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You searched for +publisher:"University of Illinois – Urbana-Champaign" +contributor:("Huang, Thomas S."). Showing records 1 – 30 of 71 total matches.

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University of Illinois – Urbana-Champaign

1. Ghauri, Ahsan. Fast superresolution based on a network structure trained using sparse coding.

Degree: MS, 1200, 2012, University of Illinois – Urbana-Champaign

 In this thesis I present a novel approach to superresolution using a network structure. Sparse representation of image signals forms the cornerstone of our approach… (more)

Subjects/Keywords: Superresolution; sparse representation; regularization; optimization and dictionary

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

APA (6th Edition):

Ghauri, A. (2012). Fast superresolution based on a network structure trained using sparse coding. (Thesis). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/34532

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):

Ghauri, Ahsan. “Fast superresolution based on a network structure trained using sparse coding.” 2012. Thesis, University of Illinois – Urbana-Champaign. Accessed April 18, 2021. http://hdl.handle.net/2142/34532.

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

MLA Handbook (7th Edition):

Ghauri, Ahsan. “Fast superresolution based on a network structure trained using sparse coding.” 2012. Web. 18 Apr 2021.

Vancouver:

Ghauri A. Fast superresolution based on a network structure trained using sparse coding. [Internet] [Thesis]. University of Illinois – Urbana-Champaign; 2012. [cited 2021 Apr 18]. Available from: http://hdl.handle.net/2142/34532.

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

Council of Science Editors:

Ghauri A. Fast superresolution based on a network structure trained using sparse coding. [Thesis]. University of Illinois – Urbana-Champaign; 2012. Available from: http://hdl.handle.net/2142/34532

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


University of Illinois – Urbana-Champaign

2. Chang, Shiyu. Structured concept recycling by probabilistic logic ontology tree.

Degree: MS, 1200, 2014, University of Illinois – Urbana-Champaign

 Recent advances in multimedia research have generated a large collection of concept models, e.g., LSCOM and Mediamill 101, which have become accessible to other researchers.… (more)

Subjects/Keywords: Multimedia LEarning structured model by probabilistic loGic Ontology (LEGO); Concept recycling; Model warehouse; Probabilistic logic ontology tree; Logical operations

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APA (6th Edition):

Chang, S. (2014). Structured concept recycling by probabilistic logic ontology tree. (Thesis). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/46848

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):

Chang, Shiyu. “Structured concept recycling by probabilistic logic ontology tree.” 2014. Thesis, University of Illinois – Urbana-Champaign. Accessed April 18, 2021. http://hdl.handle.net/2142/46848.

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

MLA Handbook (7th Edition):

Chang, Shiyu. “Structured concept recycling by probabilistic logic ontology tree.” 2014. Web. 18 Apr 2021.

Vancouver:

Chang S. Structured concept recycling by probabilistic logic ontology tree. [Internet] [Thesis]. University of Illinois – Urbana-Champaign; 2014. [cited 2021 Apr 18]. Available from: http://hdl.handle.net/2142/46848.

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

Council of Science Editors:

Chang S. Structured concept recycling by probabilistic logic ontology tree. [Thesis]. University of Illinois – Urbana-Champaign; 2014. Available from: http://hdl.handle.net/2142/46848

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


University of Illinois – Urbana-Champaign

3. Khorrami, Pooya. Fully automatic vision-based system for vehicle crash prediction and recognition.

Degree: MS, 1200, 2014, University of Illinois – Urbana-Champaign

 Just as they were half a century ago, automobile accidents are, unfortunately, one of the leading causes of death today. Therefore, it is no surprise… (more)

Subjects/Keywords: Vision-Based Surveillance System; Robust Principal Component Analysis (PCA); Kalman Filter; Accident Prediction; Accident Recognition; Background Subtraction; Object Tracking

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APA (6th Edition):

Khorrami, P. (2014). Fully automatic vision-based system for vehicle crash prediction and recognition. (Thesis). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/46867

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):

Khorrami, Pooya. “Fully automatic vision-based system for vehicle crash prediction and recognition.” 2014. Thesis, University of Illinois – Urbana-Champaign. Accessed April 18, 2021. http://hdl.handle.net/2142/46867.

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

MLA Handbook (7th Edition):

Khorrami, Pooya. “Fully automatic vision-based system for vehicle crash prediction and recognition.” 2014. Web. 18 Apr 2021.

Vancouver:

Khorrami P. Fully automatic vision-based system for vehicle crash prediction and recognition. [Internet] [Thesis]. University of Illinois – Urbana-Champaign; 2014. [cited 2021 Apr 18]. Available from: http://hdl.handle.net/2142/46867.

