouver:

Choi H. Medical Image Registration Using Artificial Neural Network. [Internet] [Masters thesis]. Cal Poly; 2015. [cited 2020 Feb 17]. Available from: https://digitalcommons.calpoly.edu/theses/1523 ; 10.15368/theses.2015.171.

Council of Science Editors:

Choi H. Medical Image Registration Using Artificial Neural Network. [Masters Thesis]. Cal Poly; 2015. Available from: https://digitalcommons.calpoly.edu/theses/1523 ; 10.15368/theses.2015.171


Delft University of Technology

13. Pham, T.A. Optimization of Texture Feature Extraction Algorithm:.

Degree: 2010, Delft University of Technology

 Texture, the pattern of information or arrangement of the structure found in an image, is an important feature of many image types.In a general sense,… (more)

Subjects/Keywords: Texture Feature Extraction; image processing; parallel computing; cell processor

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APA (6th Edition):

Pham, T. A. (2010). Optimization of Texture Feature Extraction Algorithm:. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:a7924113-c9f8-435d-824f-0232ff6b419c

Chicago Manual of Style (16th Edition):

Pham, T A. “Optimization of Texture Feature Extraction Algorithm:.” 2010. Masters Thesis, Delft University of Technology. Accessed February 17, 2020. http://resolver.tudelft.nl/uuid:a7924113-c9f8-435d-824f-0232ff6b419c.

MLA Handbook (7th Edition):

Pham, T A. “Optimization of Texture Feature Extraction Algorithm:.” 2010. Web. 17 Feb 2020.

Vancouver:

Pham TA. Optimization of Texture Feature Extraction Algorithm:. [Internet] [Masters thesis]. Delft University of Technology; 2010. [cited 2020 Feb 17]. Available from: http://resolver.tudelft.nl/uuid:a7924113-c9f8-435d-824f-0232ff6b419c.

Council of Science Editors:

Pham TA. Optimization of Texture Feature Extraction Algorithm:. [Masters Thesis]. Delft University of Technology; 2010. Available from: http://resolver.tudelft.nl/uuid:a7924113-c9f8-435d-824f-0232ff6b419c


University of Waterloo

14. Cameron, Andrew. MAPS: A Comprehensive Feature Model for Prostate Cancer Diagnosis With Multiparametric MRI.

Degree: 2014, University of Waterloo

 Prostate cancer killed over 33000 North American men in 2013. However, the survival outlook for prostate cancer is very good if it is caught early.… (more)

Subjects/Keywords: medical image processing; magnetic resonance imaging; feature extraction

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APA (6th Edition):

Cameron, A. (2014). MAPS: A Comprehensive Feature Model for Prostate Cancer Diagnosis With Multiparametric MRI. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/8740

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):

Cameron, Andrew. “MAPS: A Comprehensive Feature Model for Prostate Cancer Diagnosis With Multiparametric MRI.” 2014. Thesis, University of Waterloo. Accessed February 17, 2020. http://hdl.handle.net/10012/8740.

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

MLA Handbook (7th Edition):

Cameron, Andrew. “MAPS: A Comprehensive Feature Model for Prostate Cancer Diagnosis With Multiparametric MRI.” 2014. Web. 17 Feb 2020.

Vancouver:

Cameron A. MAPS: A Comprehensive Feature Model for Prostate Cancer Diagnosis With Multiparametric MRI. [Internet] [Thesis]. University of Waterloo; 2014. [cited 2020 Feb 17]. Available from: http://hdl.handle.net/10012/8740.

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

Council of Science Editors:

Cameron A. MAPS: A Comprehensive Feature Model for Prostate Cancer Diagnosis With Multiparametric MRI. [Thesis]. University of Waterloo; 2014. Available from: http://hdl.handle.net/10012/8740

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


University of Wollongong

15. Wei, Xue. Scene categorization under geometric deformations.

Degree: PhD, 2016, University of Wollongong

  Humans are endowed with the ability to grasp the overall meaning or the gist of a complex visual scene at a glance. We need… (more)

Subjects/Keywords: image normalization; affine deformations; scene categorization; feature extraction

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

APA (6th Edition):

Wei, X. (2016). Scene categorization under geometric deformations. (Doctoral Dissertation). University of Wollongong. Retrieved from 0801 ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING ; https://ro.uow.edu.au/theses/4783

Chicago Manual of Style (16th Edition):

Wei, Xue. “Scene categorization under geometric deformations.” 2016. Doctoral Dissertation, University of Wollongong. Accessed February 17, 2020. 0801 ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING ; https://ro.uow.edu.au/theses/4783.

