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You searched for +publisher:"University of North Carolina" +contributor:("Berg, Tamara"). Showing records 1 – 10 of 10 total matches.

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University of North Carolina

1. Kiapour, Mohammadhadi. LARGE SCALE VISUAL RECOGNITION OF CLOTHING, PEOPLE AND STYLES.

Degree: Computer Science, 2015, University of North Carolina

 Clothing recognition is a societally and commercially important yet extremely challenging problem due to large variations in clothing appearance, layering, style, body shape and pose.… (more)

Subjects/Keywords: Computer science; College of Arts and Sciences; Department of Computer Science

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

APA (6th Edition):

Kiapour, M. (2015). LARGE SCALE VISUAL RECOGNITION OF CLOTHING, PEOPLE AND STYLES. (Thesis). University of North Carolina. Retrieved from https://cdr.lib.unc.edu/record/uuid:d5241918-b3f4-4089-86de-f9d957179775

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):

Kiapour, Mohammadhadi. “LARGE SCALE VISUAL RECOGNITION OF CLOTHING, PEOPLE AND STYLES.” 2015. Thesis, University of North Carolina. Accessed December 04, 2020. https://cdr.lib.unc.edu/record/uuid:d5241918-b3f4-4089-86de-f9d957179775.

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

MLA Handbook (7th Edition):

Kiapour, Mohammadhadi. “LARGE SCALE VISUAL RECOGNITION OF CLOTHING, PEOPLE AND STYLES.” 2015. Web. 04 Dec 2020.

Vancouver:

Kiapour M. LARGE SCALE VISUAL RECOGNITION OF CLOTHING, PEOPLE AND STYLES. [Internet] [Thesis]. University of North Carolina; 2015. [cited 2020 Dec 04]. Available from: https://cdr.lib.unc.edu/record/uuid:d5241918-b3f4-4089-86de-f9d957179775.

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

Council of Science Editors:

Kiapour M. LARGE SCALE VISUAL RECOGNITION OF CLOTHING, PEOPLE AND STYLES. [Thesis]. University of North Carolina; 2015. Available from: https://cdr.lib.unc.edu/record/uuid:d5241918-b3f4-4089-86de-f9d957179775

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


University of North Carolina

2. Ordonez Roman, Vicente. Language and Perceptual Categorization in Computational Visual Recognition.

Degree: Computer Science, 2015, University of North Carolina

 Computational visual recognition or giving computers the ability to understand images as well as humans do is a core problem in Computer Vision. Traditional recognition… (more)

Subjects/Keywords: Computer science; College of Arts and Sciences; Department of Computer Science

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APA (6th Edition):

Ordonez Roman, V. (2015). Language and Perceptual Categorization in Computational Visual Recognition. (Thesis). University of North Carolina. Retrieved from https://cdr.lib.unc.edu/record/uuid:188ef51f-d3dc-4216-97ea-07da5109a1a6

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):

Ordonez Roman, Vicente. “Language and Perceptual Categorization in Computational Visual Recognition.” 2015. Thesis, University of North Carolina. Accessed December 04, 2020. https://cdr.lib.unc.edu/record/uuid:188ef51f-d3dc-4216-97ea-07da5109a1a6.

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

MLA Handbook (7th Edition):

Ordonez Roman, Vicente. “Language and Perceptual Categorization in Computational Visual Recognition.” 2015. Web. 04 Dec 2020.

Vancouver:

Ordonez Roman V. Language and Perceptual Categorization in Computational Visual Recognition. [Internet] [Thesis]. University of North Carolina; 2015. [cited 2020 Dec 04]. Available from: https://cdr.lib.unc.edu/record/uuid:188ef51f-d3dc-4216-97ea-07da5109a1a6.

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

Council of Science Editors:

Ordonez Roman V. Language and Perceptual Categorization in Computational Visual Recognition. [Thesis]. University of North Carolina; 2015. Available from: https://cdr.lib.unc.edu/record/uuid:188ef51f-d3dc-4216-97ea-07da5109a1a6

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


University of North Carolina

3. Han, Xufeng. LEARNING WITH MORE DATA AND BETTER MODELS FOR VISUAL SIMILARITY AND DIFFERENTIATION.

Degree: Computer Science, 2016, University of North Carolina

 This thesis studies machine learning problems involved in visual recognition on a variety of computer vision tasks. It attacks the challenge of scaling-up learning to… (more)

Subjects/Keywords: College of Arts and Sciences; Department of Computer Science

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

APA (6th Edition):

Han, X. (2016). LEARNING WITH MORE DATA AND BETTER MODELS FOR VISUAL SIMILARITY AND DIFFERENTIATION. (Thesis). University of North Carolina. Retrieved from https://cdr.lib.unc.edu/record/uuid:e712c6a4-e4ff-4c3b-8646-a6c1c872f042

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):

Han, Xufeng. “LEARNING WITH MORE DATA AND BETTER MODELS FOR VISUAL SIMILARITY AND DIFFERENTIATION.” 2016. Thesis, University of North Carolina. Accessed December 04, 2020. https://cdr.lib.unc.edu/record/uuid:e712c6a4-e4ff-4c3b-8646-a6c1c872f042.

