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You searched for +publisher:"University of Texas – Austin" +contributor:("Grauman, Kristen"). Showing records 1 – 30 of 42 total matches.

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University of Texas – Austin

1. Kelle, Joshua Allen. Frugal Forests : learning a dynamic and cost sensitive feature extraction policy for anytime activity classification.

Degree: Computer Sciences, 2018, University of Texas – Austin

 Many approaches to activity classification use supervised learning and so rely on extracting some form of features from the video. This feature extraction process can… (more)

Subjects/Keywords: Frugal Forest; Feature extraction; Activity recognition; Cost; Dynamic

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

APA (6th Edition):

Kelle, J. A. (2018). Frugal Forests : learning a dynamic and cost sensitive feature extraction policy for anytime activity classification. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/68857

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):

Kelle, Joshua Allen. “Frugal Forests : learning a dynamic and cost sensitive feature extraction policy for anytime activity classification.” 2018. Thesis, University of Texas – Austin. Accessed April 22, 2019. http://hdl.handle.net/2152/68857.

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

MLA Handbook (7th Edition):

Kelle, Joshua Allen. “Frugal Forests : learning a dynamic and cost sensitive feature extraction policy for anytime activity classification.” 2018. Web. 22 Apr 2019.

Vancouver:

Kelle JA. Frugal Forests : learning a dynamic and cost sensitive feature extraction policy for anytime activity classification. [Internet] [Thesis]. University of Texas – Austin; 2018. [cited 2019 Apr 22]. Available from: http://hdl.handle.net/2152/68857.

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

Council of Science Editors:

Kelle JA. Frugal Forests : learning a dynamic and cost sensitive feature extraction policy for anytime activity classification. [Thesis]. University of Texas – Austin; 2018. Available from: http://hdl.handle.net/2152/68857

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


University of Texas – Austin

2. Hwang, Sung Ju. Reading between the lines : object localization using implicit cues from image tags.

Degree: Computer Sciences, 2010, University of Texas – Austin

 Current uses of tagged images typically exploit only the most explicit information: the link between the nouns named and the objects present somewhere in the… (more)

Subjects/Keywords: Computer vision; Object recognition; Object detection

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APA (6th Edition):

Hwang, S. J. (2010). Reading between the lines : object localization using implicit cues from image tags. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/ETD-UT-2010-05-1514

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):

Hwang, Sung Ju. “Reading between the lines : object localization using implicit cues from image tags.” 2010. Thesis, University of Texas – Austin. Accessed April 22, 2019. http://hdl.handle.net/2152/ETD-UT-2010-05-1514.

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

MLA Handbook (7th Edition):

Hwang, Sung Ju. “Reading between the lines : object localization using implicit cues from image tags.” 2010. Web. 22 Apr 2019.

Vancouver:

Hwang SJ. Reading between the lines : object localization using implicit cues from image tags. [Internet] [Thesis]. University of Texas – Austin; 2010. [cited 2019 Apr 22]. Available from: http://hdl.handle.net/2152/ETD-UT-2010-05-1514.

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

Council of Science Editors:

Hwang SJ. Reading between the lines : object localization using implicit cues from image tags. [Thesis]. University of Texas – Austin; 2010. Available from: http://hdl.handle.net/2152/ETD-UT-2010-05-1514

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


University of Texas – Austin

3. Sheshadri, Aashish. A collaborative approach to IR evaluation.

Degree: Computer Sciences, 2014, University of Texas – Austin

 In this thesis we investigate two main problems: 1) inferring consensus from disparate inputs to improve quality of crowd contributed data; and 2) developing a… (more)

Subjects/Keywords: Crowdsourcing; Evaluation; Information retrieval; Simulation

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APA (6th Edition):

Sheshadri, A. (2014). A collaborative approach to IR evaluation. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/25910

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):

Sheshadri, Aashish. “A collaborative approach to IR evaluation.” 2014. Thesis, University of Texas – Austin. Accessed April 22, 2019. http://hdl.handle.net/2152/25910.

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

MLA Handbook (7th Edition):

Sheshadri, Aashish. “A collaborative approach to IR evaluation.” 2014. Web. 22 Apr 2019.

Vancouver:

Sheshadri A. A collaborative approach to IR evaluation. [Internet] [Thesis]. University of Texas – Austin; 2014. [cited 2019 Apr 22]. Available from: http://hdl.handle.net/2152/25910.

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

Council of Science Editors:

Sheshadri A. A collaborative approach to IR evaluation. [Thesis]. University of Texas – Austin; 2014. Available from: http://hdl.handle.net/2152/25910

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


University of Texas – Austin

4. Kolawole, Olamide Temitayo. Classification of internet memes.

Degree: Electrical and Computer Engineering, 2015, University of Texas – Austin

 This paper explores a system that could be used to classify internet memes by certain characteristics. The anatomy of these viral images are explored to… (more)

Subjects/Keywords: Classification

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

APA (6th Edition):

Kolawole, O. T. (2015). Classification of internet memes. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/35301

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):

Kolawole, Olamide Temitayo. “Classification of internet memes.” 2015. Thesis, University of Texas – Austin. Accessed April 22, 2019. http://hdl.handle.net/2152/35301.

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

MLA Handbook (7th Edition):

Kolawole, Olamide Temitayo. “Classification of internet memes.” 2015. Web. 22 Apr 2019.

Vancouver:

Kolawole OT. Classification of internet memes. [Internet] [Thesis]. University of Texas – Austin; 2015. [cited 2019 Apr 22]. Available from: http://hdl.handle.net/2152/35301.