MLA Handbook (7th Edition):

Wei, Xue. “Scene categorization under geometric deformations.” 2016. Web. 17 Feb 2020.

Vancouver:

Wei X. Scene categorization under geometric deformations. [Internet] [Doctoral dissertation]. University of Wollongong; 2016. [cited 2020 Feb 17]. Available from: 0801 ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING ; https://ro.uow.edu.au/theses/4783.

Council of Science Editors:

Wei X. Scene categorization under geometric deformations. [Doctoral Dissertation]. University of Wollongong; 2016. Available from: 0801 ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING ; https://ro.uow.edu.au/theses/4783


University of Technology, Sydney

16. Li, Jiatong. Combating tracking drift : developing robust object tracking methods.

Degree: 2017, University of Technology, Sydney

 Visual object tracking plays an important role in many computer vision applications, such as video surveillance, unmanned aerial vehicle image processing, human computer interaction and… (more)

Subjects/Keywords: Robust object tracking.; Shape feature extraction techniques.; Weber's law; Image processing.

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

APA (6th Edition):

Li, J. (2017). Combating tracking drift : developing robust object tracking methods. (Thesis). University of Technology, Sydney. Retrieved from http://hdl.handle.net/10453/120179

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

Chicago Manual of Style (16th Edition):

Li, Jiatong. “Combating tracking drift : developing robust object tracking methods.” 2017. Thesis, University of Technology, Sydney. Accessed February 17, 2020. http://hdl.handle.net/10453/120179.

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

MLA Handbook (7th Edition):

Li, Jiatong. “Combating tracking drift : developing robust object tracking methods.” 2017. Web. 17 Feb 2020.

Vancouver:

Li J. Combating tracking drift : developing robust object tracking methods. [Internet] [Thesis]. University of Technology, Sydney; 2017. [cited 2020 Feb 17]. Available from: http://hdl.handle.net/10453/120179.

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

Council of Science Editors:

Li J. Combating tracking drift : developing robust object tracking methods. [Thesis]. University of Technology, Sydney; 2017. Available from: http://hdl.handle.net/10453/120179

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


Cape Peninsula University of Technology

17. Gashayija, Jean Marie. Image classification, storage and retrieval system for a 3 u cubesat .

Degree: 2014, Cape Peninsula University of Technology

 Small satellites, such as CubeSats are mainly utilized for space and earth imaging missions. Imaging CubeSats are equipped with high resolution cameras for the capturing… (more)

Subjects/Keywords: Image classification; CubeSat; Content based image retrieval system; Nanosatellite; Feature extraction and distance measure

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APA (6th Edition):

Gashayija, J. M. (2014). Image classification, storage and retrieval system for a 3 u cubesat . (Thesis). Cape Peninsula University of Technology. Retrieved from http://etd.cput.ac.za/handle/20.500.11838/1189

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):

Gashayija, Jean Marie. “Image classification, storage and retrieval system for a 3 u cubesat .” 2014. Thesis, Cape Peninsula University of Technology. Accessed February 17, 2020. http://etd.cput.ac.za/handle/20.500.11838/1189.

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

MLA Handbook (7th Edition):

Gashayija, Jean Marie. “Image classification, storage and retrieval system for a 3 u cubesat .” 2014. Web. 17 Feb 2020.

Vancouver:

Gashayija JM. Image classification, storage and retrieval system for a 3 u cubesat . [Internet] [Thesis]. Cape Peninsula University of Technology; 2014. [cited 2020 Feb 17]. Available from: http://etd.cput.ac.za/handle/20.500.11838/1189.

