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You searched for subject:(Computer vision AND machine learning applications ). Showing records 1 – 30 of 755 total matches.

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University of Oxford

1. Mahendran, Aravindh. Self-supervised learning using motion and visualizing convolutional neural networks.

Degree: PhD, 2018, University of Oxford

 We propose a novel method for learning convolutional image representations without manual supervision. We use motion in the form of optical-flow, to supervise representations of… (more)

Subjects/Keywords: Computer Vision; Machine Learning

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APA (6th Edition):

Mahendran, A. (2018). Self-supervised learning using motion and visualizing convolutional neural networks. (Doctoral Dissertation). University of Oxford. Retrieved from http://ora.ox.ac.uk/objects/uuid:05ef7004-0bb1-4852-be1f-892daf694430 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.770662

Chicago Manual of Style (16th Edition):

Mahendran, Aravindh. “Self-supervised learning using motion and visualizing convolutional neural networks.” 2018. Doctoral Dissertation, University of Oxford. Accessed October 15, 2019. http://ora.ox.ac.uk/objects/uuid:05ef7004-0bb1-4852-be1f-892daf694430 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.770662.

MLA Handbook (7th Edition):

Mahendran, Aravindh. “Self-supervised learning using motion and visualizing convolutional neural networks.” 2018. Web. 15 Oct 2019.

Vancouver:

Mahendran A. Self-supervised learning using motion and visualizing convolutional neural networks. [Internet] [Doctoral dissertation]. University of Oxford; 2018. [cited 2019 Oct 15]. Available from: http://ora.ox.ac.uk/objects/uuid:05ef7004-0bb1-4852-be1f-892daf694430 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.770662.

Council of Science Editors:

Mahendran A. Self-supervised learning using motion and visualizing convolutional neural networks. [Doctoral Dissertation]. University of Oxford; 2018. Available from: http://ora.ox.ac.uk/objects/uuid:05ef7004-0bb1-4852-be1f-892daf694430 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.770662


University of Cambridge

2. Kendall, Alex Guy. Geometry and uncertainty in deep learning for computer vision.

Degree: PhD, 2019, University of Cambridge

 Deep learning and convolutional neural networks have become the dominant tool for computer vision. These techniques excel at learning complicated representations from data using supervised… (more)

Subjects/Keywords: Deep Learning; Computer Vision; Machine Learning; Robotics

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APA (6th Edition):

Kendall, A. G. (2019). Geometry and uncertainty in deep learning for computer vision. (Doctoral Dissertation). University of Cambridge. Retrieved from https://www.repository.cam.ac.uk/handle/1810/287944 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.763850

Chicago Manual of Style (16th Edition):

Kendall, Alex Guy. “Geometry and uncertainty in deep learning for computer vision.” 2019. Doctoral Dissertation, University of Cambridge. Accessed October 15, 2019. https://www.repository.cam.ac.uk/handle/1810/287944 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.763850.

MLA Handbook (7th Edition):

Kendall, Alex Guy. “Geometry and uncertainty in deep learning for computer vision.” 2019. Web. 15 Oct 2019.

Vancouver:

Kendall AG. Geometry and uncertainty in deep learning for computer vision. [Internet] [Doctoral dissertation]. University of Cambridge; 2019. [cited 2019 Oct 15]. Available from: https://www.repository.cam.ac.uk/handle/1810/287944 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.763850.

Council of Science Editors:

Kendall AG. Geometry and uncertainty in deep learning for computer vision. [Doctoral Dissertation]. University of Cambridge; 2019. Available from: https://www.repository.cam.ac.uk/handle/1810/287944 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.763850


Victoria University of Wellington

3. Ghifary, Muhammad. Domain Adaptation and Domain Generalization with Representation Learning.

Degree: 2016, Victoria University of Wellington

Machine learning has achieved great successes in the area of computer vision, especially in object recognition or classification. One of the core factors of the… (more)

Subjects/Keywords: Machine learning; Computer vision; Transfer learning

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APA (6th Edition):

Ghifary, M. (2016). Domain Adaptation and Domain Generalization with Representation Learning. (Doctoral Dissertation). Victoria University of Wellington. Retrieved from http://hdl.handle.net/10063/5181

Chicago Manual of Style (16th Edition):

Ghifary, Muhammad. “Domain Adaptation and Domain Generalization with Representation Learning.” 2016. Doctoral Dissertation, Victoria University of Wellington. Accessed October 15, 2019. http://hdl.handle.net/10063/5181.

MLA Handbook (7th Edition):

Ghifary, Muhammad. “Domain Adaptation and Domain Generalization with Representation Learning.” 2016. Web. 15 Oct 2019.

Vancouver:

Ghifary M. Domain Adaptation and Domain Generalization with Representation Learning. [Internet] [Doctoral dissertation]. Victoria University of Wellington; 2016. [cited 2019 Oct 15]. Available from: http://hdl.handle.net/10063/5181.

Council of Science Editors:

Ghifary M. Domain Adaptation and Domain Generalization with Representation Learning. [Doctoral Dissertation]. Victoria University of Wellington; 2016. Available from: http://hdl.handle.net/10063/5181


University of California – San Diego

4. Christiansen, Eric Martin. From local descriptors to in silico labeling.

Degree: Computer Science, 2018, University of California – San Diego

Computer vision has seen great change in the last decade, characterized by shallow machine learning models on hand-tuned features in 2010, to deep models with… (more)

Subjects/Keywords: Computer science; computer vision; machine learning; microscopy

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APA (6th Edition):

Christiansen, E. M. (2018). From local descriptors to in silico labeling. (Thesis). University of California – San Diego. Retrieved from http://www.escholarship.org/uc/item/40b479dh

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):

Christiansen, Eric Martin. “From local descriptors to in silico labeling.” 2018. Thesis, University of California – San Diego. Accessed October 15, 2019. http://www.escholarship.org/uc/item/40b479dh.