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

Council of Science Editors:

Kolawole OT. Classification of internet memes. [Thesis]. University of Texas – Austin; 2015. Available from: http://hdl.handle.net/2152/35301

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


University of Texas – Austin

5. Gupta, Sonal. Activity retrieval in closed captioned videos.

Degree: Computer Sciences, 2009, University of Texas – Austin

 Recognizing activities in real-world videos is a difficult problem exacerbated by background clutter, changes in camera angle & zoom, occlusion and rapid camera movements. Large… (more)

Subjects/Keywords: Activity Recognition; Action Recognition; Video Retrieval; Machine Learning; Computer Vision; Multimedia; Closed Captions

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

APA (6th Edition):

Gupta, S. (2009). Activity retrieval in closed captioned videos. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/ETD-UT-2009-08-305

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):

Gupta, Sonal. “Activity retrieval in closed captioned videos.” 2009. Thesis, University of Texas – Austin. Accessed April 22, 2019. http://hdl.handle.net/2152/ETD-UT-2009-08-305.

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

MLA Handbook (7th Edition):

Gupta, Sonal. “Activity retrieval in closed captioned videos.” 2009. Web. 22 Apr 2019.

Vancouver:

Gupta S. Activity retrieval in closed captioned videos. [Internet] [Thesis]. University of Texas – Austin; 2009. [cited 2019 Apr 22]. Available from: http://hdl.handle.net/2152/ETD-UT-2009-08-305.

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

Council of Science Editors:

Gupta S. Activity retrieval in closed captioned videos. [Thesis]. University of Texas – Austin; 2009. Available from: http://hdl.handle.net/2152/ETD-UT-2009-08-305

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


University of Texas – Austin

6. Jain, Suyog Dutt. Facial expression recognition with temporal modeling of shapes.

Degree: Computer Sciences, 2011, University of Texas – Austin

 Conditional Random Fields (CRFs) is a discriminative and supervised approach for simultaneous sequence segmentation and frame labeling. Latent-Dynamic Conditional Random Fields (LDCRFs) incorporates hidden state… (more)

Subjects/Keywords: Facial expression recognition; Temporal modeling; Procrustes analysis

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APA (6th Edition):

Jain, S. D. (2011). Facial expression recognition with temporal modeling of shapes. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/ETD-UT-2011-08-4279

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):

Jain, Suyog Dutt. “Facial expression recognition with temporal modeling of shapes.” 2011. Thesis, University of Texas – Austin. Accessed April 22, 2019. http://hdl.handle.net/2152/ETD-UT-2011-08-4279.

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

MLA Handbook (7th Edition):

Jain, Suyog Dutt. “Facial expression recognition with temporal modeling of shapes.” 2011. Web. 22 Apr 2019.

Vancouver:

Jain SD. Facial expression recognition with temporal modeling of shapes. [Internet] [Thesis]. University of Texas – Austin; 2011. [cited 2019 Apr 22]. Available from: http://hdl.handle.net/2152/ETD-UT-2011-08-4279.

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

Council of Science Editors:

Jain SD. Facial expression recognition with temporal modeling of shapes. [Thesis]. University of Texas – Austin; 2011. Available from: http://hdl.handle.net/2152/ETD-UT-2011-08-4279

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


University of Texas – Austin

7. Tamersoy, Birgi. Vehicle detection and tracking in highway surveillance videos.

Degree: Electrical and Computer Engineering, 2009, University of Texas – Austin

 We present a novel approach for vehicle detection and tracking in highway surveillance videos. This method incorporates well-studied computer vision and machine learning techniques to… (more)

Subjects/Keywords: vehicle detection; vehicle tracking; highway surveillance; traffic monitoring; unsupervised learning

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APA (6th Edition):

Tamersoy, B. (2009). Vehicle detection and tracking in highway surveillance videos. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/ETD-UT-2009-08-316

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):

Tamersoy, Birgi. “Vehicle detection and tracking in highway surveillance videos.” 2009. Thesis, University of Texas – Austin. Accessed April 22, 2019. http://hdl.handle.net/2152/ETD-UT-2009-08-316.

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

MLA Handbook (7th Edition):

Tamersoy, Birgi. “Vehicle detection and tracking in highway surveillance videos.” 2009. Web. 22 Apr 2019.

Vancouver:

Tamersoy B. Vehicle detection and tracking in highway surveillance videos. [Internet] [Thesis]. University of Texas – Austin; 2009. [cited 2019 Apr 22]. Available from: http://hdl.handle.net/2152/ETD-UT-2009-08-316.

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

Council of Science Editors:

Tamersoy B. Vehicle detection and tracking in highway surveillance videos. [Thesis]. University of Texas – Austin; 2009. Available from: http://hdl.handle.net/2152/ETD-UT-2009-08-316

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


University of Texas – Austin

8. Chen, Chao-Yeh. Clues from the beaten path : location estimation with bursty sequences of tourist photos: Location estimation with bursty sequences of tourist photos.

Degree: Electrical and Computer Engineering, 2010, University of Texas – Austin

 Existing methods for image-based location estimation generally attempt to recognize every photo independently, and their resulting reliance on strong visual feature matches makes them most… (more)

Subjects/Keywords: Location estimation; Hidden Markov Model; Object recognition

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APA (6th Edition):

Chen, C. (2010). Clues from the beaten path : location estimation with bursty sequences of tourist photos: Location estimation with bursty sequences of tourist photos. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/ETD-UT-2010-12-2301

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):

Chen, Chao-Yeh. “Clues from the beaten path : location estimation with bursty sequences of tourist photos: Location estimation with bursty sequences of tourist photos.” 2010. Thesis, University of Texas – Austin. Accessed April 22, 2019. http://hdl.handle.net/2152/ETD-UT-2010-12-2301.