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

Council of Science Editors:

Gashayija JM. Image classification, storage and retrieval system for a 3 u cubesat . [Thesis]. Cape Peninsula University of Technology; 2014. Available from: http://etd.cput.ac.za/handle/20.500.11838/1189

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


University of Sydney

18. Wang, Ying. On the visual similarity analysis and visualization of art image data .

Degree: 2014, University of Sydney

 This thesis introduces a framework for analyzing the underlying visual similarities among art images. Three types of art related images are investigated. They are: (1)… (more)

Subjects/Keywords: Visual similarity analysis; Data visualization; Art image data; Image feature extraction; Self-organizing map (SOM)

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APA (6th Edition):

Wang, Y. (2014). On the visual similarity analysis and visualization of art image data . (Thesis). University of Sydney. Retrieved from http://hdl.handle.net/2123/11734

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):

Wang, Ying. “On the visual similarity analysis and visualization of art image data .” 2014. Thesis, University of Sydney. Accessed February 17, 2020. http://hdl.handle.net/2123/11734.

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

MLA Handbook (7th Edition):

Wang, Ying. “On the visual similarity analysis and visualization of art image data .” 2014. Web. 17 Feb 2020.

Vancouver:

Wang Y. On the visual similarity analysis and visualization of art image data . [Internet] [Thesis]. University of Sydney; 2014. [cited 2020 Feb 17]. Available from: http://hdl.handle.net/2123/11734.

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

Council of Science Editors:

Wang Y. On the visual similarity analysis and visualization of art image data . [Thesis]. University of Sydney; 2014. Available from: http://hdl.handle.net/2123/11734

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


Queensland University of Technology

19. Lakemond, Ruan. Multiple camera management using wide baseline matching.

Degree: 2010, Queensland University of Technology

 Camera calibration information is required in order for multiple camera networks to deliver more than the sum of many single camera systems. Methods exist for… (more)

Subjects/Keywords: image processing; computer vision; projective geometry; image registration; feature extraction; local image features; wide baseline matching; optical flow; sparse optical flow

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APA (6th Edition):

Lakemond, R. (2010). Multiple camera management using wide baseline matching. (Thesis). Queensland University of Technology. Retrieved from https://eprints.qut.edu.au/37668/

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):

Lakemond, Ruan. “Multiple camera management using wide baseline matching.” 2010. Thesis, Queensland University of Technology. Accessed February 17, 2020. https://eprints.qut.edu.au/37668/.

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

MLA Handbook (7th Edition):

Lakemond, Ruan. “Multiple camera management using wide baseline matching.” 2010. Web. 17 Feb 2020.

Vancouver:

Lakemond R. Multiple camera management using wide baseline matching. [Internet] [Thesis]. Queensland University of Technology; 2010. [cited 2020 Feb 17]. Available from: https://eprints.qut.edu.au/37668/.

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

Council of Science Editors:

Lakemond R. Multiple camera management using wide baseline matching. [Thesis]. Queensland University of Technology; 2010. Available from: https://eprints.qut.edu.au/37668/

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

20. Dias, Daniel Canastro. Detecção e Quantificação de Neurónios em C.elegans.

Degree: 2015, Instituto Politécnico do Porto

Nesta dissertação é apresentado um estudo dos sistemas de processamento automático de imagem em contexto de um problema relacionado com a individualização de neurónios em… (more)

Subjects/Keywords: C.elegans; CellProfiler; Processamento de imagem; Extração de características; Image processing; Feature extraction

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APA (6th Edition):

Dias, D. C. (2015). Detecção e Quantificação de Neurónios em C.elegans. (Thesis). Instituto Politécnico do Porto. Retrieved from https://www.rcaap.pt/detail.jsp?id=oai:recipp.ipp.pt:10400.22/8068

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):

Dias, Daniel Canastro. “Detecção e Quantificação de Neurónios em C.elegans.” 2015. Thesis, Instituto Politécnico do Porto. Accessed February 17, 2020. https://www.rcaap.pt/detail.jsp?id=oai:recipp.ipp.pt:10400.22/8068.

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

MLA Handbook (7th Edition):

Dias, Daniel Canastro. “Detecção e Quantificação de Neurónios em C.elegans.” 2015. Web. 17 Feb 2020.

Vancouver:

Dias DC. Detecção e Quantificação de Neurónios em C.elegans. [Internet] [Thesis]. Instituto Politécnico do Porto; 2015. [cited 2020 Feb 17]. Available from: https://www.rcaap.pt/detail.jsp?id=oai:recipp.ipp.pt:10400.22/8068.

