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University: University of California – San Diego

You searched for subject:(Computer vision AND machine learning applications ). Showing records 1 – 11 of 11 total matches.

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University of California – San Diego

1. 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 November 11, 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. 11 Nov 2019.

Vancouver:

Christiansen EM. From local descriptors to in silico labeling. [Internet] [Thesis]. University of California – San Diego; 2018. [cited 2019 Nov 11]. 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


University of California – San Diego

2. Lee, Chen-Yu. Enhanced Convolutional Neural Networks and Their Application to Photo Optical Character Recognition.

Degree: Electrical Engineering (Signal and Image Proc), 2016, University of California – San Diego

 This thesis presents two principled approaches to improve the performance of convolutional neural networks on visual recognition and demonstrates the effectiveness of CNNs on optical… (more)

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

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

APA (6th Edition):

Lee, C. (2016). Enhanced Convolutional Neural Networks and Their Application to Photo Optical Character Recognition. (Thesis). University of California – San Diego. Retrieved from http://www.escholarship.org/uc/item/7bb1f3p2

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):

Lee, Chen-Yu. “Enhanced Convolutional Neural Networks and Their Application to Photo Optical Character Recognition.” 2016. Thesis, University of California – San Diego. Accessed November 11, 2019. http://www.escholarship.org/uc/item/7bb1f3p2.

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

MLA Handbook (7th Edition):

Lee, Chen-Yu. “Enhanced Convolutional Neural Networks and Their Application to Photo Optical Character Recognition.” 2016. Web. 11 Nov 2019.

Vancouver:

Lee C. Enhanced Convolutional Neural Networks and Their Application to Photo Optical Character Recognition. [Internet] [Thesis]. University of California – San Diego; 2016. [cited 2019 Nov 11]. Available from: http://www.escholarship.org/uc/item/7bb1f3p2.

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

Council of Science Editors:

Lee C. Enhanced Convolutional Neural Networks and Their Application to Photo Optical Character Recognition. [Thesis]. University of California – San Diego; 2016. Available from: http://www.escholarship.org/uc/item/7bb1f3p2

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


University of California – San Diego

3. Lu, Yongxi. Adaptive Computations and Model Structures in Object and Scene Understanding Systems.

Degree: Electrical and Computer Engineering, 2019, University of California – San Diego

 This thesis presents a set of novel algorithms that address practical limitations in existing object and scene understanding systems. These limitations include high computational demands… (more)

Subjects/Keywords: Artificial intelligence; Computer science; Computer Vision; Machine Learning; Pattern recognition

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APA (6th Edition):

Lu, Y. (2019). Adaptive Computations and Model Structures in Object and Scene Understanding Systems. (Thesis). University of California – San Diego. Retrieved from http://www.escholarship.org/uc/item/1cg7n6zv

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):

Lu, Yongxi. “Adaptive Computations and Model Structures in Object and Scene Understanding Systems.” 2019. Thesis, University of California – San Diego. Accessed November 11, 2019. http://www.escholarship.org/uc/item/1cg7n6zv.

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

MLA Handbook (7th Edition):

Lu, Yongxi. “Adaptive Computations and Model Structures in Object and Scene Understanding Systems.” 2019. Web. 11 Nov 2019.

Vancouver:

Lu Y. Adaptive Computations and Model Structures in Object and Scene Understanding Systems. [Internet] [Thesis]. University of California – San Diego; 2019. [cited 2019 Nov 11]. Available from: http://www.escholarship.org/uc/item/1cg7n6zv.

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

Council of Science Editors:

Lu Y. Adaptive Computations and Model Structures in Object and Scene Understanding Systems. [Thesis]. University of California – San Diego; 2019. Available from: http://www.escholarship.org/uc/item/1cg7n6zv

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


University of California – San Diego

4. Epperson, Matthew. Empowering Conservation through Deep Convolutional Neural Networks and Unmanned Aerial Systems.

Degree: Electrical Engineering (Intelsys, Robotics and Cont), 2018, University of California – San Diego

 Tropical rainforests worldwide are negatively impacted from a variety of human-caused threats. Unfortunately, our ability to study these rainforests is impeded by logistical problems such… (more)

Subjects/Keywords: Computer science; Electrical engineering; Wildlife conservation; CNN; computer vision; conservation; deep learning; ecology; machine learning

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APA (6th Edition):

Epperson, M. (2018). Empowering Conservation through Deep Convolutional Neural Networks and Unmanned Aerial Systems. (Thesis). University of California – San Diego. Retrieved from http://www.escholarship.org/uc/item/5rh083rc

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):

Epperson, Matthew. “Empowering Conservation through Deep Convolutional Neural Networks and Unmanned Aerial Systems.” 2018. Thesis, University of California – San Diego. Accessed November 11, 2019. http://www.escholarship.org/uc/item/5rh083rc.