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

MLA Handbook (7th Edition):

Chen, Chao-Yeh. “Clues from the beaten path : location estimation with bursty sequences of tourist photos: Location estimation with bursty sequences of tourist photos.” 2010. Web. 22 Apr 2019.

Vancouver:

Chen C. Clues from the beaten path : location estimation with bursty sequences of tourist photos: Location estimation with bursty sequences of tourist photos. [Internet] [Thesis]. University of Texas – Austin; 2010. [cited 2019 Apr 22]. Available from: http://hdl.handle.net/2152/ETD-UT-2010-12-2301.

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

Council of Science Editors:

Chen C. Clues from the beaten path : location estimation with bursty sequences of tourist photos: Location estimation with bursty sequences of tourist photos. [Thesis]. University of Texas – Austin; 2010. Available from: http://hdl.handle.net/2152/ETD-UT-2010-12-2301

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


University of Texas – Austin

9. Stober, Jeremy Michael. Sensorimotor embedding : a developmental approach to learning geometry.

Degree: Computer Sciences, 2015, University of Texas – Austin

 A human infant facing the blooming, buzzing confusion of the senses grows up to be an adult with common-sense knowledge of geometry; this knowledge then… (more)

Subjects/Keywords: Sensorimotor; Ai; Robotics; Development

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APA (6th Edition):

Stober, J. M. (2015). Sensorimotor embedding : a developmental approach to learning geometry. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/30532

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):

Stober, Jeremy Michael. “Sensorimotor embedding : a developmental approach to learning geometry.” 2015. Thesis, University of Texas – Austin. Accessed April 22, 2019. http://hdl.handle.net/2152/30532.

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

MLA Handbook (7th Edition):

Stober, Jeremy Michael. “Sensorimotor embedding : a developmental approach to learning geometry.” 2015. Web. 22 Apr 2019.

Vancouver:

Stober JM. Sensorimotor embedding : a developmental approach to learning geometry. [Internet] [Thesis]. University of Texas – Austin; 2015. [cited 2019 Apr 22]. Available from: http://hdl.handle.net/2152/30532.

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

Council of Science Editors:

Stober JM. Sensorimotor embedding : a developmental approach to learning geometry. [Thesis]. University of Texas – Austin; 2015. Available from: http://hdl.handle.net/2152/30532

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


University of Texas – Austin

10. -8928-8917. Rapid and contextual activity analysis : a semi-automated activity category, time, location, and video data collection and analysis methodology.

Degree: Civil, Architectural, and Environmental Engineering, 2015, University of Texas – Austin

 The performance of construction projects is significantly impacted by on-site labor and the productivity thereof. Despite the benefits from technological advancements in recent decades, construction… (more)

Subjects/Keywords: Activity analysis; Automated data collection; Productivity; Work sampling; Labor productivity; Contextual information; Data integration; Voice recognition; Database; OLAP

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APA (6th Edition):

-8928-8917. (2015). Rapid and contextual activity analysis : a semi-automated activity category, time, location, and video data collection and analysis methodology. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/32162

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

Chicago Manual of Style (16th Edition):

-8928-8917. “Rapid and contextual activity analysis : a semi-automated activity category, time, location, and video data collection and analysis methodology.” 2015. Thesis, University of Texas – Austin. Accessed April 22, 2019. http://hdl.handle.net/2152/32162.

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

MLA Handbook (7th Edition):

-8928-8917. “Rapid and contextual activity analysis : a semi-automated activity category, time, location, and video data collection and analysis methodology.” 2015. Web. 22 Apr 2019.

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

Vancouver:

-8928-8917. Rapid and contextual activity analysis : a semi-automated activity category, time, location, and video data collection and analysis methodology. [Internet] [Thesis]. University of Texas – Austin; 2015. [cited 2019 Apr 22]. Available from: http://hdl.handle.net/2152/32162.

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

Council of Science Editors:

-8928-8917. Rapid and contextual activity analysis : a semi-automated activity category, time, location, and video data collection and analysis methodology. [Thesis]. University of Texas – Austin; 2015. Available from: http://hdl.handle.net/2152/32162

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


University of Texas – Austin

11. -6435-245X. Learning with positive and unlabeled examples.

Degree: Computer Sciences, 2015, University of Texas – Austin

 Developing partially supervised models is becoming increasingly relevant in the context of modern machine learning applications, where supervision often comes at a cost. In particular,… (more)

Subjects/Keywords: PU learning; Learning theory

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APA (6th Edition):

-6435-245X. (2015). Learning with positive and unlabeled examples. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/32826

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

Chicago Manual of Style (16th Edition):

-6435-245X. “Learning with positive and unlabeled examples.” 2015. Thesis, University of Texas – Austin. Accessed April 22, 2019. http://hdl.handle.net/2152/32826.

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

MLA Handbook (7th Edition):

-6435-245X. “Learning with positive and unlabeled examples.” 2015. Web. 22 Apr 2019.

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

Vancouver:

-6435-245X. Learning with positive and unlabeled examples. [Internet] [Thesis]. University of Texas – Austin; 2015. [cited 2019 Apr 22]. Available from: http://hdl.handle.net/2152/32826.