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

MLA Handbook (7th Edition):

Christiansen, Eric Martin. “From local descriptors to in silico labeling.” 2018. Web. 15 Oct 2019.

Vancouver:

Christiansen EM. From local descriptors to in silico labeling. [Internet] [Thesis]. University of California – San Diego; 2018. [cited 2019 Oct 15]. Available from: http://www.escholarship.org/uc/item/40b479dh.

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

Council of Science Editors:

Christiansen EM. From local descriptors to in silico labeling. [Thesis]. University of California – San Diego; 2018. Available from: http://www.escholarship.org/uc/item/40b479dh

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


Cornell University

5. Veit, Andreas. Learning Conditional Models for Visual Perception .

Degree: 2018, Cornell University

 In recent years, the field of computer vision has seen a series of major advances, made possible by rapid development in algorithms, data collection and… (more)

Subjects/Keywords: computer vision; machine learning; Computer science

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APA (6th Edition):

Veit, A. (2018). Learning Conditional Models for Visual Perception . (Thesis). Cornell University. Retrieved from http://hdl.handle.net/1813/59494

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):

Veit, Andreas. “Learning Conditional Models for Visual Perception .” 2018. Thesis, Cornell University. Accessed October 15, 2019. http://hdl.handle.net/1813/59494.

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

MLA Handbook (7th Edition):

Veit, Andreas. “Learning Conditional Models for Visual Perception .” 2018. Web. 15 Oct 2019.

Vancouver:

Veit A. Learning Conditional Models for Visual Perception . [Internet] [Thesis]. Cornell University; 2018. [cited 2019 Oct 15]. Available from: http://hdl.handle.net/1813/59494.

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

Council of Science Editors:

Veit A. Learning Conditional Models for Visual Perception . [Thesis]. Cornell University; 2018. Available from: http://hdl.handle.net/1813/59494

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


Cornell University

6. Upchurch, Paul. Data-Driven Material Recognition and Photorealistic Image Editing Using Deep Convolutional Neural Networks .

Degree: 2018, Cornell University

 Fully automatic processing of images is a key challenge for the 21st century. Our processing needs lie beyond just organizing photos by date and location.… (more)

Subjects/Keywords: computer vision; machine learning; Computer science

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APA (6th Edition):

Upchurch, P. (2018). Data-Driven Material Recognition and Photorealistic Image Editing Using Deep Convolutional Neural Networks . (Thesis). Cornell University. Retrieved from http://hdl.handle.net/1813/59808

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):

Upchurch, Paul. “Data-Driven Material Recognition and Photorealistic Image Editing Using Deep Convolutional Neural Networks .” 2018. Thesis, Cornell University. Accessed October 15, 2019. http://hdl.handle.net/1813/59808.

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

MLA Handbook (7th Edition):

Upchurch, Paul. “Data-Driven Material Recognition and Photorealistic Image Editing Using Deep Convolutional Neural Networks .” 2018. Web. 15 Oct 2019.

Vancouver:

Upchurch P. Data-Driven Material Recognition and Photorealistic Image Editing Using Deep Convolutional Neural Networks . [Internet] [Thesis]. Cornell University; 2018. [cited 2019 Oct 15]. Available from: http://hdl.handle.net/1813/59808.

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

Council of Science Editors:

Upchurch P. Data-Driven Material Recognition and Photorealistic Image Editing Using Deep Convolutional Neural Networks . [Thesis]. Cornell University; 2018. Available from: http://hdl.handle.net/1813/59808

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


University of Michigan

7. Chao, Yu-Wei. Visual Recognition and Synthesis of Human-Object Interactions.

Degree: PhD, Computer Science & Engineering, 2019, University of Michigan

 The ability to perceive and understand people's actions enables humans to efficiently communicate and collaborate in society. Endowing machines with such ability is an important… (more)

Subjects/Keywords: computer vision; machine learning; Computer Science; Engineering

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APA (6th Edition):

Chao, Y. (2019). Visual Recognition and Synthesis of Human-Object Interactions. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/150045

Chicago Manual of Style (16th Edition):

Chao, Yu-Wei. “Visual Recognition and Synthesis of Human-Object Interactions.” 2019. Doctoral Dissertation, University of Michigan. Accessed October 15, 2019. http://hdl.handle.net/2027.42/150045.

MLA Handbook (7th Edition):

Chao, Yu-Wei. “Visual Recognition and Synthesis of Human-Object Interactions.” 2019. Web. 15 Oct 2019.

Vancouver:

Chao Y. Visual Recognition and Synthesis of Human-Object Interactions. [Internet] [Doctoral dissertation]. University of Michigan; 2019. [cited 2019 Oct 15]. Available from: http://hdl.handle.net/2027.42/150045.

Council of Science Editors:

Chao Y. Visual Recognition and Synthesis of Human-Object Interactions. [Doctoral Dissertation]. University of Michigan; 2019. Available from: http://hdl.handle.net/2027.42/150045


University of Louisville

8. Moalla, Mahdi. Developing computer vision technology to automate pitch analysis in baseball.

Degree: MS, 2017, University of Louisville

  Lokator is a baseball training system designed to document pitch location while teaching pitch command, selection and sequencing. It is composed of a pitching… (more)

Subjects/Keywords: computer vision; machine learning; baseball; Sports Management

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APA (6th Edition):

Moalla, M. (2017). Developing computer vision technology to automate pitch analysis in baseball. (Masters Thesis). University of Louisville. Retrieved from 10.18297/etd/2686 ; https://ir.library.louisville.edu/etd/2686

Chicago Manual of Style (16th Edition):

Moalla, Mahdi. “Developing computer vision technology to automate pitch analysis in baseball.” 2017. Masters Thesis, University of Louisville. Accessed October 15, 2019. 10.18297/etd/2686 ; https://ir.library.louisville.edu/etd/2686.

MLA Handbook (7th Edition):

Moalla, Mahdi. “Developing computer vision technology to automate pitch analysis in baseball.” 2017. Web. 15 Oct 2019.