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

Council of Science Editors:

Khorrami P. Fully automatic vision-based system for vehicle crash prediction and recognition. [Thesis]. University of Illinois – Urbana-Champaign; 2014. Available from: http://hdl.handle.net/2142/46867

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


University of Illinois – Urbana-Champaign

4. Shi, Honghui. Galaxy classification with deep convolutional neural networks.

Degree: MS, Electrical & Computer Engr, 2016, University of Illinois – Urbana-Champaign

 Galaxy classification, using digital images captured from sky surveys to determine the galaxy morphological classes, is of great interest to astronomy researchers. Conventional methods rely… (more)

Subjects/Keywords: Galaxy Classification; Convolutional Neural Networks

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APA (6th Edition):

Shi, H. (2016). Galaxy classification with deep convolutional neural networks. (Thesis). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/90939

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):

Shi, Honghui. “Galaxy classification with deep convolutional neural networks.” 2016. Thesis, University of Illinois – Urbana-Champaign. Accessed April 18, 2021. http://hdl.handle.net/2142/90939.

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

MLA Handbook (7th Edition):

Shi, Honghui. “Galaxy classification with deep convolutional neural networks.” 2016. Web. 18 Apr 2021.

Vancouver:

Shi H. Galaxy classification with deep convolutional neural networks. [Internet] [Thesis]. University of Illinois – Urbana-Champaign; 2016. [cited 2021 Apr 18]. Available from: http://hdl.handle.net/2142/90939.

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

Council of Science Editors:

Shi H. Galaxy classification with deep convolutional neural networks. [Thesis]. University of Illinois – Urbana-Champaign; 2016. Available from: http://hdl.handle.net/2142/90939

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


University of Illinois – Urbana-Champaign

5. Gu, Kuangxiao. Improving coarticulation performance of 3D avatar and gaze estimation using RGB webcam.

Degree: MS, Electrical & Computer Engr, 2016, University of Illinois – Urbana-Champaign

 This thesis explores two applications of computer vision in psychology-related studies: enhanced patient portal messages using 3D avatar and gaze estimation using a single RGB… (more)

Subjects/Keywords: 3D avatar; Gaze estimation; RGB camera

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APA (6th Edition):

Gu, K. (2016). Improving coarticulation performance of 3D avatar and gaze estimation using RGB webcam. (Thesis). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/93004

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):

Gu, Kuangxiao. “Improving coarticulation performance of 3D avatar and gaze estimation using RGB webcam.” 2016. Thesis, University of Illinois – Urbana-Champaign. Accessed April 18, 2021. http://hdl.handle.net/2142/93004.

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

MLA Handbook (7th Edition):

Gu, Kuangxiao. “Improving coarticulation performance of 3D avatar and gaze estimation using RGB webcam.” 2016. Web. 18 Apr 2021.

Vancouver:

Gu K. Improving coarticulation performance of 3D avatar and gaze estimation using RGB webcam. [Internet] [Thesis]. University of Illinois – Urbana-Champaign; 2016. [cited 2021 Apr 18]. Available from: http://hdl.handle.net/2142/93004.

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

Council of Science Editors:

Gu K. Improving coarticulation performance of 3D avatar and gaze estimation using RGB webcam. [Thesis]. University of Illinois – Urbana-Champaign; 2016. Available from: http://hdl.handle.net/2142/93004

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


University of Illinois – Urbana-Champaign

6. Yu, Jiahui. Towards efficient, on-demand and automated deep learning.

Degree: PhD, Electrical & Computer Engr, 2020, University of Illinois – Urbana-Champaign

 In the past decade, deep learning has achieved great breakthroughs on tasks of computer vision, speech, language, control and many others. The advanced and dedicated… (more)

Subjects/Keywords: efficient; on-demand; automated; deep learning; automl

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APA (6th Edition):

Yu, J. (2020). Towards efficient, on-demand and automated deep learning. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/107845

Chicago Manual of Style (16th Edition):

Yu, Jiahui. “Towards efficient, on-demand and automated deep learning.” 2020. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed April 18, 2021. http://hdl.handle.net/2142/107845.

MLA Handbook (7th Edition):

Yu, Jiahui. “Towards efficient, on-demand and automated deep learning.” 2020. Web. 18 Apr 2021.

Vancouver:

Yu J. Towards efficient, on-demand and automated deep learning. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2020. [cited 2021 Apr 18]. Available from: http://hdl.handle.net/2142/107845.

Council of Science Editors:

Yu J. Towards efficient, on-demand and automated deep learning. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2020. Available from: http://hdl.handle.net/2142/107845


University of Illinois – Urbana-Champaign

7. Chu, Xinqi. Layout-aware mixture models for patch-based image representation and analysis.

Degree: PhD, 1200, 2015, University of Illinois – Urbana-Champaign

 Image and video representation and modeling is an important topic in computer vision and image processing. An image model provides an abstraction of the large… (more)

Subjects/Keywords: computer vision; image recognition; image reconstruction; layout modelling image representation; discriminative models; generative models; latent variable models; colorization; joint detection and recognition; Expectation Maximization (EM)-Learning; image representation

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APA (6th Edition):

Chu, X. (2015). Layout-aware mixture models for patch-based image representation and analysis. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/72973

Chicago Manual of Style (16th Edition):

Chu, Xinqi. “Layout-aware mixture models for patch-based image representation and analysis.” 2015. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed April 18, 2021. http://hdl.handle.net/2142/72973.

MLA Handbook (7th Edition):

Chu, Xinqi. “Layout-aware mixture models for patch-based image representation and analysis.” 2015. Web. 18 Apr 2021.