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

MLA Handbook (7th Edition):

Epperson, Matthew. “Empowering Conservation through Deep Convolutional Neural Networks and Unmanned Aerial Systems.” 2018. Web. 11 Nov 2019.

Vancouver:

Epperson M. Empowering Conservation through Deep Convolutional Neural Networks and Unmanned Aerial Systems. [Internet] [Thesis]. University of California – San Diego; 2018. [cited 2019 Nov 11]. Available from: http://www.escholarship.org/uc/item/5rh083rc.

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

Council of Science Editors:

Epperson M. Empowering Conservation through Deep Convolutional Neural Networks and Unmanned Aerial Systems. [Thesis]. University of California – San Diego; 2018. Available from: http://www.escholarship.org/uc/item/5rh083rc

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


University of California – San Diego

5. Vartanians, Dalar. Vehicle Classification and Identification of Salient Information in Images.

Degree: Electrical Engineering (Intelsys, Robotics and Cont), 2016, University of California – San Diego

 Vehicle classification is currently a widely implemented component in intelligent vehicles, surveillance systems, and traffic monitoring. The major component of vehicle classification is to learn… (more)

Subjects/Keywords: Electrical engineering; computer vision; image processing; machine learning

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APA (6th Edition):

Vartanians, D. (2016). Vehicle Classification and Identification of Salient Information in Images. (Thesis). University of California – San Diego. Retrieved from http://www.escholarship.org/uc/item/7jj0784m

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):

Vartanians, Dalar. “Vehicle Classification and Identification of Salient Information in Images.” 2016. Thesis, University of California – San Diego. Accessed November 11, 2019. http://www.escholarship.org/uc/item/7jj0784m.

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

MLA Handbook (7th Edition):

Vartanians, Dalar. “Vehicle Classification and Identification of Salient Information in Images.” 2016. Web. 11 Nov 2019.

Vancouver:

Vartanians D. Vehicle Classification and Identification of Salient Information in Images. [Internet] [Thesis]. University of California – San Diego; 2016. [cited 2019 Nov 11]. Available from: http://www.escholarship.org/uc/item/7jj0784m.

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

Council of Science Editors:

Vartanians D. Vehicle Classification and Identification of Salient Information in Images. [Thesis]. University of California – San Diego; 2016. Available from: http://www.escholarship.org/uc/item/7jj0784m

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


University of California – San Diego

6. Li, Weixin. Visual Understanding of Complex Human Behavior via Attribute Dynamics.

Degree: Electrical Engineering (Signal and Image Proc), 2016, University of California – San Diego

 Visual understanding of human behavior in video sequences is one of the fundamental topics in computational vision. Being a sequential signal by nature, most critical… (more)

Subjects/Keywords: Computer science; Action Recognition; Artificial Intelligence; Computer Vision; Dynamic Bayesian Networks; Machine Learning; Variational Inference

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

APA (6th Edition):

Li, W. (2016). Visual Understanding of Complex Human Behavior via Attribute Dynamics. (Thesis). University of California – San Diego. Retrieved from http://www.escholarship.org/uc/item/0sm5h465

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, Weixin. “Visual Understanding of Complex Human Behavior via Attribute Dynamics.” 2016. Thesis, University of California – San Diego. Accessed November 11, 2019. http://www.escholarship.org/uc/item/0sm5h465.

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

MLA Handbook (7th Edition):

Li, Weixin. “Visual Understanding of Complex Human Behavior via Attribute Dynamics.” 2016. Web. 11 Nov 2019.

Vancouver:

Li W. Visual Understanding of Complex Human Behavior via Attribute Dynamics. [Internet] [Thesis]. University of California – San Diego; 2016. [cited 2019 Nov 11]. Available from: http://www.escholarship.org/uc/item/0sm5h465.