Vancouver:

Moalla M. Developing computer vision technology to automate pitch analysis in baseball. [Internet] [Masters thesis]. University of Louisville; 2017. [cited 2019 Oct 15]. Available from: 10.18297/etd/2686 ; https://ir.library.louisville.edu/etd/2686.

Council of Science Editors:

Moalla M. Developing computer vision technology to automate pitch analysis in baseball. [Masters Thesis]. University of Louisville; 2017. Available from: 10.18297/etd/2686 ; https://ir.library.louisville.edu/etd/2686


University of Victoria

9. Mehrnejad, Marzieh. Towards robust identification of slow moving animals in deep-sea imagery by integrating shape and appearance cues.

Degree: Department of Electrical and Computer Engineering, 2015, University of Victoria

 Underwater video data are a rich source of information for marine biologists. However, the large amount of recorded video creates a ’big data’ problem, which… (more)

Subjects/Keywords: Computer Vision; Classification; Neural Networks; Machine Learning

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APA (6th Edition):

Mehrnejad, M. (2015). Towards robust identification of slow moving animals in deep-sea imagery by integrating shape and appearance cues. (Masters Thesis). University of Victoria. Retrieved from http://hdl.handle.net/1828/6439

Chicago Manual of Style (16th Edition):

Mehrnejad, Marzieh. “Towards robust identification of slow moving animals in deep-sea imagery by integrating shape and appearance cues.” 2015. Masters Thesis, University of Victoria. Accessed October 15, 2019. http://hdl.handle.net/1828/6439.

MLA Handbook (7th Edition):

Mehrnejad, Marzieh. “Towards robust identification of slow moving animals in deep-sea imagery by integrating shape and appearance cues.” 2015. Web. 15 Oct 2019.

Vancouver:

Mehrnejad M. Towards robust identification of slow moving animals in deep-sea imagery by integrating shape and appearance cues. [Internet] [Masters thesis]. University of Victoria; 2015. [cited 2019 Oct 15]. Available from: http://hdl.handle.net/1828/6439.

Council of Science Editors:

Mehrnejad M. Towards robust identification of slow moving animals in deep-sea imagery by integrating shape and appearance cues. [Masters Thesis]. University of Victoria; 2015. Available from: http://hdl.handle.net/1828/6439


University of Ontario Institute of Technology

10. Taylor, Wesley. Achieving real-time video summarization on commodity hardware.

Degree: 2018, University of Ontario Institute of Technology

 We present a system for automatic video summarization which is able to operate in real-time on commodity hardware. This is achieved by performing segmentation to… (more)

Subjects/Keywords: Video summarization; Machine learning; Computer vision

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APA (6th Edition):

Taylor, W. (2018). Achieving real-time video summarization on commodity hardware. (Thesis). University of Ontario Institute of Technology. Retrieved from http://hdl.handle.net/10155/918

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):

Taylor, Wesley. “Achieving real-time video summarization on commodity hardware.” 2018. Thesis, University of Ontario Institute of Technology. Accessed October 15, 2019. http://hdl.handle.net/10155/918.

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

MLA Handbook (7th Edition):

Taylor, Wesley. “Achieving real-time video summarization on commodity hardware.” 2018. Web. 15 Oct 2019.

Vancouver:

Taylor W. Achieving real-time video summarization on commodity hardware. [Internet] [Thesis]. University of Ontario Institute of Technology; 2018. [cited 2019 Oct 15]. Available from: http://hdl.handle.net/10155/918.

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

Council of Science Editors:

Taylor W. Achieving real-time video summarization on commodity hardware. [Thesis]. University of Ontario Institute of Technology; 2018. Available from: http://hdl.handle.net/10155/918

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


Virginia Tech

11. Burns, James Ian. Agricultural Crop Monitoring with Computer Vision.

Degree: MS, Mechanical Engineering, 2014, Virginia Tech

 Precision agriculture allows farmers to efficiently use their resources with site-specific applications. The current work looks to computer vision for the data collection method necessary… (more)

Subjects/Keywords: Computer Vision; Machine Learning; Agriculture; Multispectral; Automation

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APA (6th Edition):

Burns, J. I. (2014). Agricultural Crop Monitoring with Computer Vision. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/52563

Chicago Manual of Style (16th Edition):

Burns, James Ian. “Agricultural Crop Monitoring with Computer Vision.” 2014. Masters Thesis, Virginia Tech. Accessed October 15, 2019. http://hdl.handle.net/10919/52563.

MLA Handbook (7th Edition):

Burns, James Ian. “Agricultural Crop Monitoring with Computer Vision.” 2014. Web. 15 Oct 2019.

Vancouver:

Burns JI. Agricultural Crop Monitoring with Computer Vision. [Internet] [Masters thesis]. Virginia Tech; 2014. [cited 2019 Oct 15]. Available from: http://hdl.handle.net/10919/52563.

Council of Science Editors:

Burns JI. Agricultural Crop Monitoring with Computer Vision. [Masters Thesis]. Virginia Tech; 2014. Available from: http://hdl.handle.net/10919/52563


Virginia Tech

12. Cogswell, Michael Andrew. Understanding Representations and Reducing their Redundancy in Deep Networks.

Degree: MS, Computer Science, 2016, Virginia Tech

 Neural networks in their modern deep learning incarnation have achieved state of the art performance on a wide variety of tasks and domains. A core… (more)

Subjects/Keywords: Object Recognition; Overfitting; Computer Vision; Machine Learning

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APA (6th Edition):

Cogswell, M. A. (2016). Understanding Representations and Reducing their Redundancy in Deep Networks. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/78167

Chicago Manual of Style (16th Edition):

Cogswell, Michael Andrew. “Understanding Representations and Reducing their Redundancy in Deep Networks.” 2016. Masters Thesis, Virginia Tech. Accessed October 15, 2019. http://hdl.handle.net/10919/78167.

MLA Handbook (7th Edition):

Cogswell, Michael Andrew. “Understanding Representations and Reducing their Redundancy in Deep Networks.” 2016. Web. 15 Oct 2019.