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

Council of Science Editors:

Dias DC. Detecção e Quantificação de Neurónios em C.elegans. [Thesis]. Instituto Politécnico do Porto; 2015. Available from: https://www.rcaap.pt/detail.jsp?id=oai:recipp.ipp.pt:10400.22/8068

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


University of Louisville

21. Durak, Nurcan. Coronal loop detection from solar images and extraction of salient contour groups from cluttered images.

Degree: PhD, 2011, University of Louisville

  This dissertation addresses two different problems: 1) coronal loop detection from solar images: and 2) salient contour group extraction from cluttered images. In the… (more)

Subjects/Keywords: Curve tracing; Feature extraction; Contour grouping; Pattern recognition; Coronal loops; Image retrieval

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

APA (6th Edition):

Durak, N. (2011). Coronal loop detection from solar images and extraction of salient contour groups from cluttered images. (Doctoral Dissertation). University of Louisville. Retrieved from 10.18297/etd/386 ; https://ir.library.louisville.edu/etd/386

Chicago Manual of Style (16th Edition):

Durak, Nurcan. “Coronal loop detection from solar images and extraction of salient contour groups from cluttered images.” 2011. Doctoral Dissertation, University of Louisville. Accessed February 17, 2020. 10.18297/etd/386 ; https://ir.library.louisville.edu/etd/386.

MLA Handbook (7th Edition):

Durak, Nurcan. “Coronal loop detection from solar images and extraction of salient contour groups from cluttered images.” 2011. Web. 17 Feb 2020.

Vancouver:

Durak N. Coronal loop detection from solar images and extraction of salient contour groups from cluttered images. [Internet] [Doctoral dissertation]. University of Louisville; 2011. [cited 2020 Feb 17]. Available from: 10.18297/etd/386 ; https://ir.library.louisville.edu/etd/386.

Council of Science Editors:

Durak N. Coronal loop detection from solar images and extraction of salient contour groups from cluttered images. [Doctoral Dissertation]. University of Louisville; 2011. Available from: 10.18297/etd/386 ; https://ir.library.louisville.edu/etd/386


North Carolina State University

22. Gunduz, Aysegul. Compression and Transmission of Facial Images Over Very Narrowband Channels.

Degree: MS, Electrical Engineering, 2003, North Carolina State University

 Law enforcement officers on mobile duty are often confronted with ID authentication of subjects, requiring the transmission of a driver's license picture over wireless channels… (more)

Subjects/Keywords: Facial Feature Extraction; Facial Image Compression

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

APA (6th Edition):

Gunduz, A. (2003). Compression and Transmission of Facial Images Over Very Narrowband Channels. (Thesis). North Carolina State University. Retrieved from http://www.lib.ncsu.edu/resolver/1840.16/2375

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):

Gunduz, Aysegul. “Compression and Transmission of Facial Images Over Very Narrowband Channels.” 2003. Thesis, North Carolina State University. Accessed February 17, 2020. http://www.lib.ncsu.edu/resolver/1840.16/2375.

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

MLA Handbook (7th Edition):

Gunduz, Aysegul. “Compression and Transmission of Facial Images Over Very Narrowband Channels.” 2003. Web. 17 Feb 2020.

Vancouver:

Gunduz A. Compression and Transmission of Facial Images Over Very Narrowband Channels. [Internet] [Thesis]. North Carolina State University; 2003. [cited 2020 Feb 17]. Available from: http://www.lib.ncsu.edu/resolver/1840.16/2375.

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

Council of Science Editors:

Gunduz A. Compression and Transmission of Facial Images Over Very Narrowband Channels. [Thesis]. North Carolina State University; 2003. Available from: http://www.lib.ncsu.edu/resolver/1840.16/2375

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


University of Dayton

23. Krieger, Evan. Directional Ringlet Intensity Feature Transform for Tracking in Enhanced Wide Area Motion Imagery.

Degree: MS(M.S.), Electrical Engineering, 2015, University of Dayton

 Object tracking in the wide area motion imagery (WAMI) data may be subjected to many challenges including object occlusion, rotation, scaling, illumination changes, and background… (more)

Subjects/Keywords: Electrical Engineering; object tracking; Kirsch mask; Gaussian ringlet; image enhancement; super-resolution; feature extraction

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APA (6th Edition):

Krieger, E. (2015). Directional Ringlet Intensity Feature Transform for Tracking in Enhanced Wide Area Motion Imagery. (Masters Thesis). University of Dayton. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=dayton1447950509

Chicago Manual of Style (16th Edition):

Krieger, Evan. “Directional Ringlet Intensity Feature Transform for Tracking in Enhanced Wide Area Motion Imagery.” 2015. Masters Thesis, University of Dayton. Accessed February 17, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1447950509.