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

Council of Science Editors:

-6435-245X. Learning with positive and unlabeled examples. [Thesis]. University of Texas – Austin; 2015. Available from: http://hdl.handle.net/2152/32826

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


University of Texas – Austin

12. Whang, Joyce Jiyoung. Overlapping community detection in massive social networks.

Degree: Computer Sciences, 2015, University of Texas – Austin

 Massive social networks have become increasingly popular in recent years. Community detection is one of the most important techniques for the analysis of such complex… (more)

Subjects/Keywords: Community detection; Clustering; Social networks; Overlapping communities; Overlapping clusters; Non-exhaustive clustering; Seed expansion; K-means; Semidefinite programming; Co-clustering; PageRank; Data-driven algorithm; Scalable computing

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APA (6th Edition):

Whang, J. J. (2015). Overlapping community detection in massive social networks. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/33272

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):

Whang, Joyce Jiyoung. “Overlapping community detection in massive social networks.” 2015. Thesis, University of Texas – Austin. Accessed April 22, 2019. http://hdl.handle.net/2152/33272.

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

MLA Handbook (7th Edition):

Whang, Joyce Jiyoung. “Overlapping community detection in massive social networks.” 2015. Web. 22 Apr 2019.

Vancouver:

Whang JJ. Overlapping community detection in massive social networks. [Internet] [Thesis]. University of Texas – Austin; 2015. [cited 2019 Apr 22]. Available from: http://hdl.handle.net/2152/33272.

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

Council of Science Editors:

Whang JJ. Overlapping community detection in massive social networks. [Thesis]. University of Texas – Austin; 2015. Available from: http://hdl.handle.net/2152/33272

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


University of Texas – Austin

13. Chen, Chao-Yeh. Learning human activities and poses with interconnected data sources.

Degree: Computer Sciences, 2016, University of Texas – Austin

 Understanding human actions and poses in images or videos is a challenging problem in computer vision. There are different topics related to this problem such… (more)

Subjects/Keywords: Activity recognition; Activity detection

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APA (6th Edition):

Chen, C. (2016). Learning human activities and poses with interconnected data sources. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/40260

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):

Chen, Chao-Yeh. “Learning human activities and poses with interconnected data sources.” 2016. Thesis, University of Texas – Austin. Accessed April 22, 2019. http://hdl.handle.net/2152/40260.

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

MLA Handbook (7th Edition):

Chen, Chao-Yeh. “Learning human activities and poses with interconnected data sources.” 2016. Web. 22 Apr 2019.

Vancouver:

Chen C. Learning human activities and poses with interconnected data sources. [Internet] [Thesis]. University of Texas – Austin; 2016. [cited 2019 Apr 22]. Available from: http://hdl.handle.net/2152/40260.

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

Council of Science Editors:

Chen C. Learning human activities and poses with interconnected data sources. [Thesis]. University of Texas – Austin; 2016. Available from: http://hdl.handle.net/2152/40260

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


University of Texas – Austin

14. Huynh, Tuyen Ngoc. Improving the accuracy and scalability of discriminative learning methods for Markov logic networks.

Degree: Computer Sciences, 2011, University of Texas – Austin

 Many real-world problems involve data that both have complex structures and uncertainty. Statistical relational learning (SRL) is an emerging area of research that addresses the… (more)

Subjects/Keywords: Markov logic networks; Statistical relational learning; Structured prediction; Logic networks; Artificial intelligence; Machine learning

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APA (6th Edition):

Huynh, T. N. (2011). Improving the accuracy and scalability of discriminative learning methods for Markov logic networks. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/ETD-UT-2011-05-3436

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):

Huynh, Tuyen Ngoc. “Improving the accuracy and scalability of discriminative learning methods for Markov logic networks.” 2011. Thesis, University of Texas – Austin. Accessed April 22, 2019. http://hdl.handle.net/2152/ETD-UT-2011-05-3436.

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

MLA Handbook (7th Edition):

Huynh, Tuyen Ngoc. “Improving the accuracy and scalability of discriminative learning methods for Markov logic networks.” 2011. Web. 22 Apr 2019.

Vancouver:

Huynh TN. Improving the accuracy and scalability of discriminative learning methods for Markov logic networks. [Internet] [Thesis]. University of Texas – Austin; 2011. [cited 2019 Apr 22]. Available from: http://hdl.handle.net/2152/ETD-UT-2011-05-3436.

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

Council of Science Editors:

Huynh TN. Improving the accuracy and scalability of discriminative learning methods for Markov logic networks. [Thesis]. University of Texas – Austin; 2011. Available from: http://hdl.handle.net/2152/ETD-UT-2011-05-3436

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


University of Texas – Austin

15. Si, Si, Ph.D. Large-scale non-linear prediction with applications.

Degree: Computer Sciences, 2016, University of Texas – Austin

 With an immense growth in data, there is a great need for training and testing machine learning models on very large data sets. Several standard… (more)

Subjects/Keywords: Kernel methods; Classification; Decision trees

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

APA (6th Edition):

Si, Si, P. D. (2016). Large-scale non-linear prediction with applications. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/43583

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):

Si, Si, Ph D. “Large-scale non-linear prediction with applications.” 2016. Thesis, University of Texas – Austin. Accessed April 22, 2019. http://hdl.handle.net/2152/43583.

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

MLA Handbook (7th Edition):

Si, Si, Ph D. “Large-scale non-linear prediction with applications.” 2016. Web. 22 Apr 2019.