Vancouver:

Chu X. Layout-aware mixture models for patch-based image representation and analysis. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2015. [cited 2021 Apr 18]. Available from: http://hdl.handle.net/2142/72973.

Council of Science Editors:

Chu X. Layout-aware mixture models for patch-based image representation and analysis. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2015. Available from: http://hdl.handle.net/2142/72973


University of Illinois – Urbana-Champaign

8. Dikmen, Mert. Visual detection and recognition using local features.

Degree: PhD, 1200, 2012, University of Illinois – Urbana-Champaign

 Detection and recognition of objects in images is one of the most impor- tant problems in computer vision. In this thesis we adhere to a… (more)

Subjects/Keywords: Computer Vision; Image Representation; Object Detection; Object Recognition; Parallel Programming; GPU Programming; graphics processing unit (GPU)

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APA (6th Edition):

Dikmen, M. (2012). Visual detection and recognition using local features. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/32069

Chicago Manual of Style (16th Edition):

Dikmen, Mert. “Visual detection and recognition using local features.” 2012. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed April 18, 2021. http://hdl.handle.net/2142/32069.

MLA Handbook (7th Edition):

Dikmen, Mert. “Visual detection and recognition using local features.” 2012. Web. 18 Apr 2021.

Vancouver:

Dikmen M. Visual detection and recognition using local features. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2012. [cited 2021 Apr 18]. Available from: http://hdl.handle.net/2142/32069.

Council of Science Editors:

Dikmen M. Visual detection and recognition using local features. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2012. Available from: http://hdl.handle.net/2142/32069


University of Illinois – Urbana-Champaign

9. Shi, Honghui. Deep learning in sequential data analysis.

Degree: PhD, Electrical & Computer Engr, 2017, University of Illinois – Urbana-Champaign

 Deep learning has achieved great success in recent years in computer vision and its related areas. For core computer vision tasks such as image classification,… (more)

Subjects/Keywords: Deep learning; Sequential data analysis; Visual recognition; Video object detection; Video object tracking

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APA (6th Edition):

Shi, H. (2017). Deep learning in sequential data analysis. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/99513

Chicago Manual of Style (16th Edition):

Shi, Honghui. “Deep learning in sequential data analysis.” 2017. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed April 18, 2021. http://hdl.handle.net/2142/99513.

MLA Handbook (7th Edition):

Shi, Honghui. “Deep learning in sequential data analysis.” 2017. Web. 18 Apr 2021.

Vancouver:

Shi H. Deep learning in sequential data analysis. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2017. [cited 2021 Apr 18]. Available from: http://hdl.handle.net/2142/99513.

Council of Science Editors:

Shi H. Deep learning in sequential data analysis. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2017. Available from: http://hdl.handle.net/2142/99513


University of Illinois – Urbana-Champaign

10. Li, Zhen. Generative and discriminative models for person verification and efficient search.

Degree: PhD, 1200, 2013, University of Illinois – Urbana-Champaign

 This dissertation studies the person verification problem in modern surveillance and video retrieval systems. The problem is to identify whether a pair of face or… (more)

Subjects/Keywords: Person Verification; Efficient Person Search

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APA (6th Edition):

Li, Z. (2013). Generative and discriminative models for person verification and efficient search. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/44478

Chicago Manual of Style (16th Edition):

Li, Zhen. “Generative and discriminative models for person verification and efficient search.” 2013. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed April 18, 2021. http://hdl.handle.net/2142/44478.

MLA Handbook (7th Edition):

Li, Zhen. “Generative and discriminative models for person verification and efficient search.” 2013. Web. 18 Apr 2021.

Vancouver:

Li Z. Generative and discriminative models for person verification and efficient search. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2013. [cited 2021 Apr 18]. Available from: http://hdl.handle.net/2142/44478.

Council of Science Editors:

Li Z. Generative and discriminative models for person verification and efficient search. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2013. Available from: http://hdl.handle.net/2142/44478


University of Illinois – Urbana-Champaign

11. Tsai, Min-Hsuan. On recommendations in heterogeneous social media networks.

Degree: PhD, 1200, 2014, University of Illinois – Urbana-Champaign

 In this dissertation, we study the problem of social media recommendations with a heavy emphasis on exploiting social, content and contextual information. The problem of… (more)

Subjects/Keywords: heterogeneous network; recommendations

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APA (6th Edition):

Tsai, M. (2014). On recommendations in heterogeneous social media networks. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/46830

Chicago Manual of Style (16th Edition):

Tsai, Min-Hsuan. “On recommendations in heterogeneous social media networks.” 2014. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed April 18, 2021. http://hdl.handle.net/2142/46830.

MLA Handbook (7th Edition):

Tsai, Min-Hsuan. “On recommendations in heterogeneous social media networks.” 2014. Web. 18 Apr 2021.

Vancouver:

Tsai M. On recommendations in heterogeneous social media networks. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2014. [cited 2021 Apr 18]. Available from: http://hdl.handle.net/2142/46830.