MLA Handbook (7th Edition):

Krieger, Evan. “Directional Ringlet Intensity Feature Transform for Tracking in Enhanced Wide Area Motion Imagery.” 2015. Web. 17 Feb 2020.

Vancouver:

Krieger E. Directional Ringlet Intensity Feature Transform for Tracking in Enhanced Wide Area Motion Imagery. [Internet] [Masters thesis]. University of Dayton; 2015. [cited 2020 Feb 17]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=dayton1447950509.

Council of Science Editors:

Krieger E. Directional Ringlet Intensity Feature Transform for Tracking in Enhanced Wide Area Motion Imagery. [Masters Thesis]. University of Dayton; 2015. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=dayton1447950509


Australian National University

24. Liang, Jie. Spectral-spatial Feature Extraction for Hyperspectral Image Classification .

Degree: 2016, Australian National University

 As an emerging technology, hyperspectral imaging provides huge opportunities in both remote sensing and computer vision. The advantage of hyperspectral imaging comes from the high… (more)

Subjects/Keywords: Hyperspectral Imaging; Spectral-spatial Feature Extraction; Image Classification; Saliency; Face Recognition; Sampling Strategy

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APA (6th Edition):

Liang, J. (2016). Spectral-spatial Feature Extraction for Hyperspectral Image Classification . (Thesis). Australian National University. Retrieved from http://hdl.handle.net/1885/111995

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):

Liang, Jie. “Spectral-spatial Feature Extraction for Hyperspectral Image Classification .” 2016. Thesis, Australian National University. Accessed February 17, 2020. http://hdl.handle.net/1885/111995.

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

MLA Handbook (7th Edition):

Liang, Jie. “Spectral-spatial Feature Extraction for Hyperspectral Image Classification .” 2016. Web. 17 Feb 2020.

Vancouver:

Liang J. Spectral-spatial Feature Extraction for Hyperspectral Image Classification . [Internet] [Thesis]. Australian National University; 2016. [cited 2020 Feb 17]. Available from: http://hdl.handle.net/1885/111995.

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

Council of Science Editors:

Liang J. Spectral-spatial Feature Extraction for Hyperspectral Image Classification . [Thesis]. Australian National University; 2016. Available from: http://hdl.handle.net/1885/111995

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


Central Queensland University

25. Chowdhury, S. Feature extraction and classification approach for the analysis of roadside video data.

Degree: 2017, Central Queensland University

The research in this thesis focused on developing an automatic video analysis approach for roadside object detection and classification. It investigated the problem of detecting… (more)

Subjects/Keywords: feature extraction; classification; object identification; image processing; 080108 Neural, Evolutionary and Fuzzy Computation

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APA (6th Edition):

Chowdhury, S. (2017). Feature extraction and classification approach for the analysis of roadside video data. (Thesis). Central Queensland University. Retrieved from http://hdl.cqu.edu.au/10018/1212300

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):

Chowdhury, S. “Feature extraction and classification approach for the analysis of roadside video data.” 2017. Thesis, Central Queensland University. Accessed February 17, 2020. http://hdl.cqu.edu.au/10018/1212300.

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

MLA Handbook (7th Edition):

Chowdhury, S. “Feature extraction and classification approach for the analysis of roadside video data.” 2017. Web. 17 Feb 2020.

Vancouver:

Chowdhury S. Feature extraction and classification approach for the analysis of roadside video data. [Internet] [Thesis]. Central Queensland University; 2017. [cited 2020 Feb 17]. Available from: http://hdl.cqu.edu.au/10018/1212300.