Vancouver:

Si, Si PD. Large-scale non-linear prediction with applications. [Internet] [Thesis]. University of Texas – Austin; 2016. [cited 2019 Apr 22]. Available from: http://hdl.handle.net/2152/43583.

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

Council of Science Editors:

Si, Si PD. Large-scale non-linear prediction with applications. [Thesis]. University of Texas – Austin; 2016. Available from: http://hdl.handle.net/2152/43583

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


University of Texas – Austin

16. Verma, Nishant. Biomarker for tracking progression of Alzheimer's disease in clinical trials.

Degree: Biomedical Engineering, 2015, University of Texas – Austin

 Currently, there are no treatments available for mitigating the neurological effects of Alzheimer's disease. All clinical trials of disease-modifying treatments, which showed promise in animal… (more)

Subjects/Keywords: Alzheimer's disease; Clinical trials; Alzheimer's Disease Assessment Scale-Cognitive subscale; Item response theory; ADAS-Cog; Cognitive impairment; MCI; Clinical trial efficiency; MCI stage; Prodromal stage; Cerebral atrophy; MR volumes; Automatic tissue segmentation; Biomarkers; Alzheimer’s biomarkers; ADAS-Cog scoring methodology

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APA (6th Edition):

Verma, N. (2015). Biomarker for tracking progression of Alzheimer's disease in clinical trials. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/46741

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):

Verma, Nishant. “Biomarker for tracking progression of Alzheimer's disease in clinical trials.” 2015. Thesis, University of Texas – Austin. Accessed April 22, 2019. http://hdl.handle.net/2152/46741.

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

MLA Handbook (7th Edition):

Verma, Nishant. “Biomarker for tracking progression of Alzheimer's disease in clinical trials.” 2015. Web. 22 Apr 2019.

Vancouver:

Verma N. Biomarker for tracking progression of Alzheimer's disease in clinical trials. [Internet] [Thesis]. University of Texas – Austin; 2015. [cited 2019 Apr 22]. Available from: http://hdl.handle.net/2152/46741.

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

Council of Science Editors:

Verma N. Biomarker for tracking progression of Alzheimer's disease in clinical trials. [Thesis]. University of Texas – Austin; 2015. Available from: http://hdl.handle.net/2152/46741

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

17. Chiang, Kai-Yang. Statistical analysis for modeling dyadic interactions using machine learning methods.

Degree: Computer Sciences, 2017, University of Texas – Austin

 Modeling dyadic interactions between entities is one of the fundamental problems in machine learning with many real-world applications, including recommender systems, data clustering, social network… (more)

Subjects/Keywords: Dyadic interaction modeling; Statistical machine learning; Signed network analysis; Signed graph clustering; Dyadic rank aggregation; Matrix completion; Robust PCA; Side information

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APA (6th Edition):

Chiang, K. (2017). Statistical analysis for modeling dyadic interactions using machine learning methods. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/47368

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):

Chiang, Kai-Yang. “Statistical analysis for modeling dyadic interactions using machine learning methods.” 2017. Thesis, University of Texas – Austin. Accessed April 22, 2019. http://hdl.handle.net/2152/47368.

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

MLA Handbook (7th Edition):

Chiang, Kai-Yang. “Statistical analysis for modeling dyadic interactions using machine learning methods.” 2017. Web. 22 Apr 2019.

Vancouver:

Chiang K. Statistical analysis for modeling dyadic interactions using machine learning methods. [Internet] [Thesis]. University of Texas – Austin; 2017. [cited 2019 Apr 22]. Available from: http://hdl.handle.net/2152/47368.

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

Council of Science Editors:

Chiang K. Statistical analysis for modeling dyadic interactions using machine learning methods. [Thesis]. University of Texas – Austin; 2017. Available from: http://hdl.handle.net/2152/47368

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


University of Texas – Austin

18. -3729-8456. Natural-language video description with deep recurrent neural networks.

Degree: Computer Sciences, 2017, University of Texas – Austin

 For most people, watching a brief video and describing what happened (in words) is an easy task. For machines, extracting meaning from video pixels and… (more)

Subjects/Keywords: Video; Captioning; Description; LSTM; RNN; Recurrent; Neural networks; Image captioning; Video captioning; Language and vision

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APA (6th Edition):

-3729-8456. (2017). Natural-language video description with deep recurrent neural networks. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/62987

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

Chicago Manual of Style (16th Edition):

-3729-8456. “Natural-language video description with deep recurrent neural networks.” 2017. Thesis, University of Texas – Austin. Accessed April 22, 2019. http://hdl.handle.net/2152/62987.

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

MLA Handbook (7th Edition):

-3729-8456. “Natural-language video description with deep recurrent neural networks.” 2017. Web. 22 Apr 2019.

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

Vancouver:

-3729-8456. Natural-language video description with deep recurrent neural networks. [Internet] [Thesis]. University of Texas – Austin; 2017. [cited 2019 Apr 22]. Available from: http://hdl.handle.net/2152/62987.

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

Council of Science Editors:

-3729-8456. Natural-language video description with deep recurrent neural networks. [Thesis]. University of Texas – Austin; 2017. Available from: http://hdl.handle.net/2152/62987

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


University of Texas – Austin

19. -0736-0602. Perceptual quality assessment of real-world images and videos.

Degree: Computer Sciences, 2017, University of Texas – Austin

 The development of online social-media venues and rapid advances in technology by camera and mobile device manufacturers have led to the creation and consumption of… (more)

Subjects/Keywords: Perceptual quality assessment; Quality of experience

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APA (6th Edition):

-0736-0602. (2017). Perceptual quality assessment of real-world images and videos. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/62989

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

Chicago Manual of Style (16th Edition):

-0736-0602. “Perceptual quality assessment of real-world images and videos.” 2017. Thesis, University of Texas – Austin. Accessed April 22, 2019. http://hdl.handle.net/2152/62989.