Council of Science Editors:

Tsai M. On recommendations in heterogeneous social media networks. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2014. Available from: http://hdl.handle.net/2142/46830


University of Illinois – Urbana-Champaign

12. Qi, Guo-Jun. Information trust, inference and transfer in social and information networks.

Degree: PhD, 1200, 2014, University of Illinois – Urbana-Champaign

 In this thesis, our overarching goal is to aggregate crowdsourced information that is collected from computing systems based on social networks and represented in information… (more)

Subjects/Keywords: information trust; information inference; information transfer; information networks; social networks

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APA (6th Edition):

Qi, G. (2014). Information trust, inference and transfer in social and information networks. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/46854

Chicago Manual of Style (16th Edition):

Qi, Guo-Jun. “Information trust, inference and transfer in social and information networks.” 2014. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed April 18, 2021. http://hdl.handle.net/2142/46854.

MLA Handbook (7th Edition):

Qi, Guo-Jun. “Information trust, inference and transfer in social and information networks.” 2014. Web. 18 Apr 2021.

Vancouver:

Qi G. Information trust, inference and transfer in social and information networks. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2014. [cited 2021 Apr 18]. Available from: http://hdl.handle.net/2142/46854.

Council of Science Editors:

Qi G. Information trust, inference and transfer in social and information networks. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2014. Available from: http://hdl.handle.net/2142/46854


University of Illinois – Urbana-Champaign

13. Tang, Hao. One-vector representations of stochastic signals for pattern recognition.

Degree: PhD, 1200, 2011, University of Illinois – Urbana-Champaign

 When building a pattern recognition system, we primarily deal with stochastic signals such as speech, image, video, and so forth. Often, a stochastic signal is… (more)

Subjects/Keywords: Pattern Recognition; Stochastic Signal; One-Vector Representation; Hidden Markov Model

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APA (6th Edition):

Tang, H. (2011). One-vector representations of stochastic signals for pattern recognition. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/18595

Chicago Manual of Style (16th Edition):

Tang, Hao. “One-vector representations of stochastic signals for pattern recognition.” 2011. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed April 18, 2021. http://hdl.handle.net/2142/18595.

MLA Handbook (7th Edition):

Tang, Hao. “One-vector representations of stochastic signals for pattern recognition.” 2011. Web. 18 Apr 2021.

Vancouver:

Tang H. One-vector representations of stochastic signals for pattern recognition. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2011. [cited 2021 Apr 18]. Available from: http://hdl.handle.net/2142/18595.

Council of Science Editors:

Tang H. One-vector representations of stochastic signals for pattern recognition. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2011. Available from: http://hdl.handle.net/2142/18595


University of Illinois – Urbana-Champaign

14. Lin, Kai-Hsiang. Saliency in audio and visual signals.

Degree: PhD, 1200, 2015, University of Illinois – Urbana-Champaign

 This dissertation studies saliency and its applications in audio and visual signals. For each portion of the signal, its saliency means the likelihood of attracting… (more)

Subjects/Keywords: Saliency Detection; License Plate Detection; Foreground Detection; Audio Visualization

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APA (6th Edition):

Lin, K. (2015). Saliency in audio and visual signals. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/73072

Chicago Manual of Style (16th Edition):

Lin, Kai-Hsiang. “Saliency in audio and visual signals.” 2015. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed April 18, 2021. http://hdl.handle.net/2142/73072.

MLA Handbook (7th Edition):

Lin, Kai-Hsiang. “Saliency in audio and visual signals.” 2015. Web. 18 Apr 2021.

Vancouver:

Lin K. Saliency in audio and visual signals. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2015. [cited 2021 Apr 18]. Available from: http://hdl.handle.net/2142/73072.

Council of Science Editors:

Lin K. Saliency in audio and visual signals. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2015. Available from: http://hdl.handle.net/2142/73072


University of Illinois – Urbana-Champaign

15. Le, Vuong. 3D face processing for animation and biometrics.

Degree: PhD, 1200, 2014, University of Illinois – Urbana-Champaign

 In this dissertation we study core problems in 3D face processing with their important applications in biometrics and graphics. We propose efficient and accurate feature… (more)

Subjects/Keywords: 3D shape modeling; performance driven avatar; 3D morphable model; face tracking; optical flow; gaze estimation; facial animation; facial feature localization; expression recognition; nonrigid tracking; motion model

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APA (6th Edition):

Le, V. (2014). 3D face processing for animation and biometrics. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/49853

Chicago Manual of Style (16th Edition):

Le, Vuong. “3D face processing for animation and biometrics.” 2014. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed April 18, 2021. http://hdl.handle.net/2142/49853.

MLA Handbook (7th Edition):

Le, Vuong. “3D face processing for animation and biometrics.” 2014. Web. 18 Apr 2021.

Vancouver:

Le V. 3D face processing for animation and biometrics. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2014. [cited 2021 Apr 18]. Available from: http://hdl.handle.net/2142/49853.