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

Council of Science Editors:

Li W. Visual Understanding of Complex Human Behavior via Attribute Dynamics. [Thesis]. University of California – San Diego; 2016. Available from: http://www.escholarship.org/uc/item/0sm5h465

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


University of California – San Diego

7. Wang, Panqu. Towards The Deep Model : Understanding Visual Recognition Through Computational Models.

Degree: Electrical Engineering (Signal and Image Proc), 2017, University of California – San Diego

 Understanding how visual recognition is achieved in the human brain is one of the most fundamental questions in vision research. In this thesis I seek… (more)

Subjects/Keywords: Computer science; Cognitive psychology; Electrical engineering; computational modeling; deep learning; machine learning; neural network; object recognition; vision

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

APA (6th Edition):

Wang, P. (2017). Towards The Deep Model : Understanding Visual Recognition Through Computational Models. (Thesis). University of California – San Diego. Retrieved from http://www.escholarship.org/uc/item/5fc8b0pc

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, Panqu. “Towards The Deep Model : Understanding Visual Recognition Through Computational Models.” 2017. Thesis, University of California – San Diego. Accessed November 11, 2019. http://www.escholarship.org/uc/item/5fc8b0pc.

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

MLA Handbook (7th Edition):

Wang, Panqu. “Towards The Deep Model : Understanding Visual Recognition Through Computational Models.” 2017. Web. 11 Nov 2019.

Vancouver:

Wang P. Towards The Deep Model : Understanding Visual Recognition Through Computational Models. [Internet] [Thesis]. University of California – San Diego; 2017. [cited 2019 Nov 11]. Available from: http://www.escholarship.org/uc/item/5fc8b0pc.

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

Council of Science Editors:

Wang P. Towards The Deep Model : Understanding Visual Recognition Through Computational Models. [Thesis]. University of California – San Diego; 2017. Available from: http://www.escholarship.org/uc/item/5fc8b0pc

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


University of California – San Diego

8. Marks, Tim Kalman. Facing uncertainty : 3D face tracking and learning with generative models.

Degree: 2006, University of California – San Diego

 We present a generative graphical model and stochastic filtering algorithm for simultaneous tracking of 3D rigid and nonrigid motion, object texture, and background texture from… (more)

Subjects/Keywords: Computer vision; Machine learning; Human face recognition (Computer science); Cognitive psychology; Computer simulation

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APA (6th Edition):

Marks, T. K. (2006). Facing uncertainty : 3D face tracking and learning with generative models. (Thesis). University of California – San Diego. Retrieved from http://www.escholarship.org/uc/item/5327s8v6

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):

Marks, Tim Kalman. “Facing uncertainty : 3D face tracking and learning with generative models.” 2006. Thesis, University of California – San Diego. Accessed November 11, 2019. http://www.escholarship.org/uc/item/5327s8v6.

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

MLA Handbook (7th Edition):

Marks, Tim Kalman. “Facing uncertainty : 3D face tracking and learning with generative models.” 2006. Web. 11 Nov 2019.

Vancouver:

Marks TK. Facing uncertainty : 3D face tracking and learning with generative models. [Internet] [Thesis]. University of California – San Diego; 2006. [cited 2019 Nov 11]. Available from: http://www.escholarship.org/uc/item/5327s8v6.

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

Council of Science Editors:

Marks TK. Facing uncertainty : 3D face tracking and learning with generative models. [Thesis]. University of California – San Diego; 2006. Available from: http://www.escholarship.org/uc/item/5327s8v6

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


University of California – San Diego

9. Fasel, Ian Robert. Learning real-time object detectors : probabilistic generative approaches.

Degree: 2006, University of California – San Diego

 This dissertation is a computational investigation of the task of locating and recognizing objects in unconstrained images in real-time, and learning to do so with… (more)

Subjects/Keywords: Human face recognition (Computer science); Machine learning; Computer vision; Robotics; Cognitive psychology

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

APA (6th Edition):

Fasel, I. R. (2006). Learning real-time object detectors : probabilistic generative approaches. (Thesis). University of California – San Diego. Retrieved from http://www.escholarship.org/uc/item/0xk2d98k

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):

Fasel, Ian Robert. “Learning real-time object detectors : probabilistic generative approaches.” 2006. Thesis, University of California – San Diego. Accessed November 11, 2019. http://www.escholarship.org/uc/item/0xk2d98k.