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

MLA Handbook (7th Edition):

Han, Xufeng. “LEARNING WITH MORE DATA AND BETTER MODELS FOR VISUAL SIMILARITY AND DIFFERENTIATION.” 2016. Web. 04 Dec 2020.

Vancouver:

Han X. LEARNING WITH MORE DATA AND BETTER MODELS FOR VISUAL SIMILARITY AND DIFFERENTIATION. [Internet] [Thesis]. University of North Carolina; 2016. [cited 2020 Dec 04]. Available from: https://cdr.lib.unc.edu/record/uuid:e712c6a4-e4ff-4c3b-8646-a6c1c872f042.

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

Council of Science Editors:

Han X. LEARNING WITH MORE DATA AND BETTER MODELS FOR VISUAL SIMILARITY AND DIFFERENTIATION. [Thesis]. University of North Carolina; 2016. Available from: https://cdr.lib.unc.edu/record/uuid:e712c6a4-e4ff-4c3b-8646-a6c1c872f042

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


University of North Carolina

4. Liu, Wei. Localizing Objects Fast and Accurately.

Degree: Computer Science, 2016, University of North Carolina

 A fundamental problem in computer vision is knowing what is in the image and where it is. We develop models to localize objects of multiple… (more)

Subjects/Keywords: College of Arts and Sciences; Department of Computer Science

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APA (6th Edition):

Liu, W. (2016). Localizing Objects Fast and Accurately. (Thesis). University of North Carolina. Retrieved from https://cdr.lib.unc.edu/record/uuid:46218f57-e071-4cf8-9f51-0065bede73f9

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):

Liu, Wei. “Localizing Objects Fast and Accurately.” 2016. Thesis, University of North Carolina. Accessed December 04, 2020. https://cdr.lib.unc.edu/record/uuid:46218f57-e071-4cf8-9f51-0065bede73f9.

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

MLA Handbook (7th Edition):

Liu, Wei. “Localizing Objects Fast and Accurately.” 2016. Web. 04 Dec 2020.

Vancouver:

Liu W. Localizing Objects Fast and Accurately. [Internet] [Thesis]. University of North Carolina; 2016. [cited 2020 Dec 04]. Available from: https://cdr.lib.unc.edu/record/uuid:46218f57-e071-4cf8-9f51-0065bede73f9.

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

Council of Science Editors:

Liu W. Localizing Objects Fast and Accurately. [Thesis]. University of North Carolina; 2016. Available from: https://cdr.lib.unc.edu/record/uuid:46218f57-e071-4cf8-9f51-0065bede73f9

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


University of North Carolina

5. Ji, Dinghuang. Data-driven 3D Reconstruction and View Synthesis of Dynamic Scene Elements.

Degree: Computer Science, 2018, University of North Carolina

 Our world is filled with living beings and other dynamic elements. It is important to record dynamic things and events for the sake of education,… (more)

Subjects/Keywords: College of Arts and Sciences; Department of Computer Science

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APA (6th Edition):

Ji, D. (2018). Data-driven 3D Reconstruction and View Synthesis of Dynamic Scene Elements. (Thesis). University of North Carolina. Retrieved from https://cdr.lib.unc.edu/record/uuid:eaeceb64-ad74-416d-a0b6-b4ee48512f8d

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):

Ji, Dinghuang. “Data-driven 3D Reconstruction and View Synthesis of Dynamic Scene Elements.” 2018. Thesis, University of North Carolina. Accessed December 04, 2020. https://cdr.lib.unc.edu/record/uuid:eaeceb64-ad74-416d-a0b6-b4ee48512f8d.

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

MLA Handbook (7th Edition):

Ji, Dinghuang. “Data-driven 3D Reconstruction and View Synthesis of Dynamic Scene Elements.” 2018. Web. 04 Dec 2020.

Vancouver:

Ji D. Data-driven 3D Reconstruction and View Synthesis of Dynamic Scene Elements. [Internet] [Thesis]. University of North Carolina; 2018. [cited 2020 Dec 04]. Available from: https://cdr.lib.unc.edu/record/uuid:eaeceb64-ad74-416d-a0b6-b4ee48512f8d.