Council of Science Editors:

Le V. 3D face processing for animation and biometrics. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2014. Available from: http://hdl.handle.net/2142/49853


University of Illinois – Urbana-Champaign

16. Liu, Xianming. Feedback convolutional neural network in applications of computer vision.

Degree: PhD, Electrical & Computer Engr, 2016, University of Illinois – Urbana-Champaign

 With the development of deep neural networks, especially convolutional neural networks, computer vision tasks rely on training data to an unprecedented extent. As the network… (more)

Subjects/Keywords: Convolutional Neural Network; Feedback; Computer Vision

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APA (6th Edition):

Liu, X. (2016). Feedback convolutional neural network in applications of computer vision. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/95471

Chicago Manual of Style (16th Edition):

Liu, Xianming. “Feedback convolutional neural network in applications of computer vision.” 2016. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed April 18, 2021. http://hdl.handle.net/2142/95471.

MLA Handbook (7th Edition):

Liu, Xianming. “Feedback convolutional neural network in applications of computer vision.” 2016. Web. 18 Apr 2021.

Vancouver:

Liu X. Feedback convolutional neural network in applications of computer vision. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2016. [cited 2021 Apr 18]. Available from: http://hdl.handle.net/2142/95471.

Council of Science Editors:

Liu X. Feedback convolutional neural network in applications of computer vision. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2016. Available from: http://hdl.handle.net/2142/95471


University of Illinois – Urbana-Champaign

17. Khorrami, Pooya Rezvani. How deep learning can help emotion recognition.

Degree: PhD, Electrical & Computer Engr, 2017, University of Illinois – Urbana-Champaign

 As technological systems become more and more advanced, the need for including the human during the interaction process has become more apparent. One simple way… (more)

Subjects/Keywords: Emotion recognition; Deep learning; Machine learning; Computer vision; Facial expression recognition; Affective computing; Deep neural networks

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APA (6th Edition):

Khorrami, P. R. (2017). How deep learning can help emotion recognition. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/97284

Chicago Manual of Style (16th Edition):

Khorrami, Pooya Rezvani. “How deep learning can help emotion recognition.” 2017. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed April 18, 2021. http://hdl.handle.net/2142/97284.

MLA Handbook (7th Edition):

Khorrami, Pooya Rezvani. “How deep learning can help emotion recognition.” 2017. Web. 18 Apr 2021.

Vancouver:

Khorrami PR. How deep learning can help emotion recognition. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2017. [cited 2021 Apr 18]. Available from: http://hdl.handle.net/2142/97284.

Council of Science Editors:

Khorrami PR. How deep learning can help emotion recognition. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2017. Available from: http://hdl.handle.net/2142/97284


University of Illinois – Urbana-Champaign

18. Resendiz, Esther I. Computer vision for railroad track inspection.

Degree: PhD, 1200, 2011, University of Illinois – Urbana-Champaign

 In railroad track inspection, the inspection images contain periodically occurring components. Computer vision has recently been applied to several railroad applications due to its potential… (more)

Subjects/Keywords: Railroad Track Inspection; Computer Vision; Periodic Images; Multiple Signal Classification; Activity Recognition; Periodic Motion in Video

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APA (6th Edition):

Resendiz, E. I. (2011). Computer vision for railroad track inspection. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/18558

Chicago Manual of Style (16th Edition):

Resendiz, Esther I. “Computer vision for railroad track inspection.” 2011. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed April 18, 2021. http://hdl.handle.net/2142/18558.

MLA Handbook (7th Edition):

Resendiz, Esther I. “Computer vision for railroad track inspection.” 2011. Web. 18 Apr 2021.

Vancouver:

Resendiz EI. Computer vision for railroad track inspection. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2011. [cited 2021 Apr 18]. Available from: http://hdl.handle.net/2142/18558.

Council of Science Editors:

Resendiz EI. Computer vision for railroad track inspection. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2011. Available from: http://hdl.handle.net/2142/18558


University of Illinois – Urbana-Champaign

19. Balasubramanian, Arvind. Applications of low-rank matrix recovery methods in computer vision.

Degree: PhD, 1200, 2012, University of Illinois – Urbana-Champaign

 The ubiquitous availability of high-dimensional data such as images and videos has generated a lot of interest in high-dimensional data analysis. One of the key… (more)

Subjects/Keywords: Image Alignment; Texture Rectification; Low-Rank Matrix Recovery; Convex Optimization; Photometric Stereo; Principal Component Pursuit

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APA (6th Edition):

Balasubramanian, A. (2012). Applications of low-rank matrix recovery methods in computer vision. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/31929

Chicago Manual of Style (16th Edition):

Balasubramanian, Arvind. “Applications of low-rank matrix recovery methods in computer vision.” 2012. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed April 18, 2021. http://hdl.handle.net/2142/31929.

MLA Handbook (7th Edition):

Balasubramanian, Arvind. “Applications of low-rank matrix recovery methods in computer vision.” 2012. Web. 18 Apr 2021.

Vancouver:

Balasubramanian A. Applications of low-rank matrix recovery methods in computer vision. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2012. [cited 2021 Apr 18]. Available from: http://hdl.handle.net/2142/31929.

Council of Science Editors:

Balasubramanian A. Applications of low-rank matrix recovery methods in computer vision. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2012. Available from: http://hdl.handle.net/2142/31929


University of Illinois – Urbana-Champaign

20. Yang, Huiguang. From image co-segmentation to discrete optimization in computer vision - the exploration on graphical model, statistical physics, energy minimization, and integer programming.