Vancouver:

Cogswell MA. Understanding Representations and Reducing their Redundancy in Deep Networks. [Internet] [Masters thesis]. Virginia Tech; 2016. [cited 2019 Oct 15]. Available from: http://hdl.handle.net/10919/78167.

Council of Science Editors:

Cogswell MA. Understanding Representations and Reducing their Redundancy in Deep Networks. [Masters Thesis]. Virginia Tech; 2016. Available from: http://hdl.handle.net/10919/78167


Virginia Tech

13. Mohapatra, Akrit. Natural Language Driven Image Edits using a Semantic Image Manipulation Language.

Degree: MS, Electrical and Computer Engineering, 2018, Virginia Tech

 Language provides us with a powerful tool to articulate and express ourselves! Understanding and harnessing the expressions of natural language can open the doors to… (more)

Subjects/Keywords: Machine Learning; Natural language Processing; Computer Vision

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APA (6th Edition):

Mohapatra, A. (2018). Natural Language Driven Image Edits using a Semantic Image Manipulation Language. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/83452

Chicago Manual of Style (16th Edition):

Mohapatra, Akrit. “Natural Language Driven Image Edits using a Semantic Image Manipulation Language.” 2018. Masters Thesis, Virginia Tech. Accessed October 15, 2019. http://hdl.handle.net/10919/83452.

MLA Handbook (7th Edition):

Mohapatra, Akrit. “Natural Language Driven Image Edits using a Semantic Image Manipulation Language.” 2018. Web. 15 Oct 2019.

Vancouver:

Mohapatra A. Natural Language Driven Image Edits using a Semantic Image Manipulation Language. [Internet] [Masters thesis]. Virginia Tech; 2018. [cited 2019 Oct 15]. Available from: http://hdl.handle.net/10919/83452.

Council of Science Editors:

Mohapatra A. Natural Language Driven Image Edits using a Semantic Image Manipulation Language. [Masters Thesis]. Virginia Tech; 2018. Available from: http://hdl.handle.net/10919/83452


Virginia Tech

14. Kulkarni, Amruta Kiran. Classification of Faults in Railway Ties Using Computer Vision and Machine Learning.

Degree: MS, Electrical and Computer Engineering, 2017, Virginia Tech

 This work focuses on automated classification of railway ties based on their condition using aerial imagery. Four approaches are explored and compared to achieve this… (more)

Subjects/Keywords: Computer Vision; Machine Learning; Railway Ties

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APA (6th Edition):

Kulkarni, A. K. (2017). Classification of Faults in Railway Ties Using Computer Vision and Machine Learning. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/86522

Chicago Manual of Style (16th Edition):

Kulkarni, Amruta Kiran. “Classification of Faults in Railway Ties Using Computer Vision and Machine Learning.” 2017. Masters Thesis, Virginia Tech. Accessed October 15, 2019. http://hdl.handle.net/10919/86522.

MLA Handbook (7th Edition):

Kulkarni, Amruta Kiran. “Classification of Faults in Railway Ties Using Computer Vision and Machine Learning.” 2017. Web. 15 Oct 2019.

Vancouver:

Kulkarni AK. Classification of Faults in Railway Ties Using Computer Vision and Machine Learning. [Internet] [Masters thesis]. Virginia Tech; 2017. [cited 2019 Oct 15]. Available from: http://hdl.handle.net/10919/86522.

Council of Science Editors:

Kulkarni AK. Classification of Faults in Railway Ties Using Computer Vision and Machine Learning. [Masters Thesis]. Virginia Tech; 2017. Available from: http://hdl.handle.net/10919/86522


Kennesaw State University

15. Bantupalli, Kshitij. American Sign Language Recognition Using Machine Learning and Computer Vision.

Degree: MSCS, Computer Science, 2019, Kennesaw State University

  Speech impairment is a disability which affects an individual’s ability to communicate using speech and hearing. People who are affected by this use other… (more)

Subjects/Keywords: machine learning; computer vision; neural network; Robotics

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APA (6th Edition):

Bantupalli, K. (2019). American Sign Language Recognition Using Machine Learning and Computer Vision. (Thesis). Kennesaw State University. Retrieved from https://digitalcommons.kennesaw.edu/cs_etd/21

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):

Bantupalli, Kshitij. “American Sign Language Recognition Using Machine Learning and Computer Vision.” 2019. Thesis, Kennesaw State University. Accessed October 15, 2019. https://digitalcommons.kennesaw.edu/cs_etd/21.

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

MLA Handbook (7th Edition):

Bantupalli, Kshitij. “American Sign Language Recognition Using Machine Learning and Computer Vision.” 2019. Web. 15 Oct 2019.

Vancouver:

Bantupalli K. American Sign Language Recognition Using Machine Learning and Computer Vision. [Internet] [Thesis]. Kennesaw State University; 2019. [cited 2019 Oct 15]. Available from: https://digitalcommons.kennesaw.edu/cs_etd/21.

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

Council of Science Editors:

Bantupalli K. American Sign Language Recognition Using Machine Learning and Computer Vision. [Thesis]. Kennesaw State University; 2019. Available from: https://digitalcommons.kennesaw.edu/cs_etd/21

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


University of New South Wales

16. Semenovich, Dimitri. Mathematical Optimisation Techniques in Computer Vision and Machine Learning.

Degree: Computer Science & Engineering, 2014, University of New South Wales

 This thesis explores applications of mathematical optimisation to problems arising in machine learning and computer vision, including kernel methods, probabilistic graphical models and several classical… (more)

Subjects/Keywords: machine learning; mathematical optimisation; computer vision

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APA (6th Edition):

Semenovich, D. (2014). Mathematical Optimisation Techniques in Computer Vision and Machine Learning. (Doctoral Dissertation). University of New South Wales. Retrieved from http://handle.unsw.edu.au/1959.4/53913 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:12623/SOURCE02?view=true

Chicago Manual of Style (16th Edition):

Semenovich, Dimitri. “Mathematical Optimisation Techniques in Computer Vision and Machine Learning.” 2014. Doctoral Dissertation, University of New South Wales. Accessed October 15, 2019. http://handle.unsw.edu.au/1959.4/53913 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:12623/SOURCE02?view=true.