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

Council of Science Editors:

Ji D. Data-driven 3D Reconstruction and View Synthesis of Dynamic Scene Elements. [Thesis]. University of North Carolina; 2018. Available from: https://cdr.lib.unc.edu/record/uuid:eaeceb64-ad74-416d-a0b6-b4ee48512f8d

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


University of North Carolina

6. Zheng, Enliang. TOWARD 3D RECONSTRUCTION OF STATIC AND DYNAMIC OBJECTS.

Degree: Computer Science, 2016, University of North Carolina

 The goal of image-based 3D reconstruction is to construct a spatial understanding of the world from a collection of images. For applications that seek to… (more)

Subjects/Keywords: College of Arts and Sciences; Department of Computer Science

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APA (6th Edition):

Zheng, E. (2016). TOWARD 3D RECONSTRUCTION OF STATIC AND DYNAMIC OBJECTS. (Thesis). University of North Carolina. Retrieved from https://cdr.lib.unc.edu/record/uuid:adaabbef-0021-4d4b-87b3-4b6f9985d97d

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):

Zheng, Enliang. “TOWARD 3D RECONSTRUCTION OF STATIC AND DYNAMIC OBJECTS.” 2016. Thesis, University of North Carolina. Accessed December 04, 2020. https://cdr.lib.unc.edu/record/uuid:adaabbef-0021-4d4b-87b3-4b6f9985d97d.

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

MLA Handbook (7th Edition):

Zheng, Enliang. “TOWARD 3D RECONSTRUCTION OF STATIC AND DYNAMIC OBJECTS.” 2016. Web. 04 Dec 2020.

Vancouver:

Zheng E. TOWARD 3D RECONSTRUCTION OF STATIC AND DYNAMIC OBJECTS. [Internet] [Thesis]. University of North Carolina; 2016. [cited 2020 Dec 04]. Available from: https://cdr.lib.unc.edu/record/uuid:adaabbef-0021-4d4b-87b3-4b6f9985d97d.

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

Council of Science Editors:

Zheng E. TOWARD 3D RECONSTRUCTION OF STATIC AND DYNAMIC OBJECTS. [Thesis]. University of North Carolina; 2016. Available from: https://cdr.lib.unc.edu/record/uuid:adaabbef-0021-4d4b-87b3-4b6f9985d97d

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


University of North Carolina

7. Xu, Yi. Toward Robust Video Event Detection and Retrieval Under Adversarial Constraints.

Degree: Computer Science, 2016, University of North Carolina

 The continuous stream of videos that are uploaded and shared on the Internet has been leveraged by computer vision researchers for a myriad of detection… (more)

Subjects/Keywords: College of Arts and Sciences; Department of Computer Science

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

APA (6th Edition):

Xu, Y. (2016). Toward Robust Video Event Detection and Retrieval Under Adversarial Constraints. (Thesis). University of North Carolina. Retrieved from https://cdr.lib.unc.edu/record/uuid:926a5d94-944d-4c61-963f-b863b0dc1f41

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):

Xu, Yi. “Toward Robust Video Event Detection and Retrieval Under Adversarial Constraints.” 2016. Thesis, University of North Carolina. Accessed December 04, 2020. https://cdr.lib.unc.edu/record/uuid:926a5d94-944d-4c61-963f-b863b0dc1f41.

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

MLA Handbook (7th Edition):

Xu, Yi. “Toward Robust Video Event Detection and Retrieval Under Adversarial Constraints.” 2016. Web. 04 Dec 2020.

Vancouver:

Xu Y. Toward Robust Video Event Detection and Retrieval Under Adversarial Constraints. [Internet] [Thesis]. University of North Carolina; 2016. [cited 2020 Dec 04]. Available from: https://cdr.lib.unc.edu/record/uuid:926a5d94-944d-4c61-963f-b863b0dc1f41.

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

Council of Science Editors:

Xu Y. Toward Robust Video Event Detection and Retrieval Under Adversarial Constraints. [Thesis]. University of North Carolina; 2016. Available from: https://cdr.lib.unc.edu/record/uuid:926a5d94-944d-4c61-963f-b863b0dc1f41

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


University of North Carolina

8. Vittayakorn, Sirion. Visual attribute discovery and analyses from Web data.

Degree: Computer Science, 2016, University of North Carolina

 Visual attributes are important for describing and understanding an object’s appearance. For an object classification or recognition task, an algorithm needs to infer the visual… (more)

Subjects/Keywords: College of Arts and Sciences; Department of Computer Science

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

APA (6th Edition):

Vittayakorn, S. (2016). Visual attribute discovery and analyses from Web data. (Thesis). University of North Carolina. Retrieved from https://cdr.lib.unc.edu/record/uuid:e99f13f0-8689-49cc-a15d-3269cb0e4732

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):

Vittayakorn, Sirion. “Visual attribute discovery and analyses from Web data.” 2016. Thesis, University of North Carolina. Accessed December 04, 2020. https://cdr.lib.unc.edu/record/uuid:e99f13f0-8689-49cc-a15d-3269cb0e4732.