Degree: PhD, Electrical & Computer Engr, 2016, University of Illinois – Urbana-Champaign

 This dissertation aims to explore the ideas and frameworks for solving the discrete optimization problem in computer vision. Much of the work is inspired by… (more)

Subjects/Keywords: image co-segmentation; graphical model; energy minimization; integer programming; statistical physics; discrete optimization; Mixed-Integer Quadratic Programming (MIQP); clustering; local topology consistency check; sparse optimization

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APA (6th Edition):

Yang, H. (2016). From image co-segmentation to discrete optimization in computer vision - the exploration on graphical model, statistical physics, energy minimization, and integer programming. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/95489

Chicago Manual of Style (16th Edition):

Yang, Huiguang. “From image co-segmentation to discrete optimization in computer vision - the exploration on graphical model, statistical physics, energy minimization, and integer programming.” 2016. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed April 18, 2021. http://hdl.handle.net/2142/95489.

MLA Handbook (7th Edition):

Yang, Huiguang. “From image co-segmentation to discrete optimization in computer vision - the exploration on graphical model, statistical physics, energy minimization, and integer programming.” 2016. Web. 18 Apr 2021.

Vancouver:

Yang H. From image co-segmentation to discrete optimization in computer vision - the exploration on graphical model, statistical physics, energy minimization, and integer programming. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2016. [cited 2021 Apr 18]. Available from: http://hdl.handle.net/2142/95489.

Council of Science Editors:

Yang H. From image co-segmentation to discrete optimization in computer vision - the exploration on graphical model, statistical physics, energy minimization, and integer programming. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2016. Available from: http://hdl.handle.net/2142/95489


University of Illinois – Urbana-Champaign

21. Wu, Wanmin. Human-centric control of video functions and underlying resources in 3D tele-immersive systems.

Degree: PhD, 0112, 2011, University of Illinois – Urbana-Champaign

 3D tele-immersion (3DTI) has the potential of enabling virtual-reality-like interaction among remote people with real-time 3D video. However, today's 3DTI systems still suffer from various… (more)

Subjects/Keywords: Human-centric; 3D tele-immersion; view; video streams; level-of-details; data adaptation; overlay construction; quality; Quality-of-Service; Quality-of-Experience

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APA (6th Edition):

Wu, W. (2011). Human-centric control of video functions and underlying resources in 3D tele-immersive systems. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/26126

Chicago Manual of Style (16th Edition):

Wu, Wanmin. “Human-centric control of video functions and underlying resources in 3D tele-immersive systems.” 2011. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed April 18, 2021. http://hdl.handle.net/2142/26126.

MLA Handbook (7th Edition):

Wu, Wanmin. “Human-centric control of video functions and underlying resources in 3D tele-immersive systems.” 2011. Web. 18 Apr 2021.

Vancouver:

Wu W. Human-centric control of video functions and underlying resources in 3D tele-immersive systems. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2011. [cited 2021 Apr 18]. Available from: http://hdl.handle.net/2142/26126.

Council of Science Editors:

Wu W. Human-centric control of video functions and underlying resources in 3D tele-immersive systems. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2011. Available from: http://hdl.handle.net/2142/26126


University of Illinois – Urbana-Champaign

22. Majure, Lydia. Developmental model of sensorimotor map acquisition for a humanoid robot.

Degree: PhD, 1200, 2013, University of Illinois – Urbana-Champaign

 Cognitive developmental robotics unites machine learning and neuroscience with the aim of creating robots which display the robustness and efficiency of human cognition. This document… (more)

Subjects/Keywords: Cognitive Developmental Robotics; Artificial Intelligence; Humanoid Robotics; Machine Learning; Neural Dynamics

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APA (6th Edition):

Majure, L. (2013). Developmental model of sensorimotor map acquisition for a humanoid robot. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/45648

Chicago Manual of Style (16th Edition):

Majure, Lydia. “Developmental model of sensorimotor map acquisition for a humanoid robot.” 2013. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed April 18, 2021. http://hdl.handle.net/2142/45648.

MLA Handbook (7th Edition):

Majure, Lydia. “Developmental model of sensorimotor map acquisition for a humanoid robot.” 2013. Web. 18 Apr 2021.

Vancouver:

Majure L. Developmental model of sensorimotor map acquisition for a humanoid robot. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2013. [cited 2021 Apr 18]. Available from: http://hdl.handle.net/2142/45648.

Council of Science Editors:

Majure L. Developmental model of sensorimotor map acquisition for a humanoid robot. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2013. Available from: http://hdl.handle.net/2142/45648


University of Illinois – Urbana-Champaign

23. Ji, Ming. Semi-supervised learning and relevance search on networked data.

Degree: PhD, 0112, 2014, University of Illinois – Urbana-Champaign

 Real-world data entities are often connected by meaningful relationships, forming large-scale networks. With the rapid growth of social networks and online relational data, it is… (more)

Subjects/Keywords: Data Mining; Machine Learning; Semi-supervised Learning; Search; Heterogeneous Networks; Graphs

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APA (6th Edition):

Ji, M. (2014). Semi-supervised learning and relevance search on networked data. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/46856

Chicago Manual of Style (16th Edition):

Ji, Ming. “Semi-supervised learning and relevance search on networked data.” 2014. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed April 18, 2021. http://hdl.handle.net/2142/46856.