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

MLA Handbook (7th Edition):

-0736-0602. “Perceptual quality assessment of real-world images and videos.” 2017. Web. 22 Apr 2019.

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

Vancouver:

-0736-0602. Perceptual quality assessment of real-world images and videos. [Internet] [Thesis]. University of Texas – Austin; 2017. [cited 2019 Apr 22]. Available from: http://hdl.handle.net/2152/62989.

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

Council of Science Editors:

-0736-0602. Perceptual quality assessment of real-world images and videos. [Thesis]. University of Texas – Austin; 2017. Available from: http://hdl.handle.net/2152/62989

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


University of Texas – Austin

20. Jain, Suyog Dutt. Human machine collaboration for foreground segmentation in images and videos.

Degree: Computer Sciences, 2018, University of Texas – Austin

 Foreground segmentation is defined as the problem of generating pixel level foreground masks for all the objects in a given image or video. Accurate foreground… (more)

Subjects/Keywords: Computer vision; Crowdsourcing; Human machine collaboration; Image and video segmentation; Image segmentation; Video segmentation; Foreground segmentation; Object segmentation

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

APA (6th Edition):

Jain, S. D. (2018). Human machine collaboration for foreground segmentation in images and videos. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/63453

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):

Jain, Suyog Dutt. “Human machine collaboration for foreground segmentation in images and videos.” 2018. Thesis, University of Texas – Austin. Accessed April 22, 2019. http://hdl.handle.net/2152/63453.

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

MLA Handbook (7th Edition):

Jain, Suyog Dutt. “Human machine collaboration for foreground segmentation in images and videos.” 2018. Web. 22 Apr 2019.

Vancouver:

Jain SD. Human machine collaboration for foreground segmentation in images and videos. [Internet] [Thesis]. University of Texas – Austin; 2018. [cited 2019 Apr 22]. Available from: http://hdl.handle.net/2152/63453.

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

Council of Science Editors:

Jain SD. Human machine collaboration for foreground segmentation in images and videos. [Thesis]. University of Texas – Austin; 2018. Available from: http://hdl.handle.net/2152/63453

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


University of Texas – Austin

21. -6888-3095. Embodied learning for visual recognition.

Degree: Electrical and Computer Engineering, 2017, University of Texas – Austin

 The field of visual recognition in recent years has come to rely on large expensively curated and manually labeled "bags of disembodied images". In the… (more)

Subjects/Keywords: Computer vision; Unsupervised learning; Embodied learning

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APA (6th Edition):

-6888-3095. (2017). Embodied learning for visual recognition. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/63489

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

Chicago Manual of Style (16th Edition):

-6888-3095. “Embodied learning for visual recognition.” 2017. Thesis, University of Texas – Austin. Accessed April 22, 2019. http://hdl.handle.net/2152/63489.

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

MLA Handbook (7th Edition):

-6888-3095. “Embodied learning for visual recognition.” 2017. Web. 22 Apr 2019.

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

Vancouver:

-6888-3095. Embodied learning for visual recognition. [Internet] [Thesis]. University of Texas – Austin; 2017. [cited 2019 Apr 22]. Available from: http://hdl.handle.net/2152/63489.

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

Council of Science Editors:

-6888-3095. Embodied learning for visual recognition. [Thesis]. University of Texas – Austin; 2017. Available from: http://hdl.handle.net/2152/63489

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


University of Texas – Austin

22. Jun, Goo. Transfer learning for classification of spatially varying data.

Degree: Electrical and Computer Engineering, 2010, University of Texas – Austin

 Many real-world datasets have spatial components that provide valuable information about characteristics of the data. In this dissertation, a novel framework for adaptive models that… (more)

Subjects/Keywords: Machine learning; Classification and semi-supervised learning algorithms; Gaussian processes; Gaussian process regressions; Spatial statistics

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APA (6th Edition):

Jun, G. (2010). Transfer learning for classification of spatially varying data. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/ETD-UT-2010-08-1962

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):

Jun, Goo. “Transfer learning for classification of spatially varying data.” 2010. Thesis, University of Texas – Austin. Accessed April 22, 2019. http://hdl.handle.net/2152/ETD-UT-2010-08-1962.

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

MLA Handbook (7th Edition):

Jun, Goo. “Transfer learning for classification of spatially varying data.” 2010. Web. 22 Apr 2019.

Vancouver:

Jun G. Transfer learning for classification of spatially varying data. [Internet] [Thesis]. University of Texas – Austin; 2010. [cited 2019 Apr 22]. Available from: http://hdl.handle.net/2152/ETD-UT-2010-08-1962.

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

Council of Science Editors:

Jun G. Transfer learning for classification of spatially varying data. [Thesis]. University of Texas – Austin; 2010. Available from: http://hdl.handle.net/2152/ETD-UT-2010-08-1962

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

23. Wan, Shaohua. Learning to recognize egocentric activities using RGB-D data.

Degree: Electrical and Computer Engineering, 2015, University of Texas – Austin

 There are two recent trends that are changing the landscape of vision-based activity recognition. On one hand, wearable cameras have become widely used for recording… (more)

Subjects/Keywords: Computer vision; Egocentric activity recognition; RGB-D camera

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APA (6th Edition):

Wan, S. (2015). Learning to recognize egocentric activities using RGB-D data. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/33314

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):

Wan, Shaohua. “Learning to recognize egocentric activities using RGB-D data.” 2015. Thesis, University of Texas – Austin. Accessed April 22, 2019. http://hdl.handle.net/2152/33314.