MLA Handbook (7th Edition):

Semenovich, Dimitri. “Mathematical Optimisation Techniques in Computer Vision and Machine Learning.” 2014. Web. 15 Oct 2019.

Vancouver:

Semenovich D. Mathematical Optimisation Techniques in Computer Vision and Machine Learning. [Internet] [Doctoral dissertation]. University of New South Wales; 2014. [cited 2019 Oct 15]. Available from: http://handle.unsw.edu.au/1959.4/53913 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:12623/SOURCE02?view=true.

Council of Science Editors:

Semenovich D. Mathematical Optimisation Techniques in Computer Vision and Machine Learning. [Doctoral Dissertation]. University of New South Wales; 2014. Available from: http://handle.unsw.edu.au/1959.4/53913 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:12623/SOURCE02?view=true


University of New South Wales

17. Sushkov, Oleg. Autonomous robot interaction and use of objects.

Degree: Computer Science & Engineering, 2015, University of New South Wales

 This thesis is focused on the skills that must be performed by an autonomous robot to interact with objects in a complex environment. For a… (more)

Subjects/Keywords: computer vision; robotics; machine learning; robot interaction

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

APA (6th Edition):

Sushkov, O. (2015). Autonomous robot interaction and use of objects. (Doctoral Dissertation). University of New South Wales. Retrieved from http://handle.unsw.edu.au/1959.4/54289 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:34687/SOURCE02?view=true

Chicago Manual of Style (16th Edition):

Sushkov, Oleg. “Autonomous robot interaction and use of objects.” 2015. Doctoral Dissertation, University of New South Wales. Accessed October 15, 2019. http://handle.unsw.edu.au/1959.4/54289 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:34687/SOURCE02?view=true.

MLA Handbook (7th Edition):

Sushkov, Oleg. “Autonomous robot interaction and use of objects.” 2015. Web. 15 Oct 2019.

Vancouver:

Sushkov O. Autonomous robot interaction and use of objects. [Internet] [Doctoral dissertation]. University of New South Wales; 2015. [cited 2019 Oct 15]. Available from: http://handle.unsw.edu.au/1959.4/54289 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:34687/SOURCE02?view=true.

Council of Science Editors:

Sushkov O. Autonomous robot interaction and use of objects. [Doctoral Dissertation]. University of New South Wales; 2015. Available from: http://handle.unsw.edu.au/1959.4/54289 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:34687/SOURCE02?view=true


University of New South Wales

18. Thi, Tuan Hue. Learning structured data for human action analysis: a local feature approach.

Degree: Computer Science & Engineering, 2012, University of New South Wales

 This thesis presents solutions for human action analysis by learning local visual features as structured data. Human action in video, represented in set of local… (more)

Subjects/Keywords: Pattern Recognition; Computer Vision; Machine Learning

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APA (6th Edition):

Thi, T. H. (2012). Learning structured data for human action analysis: a local feature approach. (Doctoral Dissertation). University of New South Wales. Retrieved from http://handle.unsw.edu.au/1959.4/51837 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:10504/SOURCE02?view=true

Chicago Manual of Style (16th Edition):

Thi, Tuan Hue. “Learning structured data for human action analysis: a local feature approach.” 2012. Doctoral Dissertation, University of New South Wales. Accessed October 15, 2019. http://handle.unsw.edu.au/1959.4/51837 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:10504/SOURCE02?view=true.

MLA Handbook (7th Edition):

Thi, Tuan Hue. “Learning structured data for human action analysis: a local feature approach.” 2012. Web. 15 Oct 2019.

Vancouver:

Thi TH. Learning structured data for human action analysis: a local feature approach. [Internet] [Doctoral dissertation]. University of New South Wales; 2012. [cited 2019 Oct 15]. Available from: http://handle.unsw.edu.au/1959.4/51837 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:10504/SOURCE02?view=true.

Council of Science Editors:

Thi TH. Learning structured data for human action analysis: a local feature approach. [Doctoral Dissertation]. University of New South Wales; 2012. Available from: http://handle.unsw.edu.au/1959.4/51837 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:10504/SOURCE02?view=true


Clemson University

19. Kulshrestha, Ankit. Compressing Deep Neural Networks via Knowledge Distillation.

Degree: MS, Electrical and Computer Engineering (Holcomb Dept. of), 2019, Clemson University

  There has been a continuous evolution in deep neural network architectures since Alex Krizhevsky proposed AlexNet in 2012. Part of this has been due… (more)

Subjects/Keywords: Compression; Computer Vision; Machine Learning; Neural Network

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APA (6th Edition):

Kulshrestha, A. (2019). Compressing Deep Neural Networks via Knowledge Distillation. (Masters Thesis). Clemson University. Retrieved from https://tigerprints.clemson.edu/all_theses/3125

Chicago Manual of Style (16th Edition):

Kulshrestha, Ankit. “Compressing Deep Neural Networks via Knowledge Distillation.” 2019. Masters Thesis, Clemson University. Accessed October 15, 2019. https://tigerprints.clemson.edu/all_theses/3125.

MLA Handbook (7th Edition):

Kulshrestha, Ankit. “Compressing Deep Neural Networks via Knowledge Distillation.” 2019. Web. 15 Oct 2019.

Vancouver:

Kulshrestha A. Compressing Deep Neural Networks via Knowledge Distillation. [Internet] [Masters thesis]. Clemson University; 2019. [cited 2019 Oct 15]. Available from: https://tigerprints.clemson.edu/all_theses/3125.