MLA Handbook (7th Edition):

Ji, Ming. “Semi-supervised learning and relevance search on networked data.” 2014. Web. 18 Apr 2021.

Vancouver:

Ji M. Semi-supervised learning and relevance search on networked data. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2014. [cited 2021 Apr 18]. Available from: http://hdl.handle.net/2142/46856.

Council of Science Editors:

Ji M. Semi-supervised learning and relevance search on networked data. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2014. Available from: http://hdl.handle.net/2142/46856


University of Illinois – Urbana-Champaign

24. Wang, Gang. Datasets, features, learning, and models in visual recognition.

Degree: PhD, 1200, 2011, University of Illinois – Urbana-Champaign

 Visual recognition is a fundamental research topic in computer vision. This dissertation explores datasets, features, learning, and models used for visual recognition. In order to… (more)

Subjects/Keywords: Visual Recognition; Datasets; Features; Learning; Models

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APA (6th Edition):

Wang, G. (2011). Datasets, features, learning, and models in visual recognition. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/18373

Chicago Manual of Style (16th Edition):

Wang, Gang. “Datasets, features, learning, and models in visual recognition.” 2011. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed April 18, 2021. http://hdl.handle.net/2142/18373.

MLA Handbook (7th Edition):

Wang, Gang. “Datasets, features, learning, and models in visual recognition.” 2011. Web. 18 Apr 2021.

Vancouver:

Wang G. Datasets, features, learning, and models in visual recognition. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2011. [cited 2021 Apr 18]. Available from: http://hdl.handle.net/2142/18373.

Council of Science Editors:

Wang G. Datasets, features, learning, and models in visual recognition. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2011. Available from: http://hdl.handle.net/2142/18373


University of Illinois – Urbana-Champaign

25. Malik, Rahul. An Activity-Based Framework for Automated Placement and Configuration of Multiple Stereo Cameras.

Degree: PhD, 0112, 2011, University of Illinois – Urbana-Champaign

 With the advent of virtual spaces, there has been a need to integrate physical worlds with virtual spaces. The integration can be achieved by real-time… (more)

Subjects/Keywords: tele-immersion; multiple stereo camera placement; color constancy across multiple cameras

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APA (6th Edition):

Malik, R. (2011). An Activity-Based Framework for Automated Placement and Configuration of Multiple Stereo Cameras. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/18516

Chicago Manual of Style (16th Edition):

Malik, Rahul. “An Activity-Based Framework for Automated Placement and Configuration of Multiple Stereo Cameras.” 2011. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed April 18, 2021. http://hdl.handle.net/2142/18516.

MLA Handbook (7th Edition):

Malik, Rahul. “An Activity-Based Framework for Automated Placement and Configuration of Multiple Stereo Cameras.” 2011. Web. 18 Apr 2021.

Vancouver:

Malik R. An Activity-Based Framework for Automated Placement and Configuration of Multiple Stereo Cameras. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2011. [cited 2021 Apr 18]. Available from: http://hdl.handle.net/2142/18516.

Council of Science Editors:

Malik R. An Activity-Based Framework for Automated Placement and Configuration of Multiple Stereo Cameras. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2011. Available from: http://hdl.handle.net/2142/18516


University of Illinois – Urbana-Champaign

26. Yang, Qingxiong. Robust and efficient image-based 3D modeling.

Degree: PhD, 1200, 2011, University of Illinois – Urbana-Champaign

 In this dissertation, I report the progress towards building a robust and efficient 3D reconstruction system based on stereo vision. Stereo vision is known to… (more)

Subjects/Keywords: Stereo Matching; Bilateral Filter; Belief Propagation

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APA (6th Edition):

Yang, Q. (2011). Robust and efficient image-based 3D modeling. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/18553

Chicago Manual of Style (16th Edition):

Yang, Qingxiong. “Robust and efficient image-based 3D modeling.” 2011. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed April 18, 2021. http://hdl.handle.net/2142/18553.

MLA Handbook (7th Edition):

Yang, Qingxiong. “Robust and efficient image-based 3D modeling.” 2011. Web. 18 Apr 2021.

Vancouver:

Yang Q. Robust and efficient image-based 3D modeling. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2011. [cited 2021 Apr 18]. Available from: http://hdl.handle.net/2142/18553.

Council of Science Editors:

Yang Q. Robust and efficient image-based 3D modeling. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2011. Available from: http://hdl.handle.net/2142/18553


University of Illinois – Urbana-Champaign

27. Huang, Po-Sen. Shallow and deep learning for audio and natural language processing.

Degree: PhD, Electrical & Computer Engr, 2015, University of Illinois – Urbana-Champaign

 Many machine learning algorithms can be viewed as optimization problems that seek the optimum hypothesis in a hypothesis space. To model the complex dependencies in… (more)

Subjects/Keywords: deep learning; large-scale kernel machines; monaural source separation; speech recognition; information retrieval

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APA (6th Edition):

Huang, P. (2015). Shallow and deep learning for audio and natural language processing. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/78466

Chicago Manual of Style (16th Edition):

Huang, Po-Sen. “Shallow and deep learning for audio and natural language processing.” 2015. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed April 18, 2021. http://hdl.handle.net/2142/78466.