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

MLA Handbook (7th Edition):

Vittayakorn, Sirion. “Visual attribute discovery and analyses from Web data.” 2016. Web. 04 Dec 2020.

Vancouver:

Vittayakorn S. Visual attribute discovery and analyses from Web data. [Internet] [Thesis]. University of North Carolina; 2016. [cited 2020 Dec 04]. Available from: https://cdr.lib.unc.edu/record/uuid:e99f13f0-8689-49cc-a15d-3269cb0e4732.

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

Council of Science Editors:

Vittayakorn S. Visual attribute discovery and analyses from Web data. [Thesis]. University of North Carolina; 2016. Available from: https://cdr.lib.unc.edu/record/uuid:e99f13f0-8689-49cc-a15d-3269cb0e4732

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


University of North Carolina

9. Stanley, Natalie. Adapting Community Detection Approaches to Large, Multilayer, and Attributed Networks.

Degree: 2018, University of North Carolina

 Networks have become a common data mining tool to encode relational definitions between a set of entities. Whether studying biological correlations, or communication between individuals… (more)

Subjects/Keywords: School of Medicine; Curriculum in Bioinformatics and Computational Biology

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

APA (6th Edition):

Stanley, N. (2018). Adapting Community Detection Approaches to Large, Multilayer, and Attributed Networks. (Thesis). University of North Carolina. Retrieved from https://cdr.lib.unc.edu/record/uuid:887b1903-bf0f-4667-b678-381ce5646a38

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):

Stanley, Natalie. “Adapting Community Detection Approaches to Large, Multilayer, and Attributed Networks.” 2018. Thesis, University of North Carolina. Accessed December 04, 2020. https://cdr.lib.unc.edu/record/uuid:887b1903-bf0f-4667-b678-381ce5646a38.

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

MLA Handbook (7th Edition):

Stanley, Natalie. “Adapting Community Detection Approaches to Large, Multilayer, and Attributed Networks.” 2018. Web. 04 Dec 2020.

Vancouver:

Stanley N. Adapting Community Detection Approaches to Large, Multilayer, and Attributed Networks. [Internet] [Thesis]. University of North Carolina; 2018. [cited 2020 Dec 04]. Available from: https://cdr.lib.unc.edu/record/uuid:887b1903-bf0f-4667-b678-381ce5646a38.

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

Council of Science Editors:

Stanley N. Adapting Community Detection Approaches to Large, Multilayer, and Attributed Networks. [Thesis]. University of North Carolina; 2018. Available from: https://cdr.lib.unc.edu/record/uuid:887b1903-bf0f-4667-b678-381ce5646a38

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


University of North Carolina

10. YANG, SHAN. NON-RIGID BODY MECHANICAL PROPERTY RECOVERY FROM IMAGES AND VIDEOS.

Degree: Computer Science, 2018, University of North Carolina

 Material property has great importance in surgical simulation and virtual reality. The mechanical properties of the human soft tissue are critical to characterize the tissue… (more)

Subjects/Keywords: College of Arts and Sciences; Department of Computer Science

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

APA (6th Edition):

YANG, S. (2018). NON-RIGID BODY MECHANICAL PROPERTY RECOVERY FROM IMAGES AND VIDEOS. (Thesis). University of North Carolina. Retrieved from https://cdr.lib.unc.edu/record/uuid:7841341b-857a-4e7d-9872-05d23b63e56d

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

Chicago Manual of Style (16th Edition):

YANG, SHAN. “NON-RIGID BODY MECHANICAL PROPERTY RECOVERY FROM IMAGES AND VIDEOS.” 2018. Thesis, University of North Carolina. Accessed December 04, 2020. https://cdr.lib.unc.edu/record/uuid:7841341b-857a-4e7d-9872-05d23b63e56d.

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

MLA Handbook (7th Edition):

YANG, SHAN. “NON-RIGID BODY MECHANICAL PROPERTY RECOVERY FROM IMAGES AND VIDEOS.” 2018. Web. 04 Dec 2020.

Vancouver:

YANG S. NON-RIGID BODY MECHANICAL PROPERTY RECOVERY FROM IMAGES AND VIDEOS. [Internet] [Thesis]. University of North Carolina; 2018. [cited 2020 Dec 04]. Available from: https://cdr.lib.unc.edu/record/uuid:7841341b-857a-4e7d-9872-05d23b63e56d.

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

Council of Science Editors:

YANG S. NON-RIGID BODY MECHANICAL PROPERTY RECOVERY FROM IMAGES AND VIDEOS. [Thesis]. University of North Carolina; 2018. Available from: https://cdr.lib.unc.edu/record/uuid:7841341b-857a-4e7d-9872-05d23b63e56d

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

.