Council of Science Editors:

Kulshrestha A. Compressing Deep Neural Networks via Knowledge Distillation. [Masters Thesis]. Clemson University; 2019. Available from: https://tigerprints.clemson.edu/all_theses/3125


Georgia Tech

20. Kundu, Abhijit. Urban 3D Scene Understanding from Images.

Degree: PhD, Interactive Computing, 2018, Georgia Tech

 Human vision is marvelous in obtaining a structured representation of complex dynamic scenes, such as spatial scene-layout, re-organization of the scene into its constituent objects,… (more)

Subjects/Keywords: computer vision; machine learning; inverse graphics

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APA (6th Edition):

Kundu, A. (2018). Urban 3D Scene Understanding from Images. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/61114

Chicago Manual of Style (16th Edition):

Kundu, Abhijit. “Urban 3D Scene Understanding from Images.” 2018. Doctoral Dissertation, Georgia Tech. Accessed October 15, 2019. http://hdl.handle.net/1853/61114.

MLA Handbook (7th Edition):

Kundu, Abhijit. “Urban 3D Scene Understanding from Images.” 2018. Web. 15 Oct 2019.

Vancouver:

Kundu A. Urban 3D Scene Understanding from Images. [Internet] [Doctoral dissertation]. Georgia Tech; 2018. [cited 2019 Oct 15]. Available from: http://hdl.handle.net/1853/61114.

Council of Science Editors:

Kundu A. Urban 3D Scene Understanding from Images. [Doctoral Dissertation]. Georgia Tech; 2018. Available from: http://hdl.handle.net/1853/61114


Rochester Institute of Technology

21. Merkel, Cory E. Design of Neuromemristive Systems for Visual Information Processing.

Degree: PhD, Microsystems Engineering, 2015, Rochester Institute of Technology

  Neuromemristive systems (NMSs) are brain-inspired, adaptive computer architectures based on emerging resistive memory technology (memristors). NMSs adopt a mixed-signal design approach with closely-coupled memory… (more)

Subjects/Keywords: Computer vision; Machine learning; Memristor; Neuromemristive; Neuromorphic

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APA (6th Edition):

Merkel, C. E. (2015). Design of Neuromemristive Systems for Visual Information Processing. (Doctoral Dissertation). Rochester Institute of Technology. Retrieved from https://scholarworks.rit.edu/theses/8938

Chicago Manual of Style (16th Edition):

Merkel, Cory E. “Design of Neuromemristive Systems for Visual Information Processing.” 2015. Doctoral Dissertation, Rochester Institute of Technology. Accessed October 15, 2019. https://scholarworks.rit.edu/theses/8938.

MLA Handbook (7th Edition):

Merkel, Cory E. “Design of Neuromemristive Systems for Visual Information Processing.” 2015. Web. 15 Oct 2019.

Vancouver:

Merkel CE. Design of Neuromemristive Systems for Visual Information Processing. [Internet] [Doctoral dissertation]. Rochester Institute of Technology; 2015. [cited 2019 Oct 15]. Available from: https://scholarworks.rit.edu/theses/8938.

Council of Science Editors:

Merkel CE. Design of Neuromemristive Systems for Visual Information Processing. [Doctoral Dissertation]. Rochester Institute of Technology; 2015. Available from: https://scholarworks.rit.edu/theses/8938


Brigham Young University

22. La, Alex W. Eigenblades: Application of Computer Vision and Machine Learning for Mode Shape Identification.

Degree: MS, 2017, Brigham Young University

 On August 27, 2016, Southwest Airlines flight 3472 from New Orleans to Orlando had to perform an emergency landing when a fan blade separated from… (more)

Subjects/Keywords: Modal Analysis; Computer Vision; PCA; Machine Learning

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APA (6th Edition):

La, A. W. (2017). Eigenblades: Application of Computer Vision and Machine Learning for Mode Shape Identification. (Masters Thesis). Brigham Young University. Retrieved from https://scholarsarchive.byu.edu/cgi/viewcontent.cgi?article=8228&context=etd

Chicago Manual of Style (16th Edition):

La, Alex W. “Eigenblades: Application of Computer Vision and Machine Learning for Mode Shape Identification.” 2017. Masters Thesis, Brigham Young University. Accessed October 15, 2019. https://scholarsarchive.byu.edu/cgi/viewcontent.cgi?article=8228&context=etd.

MLA Handbook (7th Edition):

La, Alex W. “Eigenblades: Application of Computer Vision and Machine Learning for Mode Shape Identification.” 2017. Web. 15 Oct 2019.

Vancouver:

La AW. Eigenblades: Application of Computer Vision and Machine Learning for Mode Shape Identification. [Internet] [Masters thesis]. Brigham Young University; 2017. [cited 2019 Oct 15]. Available from: https://scholarsarchive.byu.edu/cgi/viewcontent.cgi?article=8228&context=etd.

Council of Science Editors:

La AW. Eigenblades: Application of Computer Vision and Machine Learning for Mode Shape Identification. [Masters Thesis]. Brigham Young University; 2017. Available from: https://scholarsarchive.byu.edu/cgi/viewcontent.cgi?article=8228&context=etd


Brigham Young University

23. La, Alex W. Eigenblades: Application of Computer Vision and Machine Learning for Mode Shape Identification.

Degree: MS, 2017, Brigham Young University

 On August 27, 2016, Southwest Airlines flight 3472 from New Orleans to Orlando had to perform an emergency landing when a fan blade separated from… (more)

Subjects/Keywords: Modal Analysis; Computer Vision; PCA; Machine Learning

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APA (6th Edition):

La, A. W. (2017). Eigenblades: Application of Computer Vision and Machine Learning for Mode Shape Identification. (Masters Thesis). Brigham Young University. Retrieved from https://scholarsarchive.byu.edu/cgi/viewcontent.cgi?article=8279&context=etd

Chicago Manual of Style (16th Edition):

La, Alex W. “Eigenblades: Application of Computer Vision and Machine Learning for Mode Shape Identification.” 2017. Masters Thesis, Brigham Young University. Accessed October 15, 2019. https://scholarsarchive.byu.edu/cgi/viewcontent.cgi?article=8279&context=etd.