MLA Handbook (7th Edition):

Huang, Po-Sen. “Shallow and deep learning for audio and natural language processing.” 2015. Web. 18 Apr 2021.

Vancouver:

Huang P. Shallow and deep learning for audio and natural language processing. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2015. [cited 2021 Apr 18]. Available from: http://hdl.handle.net/2142/78466.

Council of Science Editors:

Huang P. Shallow and deep learning for audio and natural language processing. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2015. Available from: http://hdl.handle.net/2142/78466


University of Illinois – Urbana-Champaign

28. Niehaus, Logan. Robots as language users: a computational model for pragmatic word learning.

Degree: PhD, 1200, 2014, University of Illinois – Urbana-Champaign

 The development of machines capable of natural linguistic interaction with humans has been an active and diverse area of research for decades. More recent frameworks,… (more)

Subjects/Keywords: cognitive robotics; language acquisition; cognitive modeling; pragmatics

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APA (6th Edition):

Niehaus, L. (2014). Robots as language users: a computational model for pragmatic word learning. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/50430

Chicago Manual of Style (16th Edition):

Niehaus, Logan. “Robots as language users: a computational model for pragmatic word learning.” 2014. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed April 18, 2021. http://hdl.handle.net/2142/50430.

MLA Handbook (7th Edition):

Niehaus, Logan. “Robots as language users: a computational model for pragmatic word learning.” 2014. Web. 18 Apr 2021.

Vancouver:

Niehaus L. Robots as language users: a computational model for pragmatic word learning. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2014. [cited 2021 Apr 18]. Available from: http://hdl.handle.net/2142/50430.

Council of Science Editors:

Niehaus L. Robots as language users: a computational model for pragmatic word learning. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2014. Available from: http://hdl.handle.net/2142/50430


University of Illinois – Urbana-Champaign

29. Sharma, Harsh. Acoustic model adaptation for recognition of dysarthric speech.

Degree: PhD, 1200, 2012, University of Illinois – Urbana-Champaign

 Speech production errors characteristic of dysarthria are chiefly responsible for the low accuracy of automatic speech recognition (ASR) when used by people diagnosed with the… (more)

Subjects/Keywords: Hidden Markov Model (HMM); model adaptation; acoustic model; acoustic model adaptation; dysarthria; speech recognition; automatic speech recognition (ASR); assistive technology; isolated word recognition; UA-Speech; motor speech disorder

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APA (6th Edition):

Sharma, H. (2012). Acoustic model adaptation for recognition of dysarthric speech. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/31966

Chicago Manual of Style (16th Edition):

Sharma, Harsh. “Acoustic model adaptation for recognition of dysarthric speech.” 2012. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed April 18, 2021. http://hdl.handle.net/2142/31966.

MLA Handbook (7th Edition):

Sharma, Harsh. “Acoustic model adaptation for recognition of dysarthric speech.” 2012. Web. 18 Apr 2021.

Vancouver:

Sharma H. Acoustic model adaptation for recognition of dysarthric speech. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2012. [cited 2021 Apr 18]. Available from: http://hdl.handle.net/2142/31966.

Council of Science Editors:

Sharma H. Acoustic model adaptation for recognition of dysarthric speech. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2012. Available from: http://hdl.handle.net/2142/31966


University of Illinois – Urbana-Champaign

30. Huang, Jui Ting. Semi-supervised learning for acoustic and prosodic modeling in speech applications.

Degree: PhD, 1200, 2012, University of Illinois – Urbana-Champaign

 Enormous amounts of audio recordings of human speech are essential ingredients for building reliable statistical models for many speech applications, such as automatic speech recognition… (more)

Subjects/Keywords: Semi-Supervised Learning; Speech Recognition; Acoustic Modeling; Prosodic Modeling

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APA (6th Edition):

Huang, J. T. (2012). Semi-supervised learning for acoustic and prosodic modeling in speech applications. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/32006

Chicago Manual of Style (16th Edition):

Huang, Jui Ting. “Semi-supervised learning for acoustic and prosodic modeling in speech applications.” 2012. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed April 18, 2021. http://hdl.handle.net/2142/32006.

MLA Handbook (7th Edition):

Huang, Jui Ting. “Semi-supervised learning for acoustic and prosodic modeling in speech applications.” 2012. Web. 18 Apr 2021.

Vancouver:

Huang JT. Semi-supervised learning for acoustic and prosodic modeling in speech applications. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2012. [cited 2021 Apr 18]. Available from: http://hdl.handle.net/2142/32006.

Council of Science Editors:

Huang JT. Semi-supervised learning for acoustic and prosodic modeling in speech applications. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2012. Available from: http://hdl.handle.net/2142/32006

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