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

Council of Science Editors:

Chowdhury S. Feature extraction and classification approach for the analysis of roadside video data. [Thesis]. Central Queensland University; 2017. Available from: http://hdl.cqu.edu.au/10018/1212300

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


Utah State University

26. He, Yi. An Analysis of Airborne Data Collection Methods for Updating Highway Feature Inventory.

Degree: MS, Civil and Environmental Engineering, 2016, Utah State University

  Highway assets, including traffic signs, traffic signals, light poles, and guardrails, are important components of transportation networks. They guide, warn and protect drivers, and… (more)

Subjects/Keywords: Airborne LiDAR; aerial image; highway inventory; automatic feature extraction; Civil and Environmental Engineering

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APA (6th Edition):

He, Y. (2016). An Analysis of Airborne Data Collection Methods for Updating Highway Feature Inventory. (Masters Thesis). Utah State University. Retrieved from https://digitalcommons.usu.edu/etd/5016

Chicago Manual of Style (16th Edition):

He, Yi. “An Analysis of Airborne Data Collection Methods for Updating Highway Feature Inventory.” 2016. Masters Thesis, Utah State University. Accessed February 17, 2020. https://digitalcommons.usu.edu/etd/5016.

MLA Handbook (7th Edition):

He, Yi. “An Analysis of Airborne Data Collection Methods for Updating Highway Feature Inventory.” 2016. Web. 17 Feb 2020.

Vancouver:

He Y. An Analysis of Airborne Data Collection Methods for Updating Highway Feature Inventory. [Internet] [Masters thesis]. Utah State University; 2016. [cited 2020 Feb 17]. Available from: https://digitalcommons.usu.edu/etd/5016.

Council of Science Editors:

He Y. An Analysis of Airborne Data Collection Methods for Updating Highway Feature Inventory. [Masters Thesis]. Utah State University; 2016. Available from: https://digitalcommons.usu.edu/etd/5016


San Jose State University

27. Ramesh, Rathna. Machine Learning Methods for Kidney Disease Screening.

Degree: MS, Computer Engineering, 2018, San Jose State University

  The number of people diagnosed with advanced stages of kidney disease has been rising every year. Early detection and constant monitoring are the only… (more)

Subjects/Keywords: computer vision; feature extraction; image processing; kidney disease monitor; machine learning; smart phone application

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APA (6th Edition):

Ramesh, R. (2018). Machine Learning Methods for Kidney Disease Screening. (Masters Thesis). San Jose State University. Retrieved from https://doi.org/10.31979/etd.hetg-6336 ; https://scholarworks.sjsu.edu/etd_theses/4951

Chicago Manual of Style (16th Edition):

Ramesh, Rathna. “Machine Learning Methods for Kidney Disease Screening.” 2018. Masters Thesis, San Jose State University. Accessed February 17, 2020. https://doi.org/10.31979/etd.hetg-6336 ; https://scholarworks.sjsu.edu/etd_theses/4951.

MLA Handbook (7th Edition):

Ramesh, Rathna. “Machine Learning Methods for Kidney Disease Screening.” 2018. Web. 17 Feb 2020.

Vancouver:

Ramesh R. Machine Learning Methods for Kidney Disease Screening. [Internet] [Masters thesis]. San Jose State University; 2018. [cited 2020 Feb 17]. Available from: https://doi.org/10.31979/etd.hetg-6336 ; https://scholarworks.sjsu.edu/etd_theses/4951.

Council of Science Editors:

Ramesh R. Machine Learning Methods for Kidney Disease Screening. [Masters Thesis]. San Jose State University; 2018. Available from: https://doi.org/10.31979/etd.hetg-6336 ; https://scholarworks.sjsu.edu/etd_theses/4951


University of Sydney

28. Yu, Kaimin. Towards Realistic Facial Expression Recognition .

Degree: 2013, University of Sydney

 Automatic facial expression recognition has attracted significant attention over the past decades. Although substantial progress has been achieved for certain scenarios (such as frontal faces… (more)

Subjects/Keywords: Facial expression recognition; pattern recognition; machine learning; feature extraction; image classification; facial expression dataset

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APA (6th Edition):

Yu, K. (2013). Towards Realistic Facial Expression Recognition . (Thesis). University of Sydney. Retrieved from http://hdl.handle.net/2123/9459

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):

Yu, Kaimin. “Towards Realistic Facial Expression Recognition .” 2013. Thesis, University of Sydney. Accessed February 17, 2020. http://hdl.handle.net/2123/9459.

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

MLA Handbook (7th Edition):

Yu, Kaimin. “Towards Realistic Facial Expression Recognition .” 2013. Web. 17 Feb 2020.