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

MLA Handbook (7th Edition):

Wan, Shaohua. “Learning to recognize egocentric activities using RGB-D data.” 2015. Web. 22 Apr 2019.

Vancouver:

Wan S. Learning to recognize egocentric activities using RGB-D data. [Internet] [Thesis]. University of Texas – Austin; 2015. [cited 2019 Apr 22]. Available from: http://hdl.handle.net/2152/33314.

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

Council of Science Editors:

Wan S. Learning to recognize egocentric activities using RGB-D data. [Thesis]. University of Texas – Austin; 2015. Available from: http://hdl.handle.net/2152/33314

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


University of Texas – Austin

24. Jahanbin, Sina. New approaches to automatic 3-D and 2-D 3-D face recognition.

Degree: Electrical and Computer Engineering, 2011, University of Texas – Austin

 Automatic face recognition has attracted the attention of many research institutes, commercial industries, and government agencies in the past few years mainly due to the… (more)

Subjects/Keywords: Biometrics; Face recognition; Facial recognition; Human face recognition (Computer science); 3-D imaging; Gabor clues

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APA (6th Edition):

Jahanbin, S. (2011). New approaches to automatic 3-D and 2-D 3-D face recognition. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/ETD-UT-2011-05-2990

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):

Jahanbin, Sina. “New approaches to automatic 3-D and 2-D 3-D face recognition.” 2011. Thesis, University of Texas – Austin. Accessed April 22, 2019. http://hdl.handle.net/2152/ETD-UT-2011-05-2990.

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

MLA Handbook (7th Edition):

Jahanbin, Sina. “New approaches to automatic 3-D and 2-D 3-D face recognition.” 2011. Web. 22 Apr 2019.

Vancouver:

Jahanbin S. New approaches to automatic 3-D and 2-D 3-D face recognition. [Internet] [Thesis]. University of Texas – Austin; 2011. [cited 2019 Apr 22]. Available from: http://hdl.handle.net/2152/ETD-UT-2011-05-2990.

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

Council of Science Editors:

Jahanbin S. New approaches to automatic 3-D and 2-D 3-D face recognition. [Thesis]. University of Texas – Austin; 2011. Available from: http://hdl.handle.net/2152/ETD-UT-2011-05-2990

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


University of Texas – Austin

25. Vijayanarasimhan, Sudheendra. Active visual category learning.

Degree: Computer Sciences, 2011, University of Texas – Austin

 Visual recognition research develops algorithms and representations to autonomously recognize visual entities such as objects, actions, and attributes. The traditional protocol involves manually collecting training… (more)

Subjects/Keywords: Artificial intelligence; Active learning; Object recognition; Object detection; Cost-sensitive learning; Multi-level learning; Budgeted learning; Large-scale active learning; Live learning; Machine learning; Visual recognition system

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APA (6th Edition):

Vijayanarasimhan, S. (2011). Active visual category learning. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/ETD-UT-2011-05-3014

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):

Vijayanarasimhan, Sudheendra. “Active visual category learning.” 2011. Thesis, University of Texas – Austin. Accessed April 22, 2019. http://hdl.handle.net/2152/ETD-UT-2011-05-3014.

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

MLA Handbook (7th Edition):

Vijayanarasimhan, Sudheendra. “Active visual category learning.” 2011. Web. 22 Apr 2019.

Vancouver:

Vijayanarasimhan S. Active visual category learning. [Internet] [Thesis]. University of Texas – Austin; 2011. [cited 2019 Apr 22]. Available from: http://hdl.handle.net/2152/ETD-UT-2011-05-3014.

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

Council of Science Editors:

Vijayanarasimhan S. Active visual category learning. [Thesis]. University of Texas – Austin; 2011. Available from: http://hdl.handle.net/2152/ETD-UT-2011-05-3014

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


University of Texas – Austin

26. Lee, Yong Jae, 1984-. Visual object category discovery in images and videos.

Degree: Electrical and Computer Engineering, 2012, University of Texas – Austin

 The current trend in visual recognition research is to place a strict division between the supervised and unsupervised learning paradigms, which is problematic for two… (more)

Subjects/Keywords: Unsupervised learning; Visual category discovery; Image and video segmentation; Video summarization

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APA (6th Edition):

Lee, Yong Jae, 1. (2012). Visual object category discovery in images and videos. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/ETD-UT-2012-05-5381

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, Yong Jae, 1984-. “Visual object category discovery in images and videos.” 2012. Thesis, University of Texas – Austin. Accessed April 22, 2019. http://hdl.handle.net/2152/ETD-UT-2012-05-5381.

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

MLA Handbook (7th Edition):

Lee, Yong Jae, 1984-. “Visual object category discovery in images and videos.” 2012. Web. 22 Apr 2019.

Vancouver:

Lee, Yong Jae 1. Visual object category discovery in images and videos. [Internet] [Thesis]. University of Texas – Austin; 2012. [cited 2019 Apr 22]. Available from: http://hdl.handle.net/2152/ETD-UT-2012-05-5381.