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

MLA Handbook (7th Edition):

Fasel, Ian Robert. “Learning real-time object detectors : probabilistic generative approaches.” 2006. Web. 11 Nov 2019.

Vancouver:

Fasel IR. Learning real-time object detectors : probabilistic generative approaches. [Internet] [Thesis]. University of California – San Diego; 2006. [cited 2019 Nov 11]. Available from: http://www.escholarship.org/uc/item/0xk2d98k.

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

Council of Science Editors:

Fasel IR. Learning real-time object detectors : probabilistic generative approaches. [Thesis]. University of California – San Diego; 2006. Available from: http://www.escholarship.org/uc/item/0xk2d98k

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


University of California – San Diego

10. Butko, Nicholas J. Active perception.

Degree: Cognitive science, 2010, University of California – San Diego

 Action is indelibly tied to perception, and good perception is vital to our survival. Action helps to inform organisms about the world they inhabit, and… (more)

Subjects/Keywords: Computer vision Research; Machine theory Research; Artificial intelligence; Real-time data processing; Data processing Vision; Mathematical models Vision

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APA (6th Edition):

Butko, N. J. (2010). Active perception. (Thesis). University of California – San Diego. Retrieved from http://www.escholarship.org/uc/item/1446v02h

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):

Butko, Nicholas J. “Active perception.” 2010. Thesis, University of California – San Diego. Accessed November 11, 2019. http://www.escholarship.org/uc/item/1446v02h.

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

MLA Handbook (7th Edition):

Butko, Nicholas J. “Active perception.” 2010. Web. 11 Nov 2019.

Vancouver:

Butko NJ. Active perception. [Internet] [Thesis]. University of California – San Diego; 2010. [cited 2019 Nov 11]. Available from: http://www.escholarship.org/uc/item/1446v02h.

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

Council of Science Editors:

Butko NJ. Active perception. [Thesis]. University of California – San Diego; 2010. Available from: http://www.escholarship.org/uc/item/1446v02h

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

11. Sikka, Karan. Latent Dynamic Space-Time Volumes for Predicting Human Facial Behavior in Videos.

Degree: Electrical Engineering (Intelsys, Robotics and Cont), 2016, University of California – San Diego

 Enabling machines to understand non-verbal facial behavior from visual data is crucial for building smart interactive systems. This thesis focusses on human behavior analysis in… (more)

Subjects/Keywords: Electrical engineering; Computer science; Robotics; Computer Vision; Facial behavior analysis; Machine Learning; Supervised Learning; Video classification

…related to emotions using machine learning (ML) and computer vision [60]… …technical expertise and understanding of computer vision, and hope to keep working with him in the… …reprint of the material as it appears in Computer Vision and Pattern Recognition Workshops 2015… …the material as it appears in British Machine and Vision Conference 2015. Sikka, Karan; Giri… …in Computer Vision and Pattern Recognition Conference 2016. Sikka, Karan; Sharma, Gaurav… 

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

APA (6th Edition):

Sikka, K. (2016). Latent Dynamic Space-Time Volumes for Predicting Human Facial Behavior in Videos. (Thesis). University of California – San Diego. Retrieved from http://www.escholarship.org/uc/item/9713p3nd

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):

Sikka, Karan. “Latent Dynamic Space-Time Volumes for Predicting Human Facial Behavior in Videos.” 2016. Thesis, University of California – San Diego. Accessed November 11, 2019. http://www.escholarship.org/uc/item/9713p3nd.

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

MLA Handbook (7th Edition):

Sikka, Karan. “Latent Dynamic Space-Time Volumes for Predicting Human Facial Behavior in Videos.” 2016. Web. 11 Nov 2019.

Vancouver:

Sikka K. Latent Dynamic Space-Time Volumes for Predicting Human Facial Behavior in Videos. [Internet] [Thesis]. University of California – San Diego; 2016. [cited 2019 Nov 11]. Available from: http://www.escholarship.org/uc/item/9713p3nd.

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

Council of Science Editors:

Sikka K. Latent Dynamic Space-Time Volumes for Predicting Human Facial Behavior in Videos. [Thesis]. University of California – San Diego; 2016. Available from: http://www.escholarship.org/uc/item/9713p3nd

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

.