Vancouver:

Yu K. Towards Realistic Facial Expression Recognition . [Internet] [Thesis]. University of Sydney; 2013. [cited 2020 Feb 17]. Available from: http://hdl.handle.net/2123/9459.

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

Council of Science Editors:

Yu K. Towards Realistic Facial Expression Recognition . [Thesis]. University of Sydney; 2013. Available from: http://hdl.handle.net/2123/9459

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


Virginia Tech

29. Wang, Yu. A Deep Learning Based Pipeline for Image Grading of Diabetic Retinopathy.

Degree: MS, Computer Science, 2018, Virginia Tech

 Diabetic Retinopathy (DR) is one of the principal sources of blindness due to diabetes mellitus. It can be identified by lesions of the retina, namely… (more)

Subjects/Keywords: Retina Image; Image Grading; Diabetic Retinopathy; Early Detection; Feature Extraction; ConvNN; Deep Learning; Boosting Decision Tree

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

APA (6th Edition):

Wang, Y. (2018). A Deep Learning Based Pipeline for Image Grading of Diabetic Retinopathy. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/83607

Chicago Manual of Style (16th Edition):

Wang, Yu. “A Deep Learning Based Pipeline for Image Grading of Diabetic Retinopathy.” 2018. Masters Thesis, Virginia Tech. Accessed February 17, 2020. http://hdl.handle.net/10919/83607.

MLA Handbook (7th Edition):

Wang, Yu. “A Deep Learning Based Pipeline for Image Grading of Diabetic Retinopathy.” 2018. Web. 17 Feb 2020.

Vancouver:

Wang Y. A Deep Learning Based Pipeline for Image Grading of Diabetic Retinopathy. [Internet] [Masters thesis]. Virginia Tech; 2018. [cited 2020 Feb 17]. Available from: http://hdl.handle.net/10919/83607.

Council of Science Editors:

Wang Y. A Deep Learning Based Pipeline for Image Grading of Diabetic Retinopathy. [Masters Thesis]. Virginia Tech; 2018. Available from: http://hdl.handle.net/10919/83607


University of North Texas

30. Dahal, Ashok. Detection of Ulcerative Colitis Severity and Enhancement of Informative Frame Filtering Using Texture Analysis in Colonoscopy Videos.

Degree: 2015, University of North Texas

 There are several types of disorders that affect our colon’s ability to function properly such as colorectal cancer, ulcerative colitis, diverticulitis, irritable bowel syndrome and… (more)

Subjects/Keywords: ulcerative colitis; image texture; clustering feature extraction; colonoscopy; Ulcerative colitis  – Diagnosis.; Colonoscopy.; Video recording in medicine.; Image processing  – Digital techniques.

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

APA (6th Edition):

Dahal, A. (2015). Detection of Ulcerative Colitis Severity and Enhancement of Informative Frame Filtering Using Texture Analysis in Colonoscopy Videos. (Thesis). University of North Texas. Retrieved from https://digital.library.unt.edu/ark:/67531/metadc822759/

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):

Dahal, Ashok. “Detection of Ulcerative Colitis Severity and Enhancement of Informative Frame Filtering Using Texture Analysis in Colonoscopy Videos.” 2015. Thesis, University of North Texas. Accessed February 17, 2020. https://digital.library.unt.edu/ark:/67531/metadc822759/.

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

MLA Handbook (7th Edition):

Dahal, Ashok. “Detection of Ulcerative Colitis Severity and Enhancement of Informative Frame Filtering Using Texture Analysis in Colonoscopy Videos.” 2015. Web. 17 Feb 2020.

Vancouver:

Dahal A. Detection of Ulcerative Colitis Severity and Enhancement of Informative Frame Filtering Using Texture Analysis in Colonoscopy Videos. [Internet] [Thesis]. University of North Texas; 2015. [cited 2020 Feb 17]. Available from: https://digital.library.unt.edu/ark:/67531/metadc822759/.

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

Council of Science Editors:

Dahal A. Detection of Ulcerative Colitis Severity and Enhancement of Informative Frame Filtering Using Texture Analysis in Colonoscopy Videos. [Thesis]. University of North Texas; 2015. Available from: https://digital.library.unt.edu/ark:/67531/metadc822759/

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

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