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

Council of Science Editors:

Lee, Yong Jae 1. Visual object category discovery in images and videos. [Thesis]. University of Texas – Austin; 2012. Available from: http://hdl.handle.net/2152/ETD-UT-2012-05-5381

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

27. Xia, Lu, active 21st century. Human detection and action recognition using depth information by Kinect.

Degree: Electrical and Computer Engineering, 2012, University of Texas – Austin

 Traditional computer vision algorithms depend on information taken by visible-light cameras. But there are inherent limitations of this data source, e.g. they are sensitive to… (more)

Subjects/Keywords: Human detection; Action recognition; Kinect; Depth image; 3D; View-invariant

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APA (6th Edition):

Xia, Lu, a. 2. c. (2012). Human detection and action recognition using depth information by Kinect. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/ETD-UT-2012-05-5509

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):

Xia, Lu, active 21st century. “Human detection and action recognition using depth information by Kinect.” 2012. Thesis, University of Texas – Austin. Accessed April 22, 2019. http://hdl.handle.net/2152/ETD-UT-2012-05-5509.

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

MLA Handbook (7th Edition):

Xia, Lu, active 21st century. “Human detection and action recognition using depth information by Kinect.” 2012. Web. 22 Apr 2019.

Vancouver:

Xia, Lu a2c. Human detection and action recognition using depth information by Kinect. [Internet] [Thesis]. University of Texas – Austin; 2012. [cited 2019 Apr 22]. Available from: http://hdl.handle.net/2152/ETD-UT-2012-05-5509.

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

Council of Science Editors:

Xia, Lu a2c. Human detection and action recognition using depth information by Kinect. [Thesis]. University of Texas – Austin; 2012. Available from: http://hdl.handle.net/2152/ETD-UT-2012-05-5509

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

28. Hwang, Sung Ju. Discriminative object categorization with external semantic knowledge.

Degree: Computer Sciences, 2013, University of Texas – Austin

 Visual object category recognition is one of the most challenging problems in computer vision. Even assuming that we can obtain a near-perfect instance level representation… (more)

Subjects/Keywords: Computer vision; Machine learning; Object categorization; Object recognition; Feature learning; Metric learning; Multitask learning; Multiple kernel learning; Embedding; Manifold learning; Regularization method; Structured sparsity; Structured regularization; Hierarchical model

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

APA (6th Edition):

Hwang, S. J. (2013). Discriminative object categorization with external semantic knowledge. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/21320

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):

Hwang, Sung Ju. “Discriminative object categorization with external semantic knowledge.” 2013. Thesis, University of Texas – Austin. Accessed April 22, 2019. http://hdl.handle.net/2152/21320.

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

MLA Handbook (7th Edition):

Hwang, Sung Ju. “Discriminative object categorization with external semantic knowledge.” 2013. Web. 22 Apr 2019.

Vancouver:

Hwang SJ. Discriminative object categorization with external semantic knowledge. [Internet] [Thesis]. University of Texas – Austin; 2013. [cited 2019 Apr 22]. Available from: http://hdl.handle.net/2152/21320.

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

Council of Science Editors:

Hwang SJ. Discriminative object categorization with external semantic knowledge. [Thesis]. University of Texas – Austin; 2013. Available from: http://hdl.handle.net/2152/21320

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

29. Kim, Jaechul. Region detection and matching for object recognition.

Degree: Computer Sciences, 2013, University of Texas – Austin

 In this thesis, I explore region detection and consider its impact on image matching for exemplar-based object recognition. Detecting regions is important to provide semantically… (more)

Subjects/Keywords: Computer vision; Object recognition; Feature detection; Segmentation; Image matching; Shape

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

APA (6th Edition):

Kim, J. (2013). Region detection and matching for object recognition. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/21261

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):

Kim, Jaechul. “Region detection and matching for object recognition.” 2013. Thesis, University of Texas – Austin. Accessed April 22, 2019. http://hdl.handle.net/2152/21261.

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

MLA Handbook (7th Edition):

Kim, Jaechul. “Region detection and matching for object recognition.” 2013. Web. 22 Apr 2019.

Vancouver:

Kim J. Region detection and matching for object recognition. [Internet] [Thesis]. University of Texas – Austin; 2013. [cited 2019 Apr 22]. Available from: http://hdl.handle.net/2152/21261.

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

Council of Science Editors:

Kim J. Region detection and matching for object recognition. [Thesis]. University of Texas – Austin; 2013. Available from: http://hdl.handle.net/2152/21261

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

30. Bandla, Sunil. Active learning of an action detector on untrimmed videos.

Degree: Computer Sciences, 2013, University of Texas – Austin

 Collecting and annotating videos of realistic human actions is tedious, yet critical for training action recognition systems. We propose a method to actively request the… (more)

Subjects/Keywords: Computer vision; Action detection; Active learning

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

APA (6th Edition):

Bandla, S. (2013). Active learning of an action detector on untrimmed videos. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/25260

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):

Bandla, Sunil. “Active learning of an action detector on untrimmed videos.” 2013. Thesis, University of Texas – Austin. Accessed April 22, 2019. http://hdl.handle.net/2152/25260.

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

MLA Handbook (7th Edition):

Bandla, Sunil. “Active learning of an action detector on untrimmed videos.” 2013. Web. 22 Apr 2019.

Vancouver:

Bandla S. Active learning of an action detector on untrimmed videos. [Internet] [Thesis]. University of Texas – Austin; 2013. [cited 2019 Apr 22]. Available from: http://hdl.handle.net/2152/25260.

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

Council of Science Editors:

Bandla S. Active learning of an action detector on untrimmed videos. [Thesis]. University of Texas – Austin; 2013. Available from: http://hdl.handle.net/2152/25260

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

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