MLA Handbook (7th Edition):

La, Alex W. “Eigenblades: Application of Computer Vision and Machine Learning for Mode Shape Identification.” 2017. Web. 15 Oct 2019.

Vancouver:

La AW. Eigenblades: Application of Computer Vision and Machine Learning for Mode Shape Identification. [Internet] [Masters thesis]. Brigham Young University; 2017. [cited 2019 Oct 15]. Available from: https://scholarsarchive.byu.edu/cgi/viewcontent.cgi?article=8279&context=etd.

Council of Science Editors:

La AW. Eigenblades: Application of Computer Vision and Machine Learning for Mode Shape Identification. [Masters Thesis]. Brigham Young University; 2017. Available from: https://scholarsarchive.byu.edu/cgi/viewcontent.cgi?article=8279&context=etd


University of Manitoba

24. Gaudreau, Colin. Fast detection of bees using deep learning and bayesian optimization.

Degree: Electrical and Computer Engineering, 2018, University of Manitoba

 In commercial beekeeping, monitoring the apiaries is difficult as they are often spread over large distances. Building a vision-based hive monitoring system is a promising—albeit… (more)

Subjects/Keywords: Computer vision; Object detection; Machine learning; Bees

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APA (6th Edition):

Gaudreau, C. (2018). Fast detection of bees using deep learning and bayesian optimization. (Masters Thesis). University of Manitoba. Retrieved from http://hdl.handle.net/1993/32981

Chicago Manual of Style (16th Edition):

Gaudreau, Colin. “Fast detection of bees using deep learning and bayesian optimization.” 2018. Masters Thesis, University of Manitoba. Accessed October 15, 2019. http://hdl.handle.net/1993/32981.

MLA Handbook (7th Edition):

Gaudreau, Colin. “Fast detection of bees using deep learning and bayesian optimization.” 2018. Web. 15 Oct 2019.

Vancouver:

Gaudreau C. Fast detection of bees using deep learning and bayesian optimization. [Internet] [Masters thesis]. University of Manitoba; 2018. [cited 2019 Oct 15]. Available from: http://hdl.handle.net/1993/32981.

Council of Science Editors:

Gaudreau C. Fast detection of bees using deep learning and bayesian optimization. [Masters Thesis]. University of Manitoba; 2018. Available from: http://hdl.handle.net/1993/32981


University of Missouri – Columbia

25. Gong, Wei. Action recognition via sequence embedding.

Degree: 2011, University of Missouri – Columbia

 [ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] A comb structural exemplar embedding based approach is introduced for action recognition. We propose a… (more)

Subjects/Keywords: computer vision; action recognition; machine learning

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APA (6th Edition):

Gong, W. (2011). Action recognition via sequence embedding. (Thesis). University of Missouri – Columbia. Retrieved from http://hdl.handle.net/10355/14908

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):

Gong, Wei. “Action recognition via sequence embedding.” 2011. Thesis, University of Missouri – Columbia. Accessed October 15, 2019. http://hdl.handle.net/10355/14908.

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

MLA Handbook (7th Edition):

Gong, Wei. “Action recognition via sequence embedding.” 2011. Web. 15 Oct 2019.

Vancouver:

Gong W. Action recognition via sequence embedding. [Internet] [Thesis]. University of Missouri – Columbia; 2011. [cited 2019 Oct 15]. Available from: http://hdl.handle.net/10355/14908.

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

Council of Science Editors:

Gong W. Action recognition via sequence embedding. [Thesis]. University of Missouri – Columbia; 2011. Available from: http://hdl.handle.net/10355/14908

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


University of Saskatchewan

26. Das, Anupama. Prediction of Early Vigor from Overhead Images of Carinata Plants.

Degree: 2019, University of Saskatchewan

 Breeding more resilient, higher yielding crops is an essential component of ensuring ongoing food security. Early season vigor is signi cantly correlated with yields and… (more)

Subjects/Keywords: machine learning; computer vision; carinata; agriculture

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APA (6th Edition):

Das, A. (2019). Prediction of Early Vigor from Overhead Images of Carinata Plants. (Thesis). University of Saskatchewan. Retrieved from http://hdl.handle.net/10388/11925

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):

Das, Anupama. “Prediction of Early Vigor from Overhead Images of Carinata Plants.” 2019. Thesis, University of Saskatchewan. Accessed October 15, 2019. http://hdl.handle.net/10388/11925.

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

MLA Handbook (7th Edition):

Das, Anupama. “Prediction of Early Vigor from Overhead Images of Carinata Plants.” 2019. Web. 15 Oct 2019.

Vancouver:

Das A. Prediction of Early Vigor from Overhead Images of Carinata Plants. [Internet] [Thesis]. University of Saskatchewan; 2019. [cited 2019 Oct 15]. Available from: http://hdl.handle.net/10388/11925.

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

Council of Science Editors:

Das A. Prediction of Early Vigor from Overhead Images of Carinata Plants. [Thesis]. University of Saskatchewan; 2019. Available from: http://hdl.handle.net/10388/11925

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


Rutgers University

27. Saleh, Babak, 1987-. A novel framework for understanding atypical images.

Degree: PhD, Computer Science, 2017, Rutgers University

In the past few years, there has been a tremendous amount of progress in the field of computer vision. As of now, we have reliable… (more)

Subjects/Keywords: Machine learning; Computer vision; Image processing

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APA (6th Edition):

Saleh, Babak, 1. (2017). A novel framework for understanding atypical images. (Doctoral Dissertation). Rutgers University. Retrieved from https://rucore.libraries.rutgers.edu/rutgers-lib/52267/

Chicago Manual of Style (16th Edition):

Saleh, Babak, 1987-. “A novel framework for understanding atypical images.” 2017. Doctoral Dissertation, Rutgers University. Accessed October 15, 2019. https://rucore.libraries.rutgers.edu/rutgers-lib/52267/.

MLA Handbook (7th Edition):

Saleh, Babak, 1987-. “A novel framework for understanding atypical images.” 2017. Web. 15 Oct 2019.

Vancouver:

Saleh, Babak 1. A novel framework for understanding atypical images. [Internet] [Doctoral dissertation]. Rutgers University; 2017. [cited 2019 Oct 15]. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/52267/.

Council of Science Editors:

Saleh, Babak 1. A novel framework for understanding atypical images. [Doctoral Dissertation]. Rutgers University; 2017. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/52267/


University of Bath

28. Beale, Dan. Autonomous visual learning for robotic systems.

Degree: PhD, 2012, University of Bath

 This thesis investigates the problem of visual learning using a robotic platform. Given a set of objects the robots task is to autonomously manipulate, observe,… (more)

Subjects/Keywords: 629.892637; robotics; machine learning; computer vision

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APA (6th Edition):

Beale, D. (2012). Autonomous visual learning for robotic systems. (Doctoral Dissertation). University of Bath. Retrieved from https://researchportal.bath.ac.uk/en/studentthesis/autonomous-visual-learning-for-robotic-systems(9df172f3-52a5-4d0b-9a3e-03fda8b8ffe6).html ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.558886

Chicago Manual of Style (16th Edition):

Beale, Dan. “Autonomous visual learning for robotic systems.” 2012. Doctoral Dissertation, University of Bath. Accessed October 15, 2019. https://researchportal.bath.ac.uk/en/studentthesis/autonomous-visual-learning-for-robotic-systems(9df172f3-52a5-4d0b-9a3e-03fda8b8ffe6).html ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.558886.

MLA Handbook (7th Edition):

Beale, Dan. “Autonomous visual learning for robotic systems.” 2012. Web. 15 Oct 2019.

Vancouver:

Beale D. Autonomous visual learning for robotic systems. [Internet] [Doctoral dissertation]. University of Bath; 2012. [cited 2019 Oct 15]. Available from: https://researchportal.bath.ac.uk/en/studentthesis/autonomous-visual-learning-for-robotic-systems(9df172f3-52a5-4d0b-9a3e-03fda8b8ffe6).html ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.558886.

Council of Science Editors:

Beale D. Autonomous visual learning for robotic systems. [Doctoral Dissertation]. University of Bath; 2012. Available from: https://researchportal.bath.ac.uk/en/studentthesis/autonomous-visual-learning-for-robotic-systems(9df172f3-52a5-4d0b-9a3e-03fda8b8ffe6).html ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.558886


Princeton University

29. Tai, Cheng. Multi-scale adaptive representation of signals: models and algorithms .

Degree: PhD, 2016, Princeton University

 Representations of data play a key role in many signal processing and machine learning applications. For low-level signal processing tasks, dictionary learning is a very… (more)

Subjects/Keywords: computer vision; machine learning; signal processing

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APA (6th Edition):

Tai, C. (2016). Multi-scale adaptive representation of signals: models and algorithms . (Doctoral Dissertation). Princeton University. Retrieved from http://arks.princeton.edu/ark:/88435/dsp01jm214r56n

Chicago Manual of Style (16th Edition):

Tai, Cheng. “Multi-scale adaptive representation of signals: models and algorithms .” 2016. Doctoral Dissertation, Princeton University. Accessed October 15, 2019. http://arks.princeton.edu/ark:/88435/dsp01jm214r56n.

MLA Handbook (7th Edition):

Tai, Cheng. “Multi-scale adaptive representation of signals: models and algorithms .” 2016. Web. 15 Oct 2019.

Vancouver:

Tai C. Multi-scale adaptive representation of signals: models and algorithms . [Internet] [Doctoral dissertation]. Princeton University; 2016. [cited 2019 Oct 15]. Available from: http://arks.princeton.edu/ark:/88435/dsp01jm214r56n.

Council of Science Editors:

Tai C. Multi-scale adaptive representation of signals: models and algorithms . [Doctoral Dissertation]. Princeton University; 2016. Available from: http://arks.princeton.edu/ark:/88435/dsp01jm214r56n


Princeton University

30. Debats, Stephanie Renee. Mapping Sub-Saharan African Agriculture in High-Resolution Satellite Imagery with Computer Vision & Machine Learning .

Degree: PhD, 2017, Princeton University

 Smallholder farms dominate in many parts of the world, including Sub-Saharan Africa. These systems are characterized by small, heterogeneous, and often indistinct field patterns, requiring… (more)

Subjects/Keywords: agriculture; computer vision; landcover; machine learning

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APA (6th Edition):

Debats, S. R. (2017). Mapping Sub-Saharan African Agriculture in High-Resolution Satellite Imagery with Computer Vision & Machine Learning . (Doctoral Dissertation). Princeton University. Retrieved from http://arks.princeton.edu/ark:/88435/dsp01rf55zb30f

Chicago Manual of Style (16th Edition):

Debats, Stephanie Renee. “Mapping Sub-Saharan African Agriculture in High-Resolution Satellite Imagery with Computer Vision & Machine Learning .” 2017. Doctoral Dissertation, Princeton University. Accessed October 15, 2019. http://arks.princeton.edu/ark:/88435/dsp01rf55zb30f.

MLA Handbook (7th Edition):

Debats, Stephanie Renee. “Mapping Sub-Saharan African Agriculture in High-Resolution Satellite Imagery with Computer Vision & Machine Learning .” 2017. Web. 15 Oct 2019.

Vancouver:

Debats SR. Mapping Sub-Saharan African Agriculture in High-Resolution Satellite Imagery with Computer Vision & Machine Learning . [Internet] [Doctoral dissertation]. Princeton University; 2017. [cited 2019 Oct 15]. Available from: http://arks.princeton.edu/ark:/88435/dsp01rf55zb30f.

Council of Science Editors:

Debats SR. Mapping Sub-Saharan African Agriculture in High-Resolution Satellite Imagery with Computer Vision & Machine Learning . [Doctoral Dissertation]. Princeton University; 2017. Available from: http://arks.princeton.edu/ark:/88435/dsp01rf55zb30f

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