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You searched for +publisher:"Temple University" +contributor:("Ling, Haibin"). Showing records 1 – 14 of 14 total matches.

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Temple University

1. Liang, Pengpeng. Techniques for Object Tracking: Algorithms and Benchmarks.

Degree: PhD, 2016, Temple University

Computer and Information Science

Visual object tracking is a fundamental computer vision task, and has a wide range of applications including video surveillance, human computer… (more)

Subjects/Keywords: Computer science

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APA (6th Edition):

Liang, P. (2016). Techniques for Object Tracking: Algorithms and Benchmarks. (Doctoral Dissertation). Temple University. Retrieved from http://digital.library.temple.edu/u?/p245801coll10,413757

Chicago Manual of Style (16th Edition):

Liang, Pengpeng. “Techniques for Object Tracking: Algorithms and Benchmarks.” 2016. Doctoral Dissertation, Temple University. Accessed October 20, 2020. http://digital.library.temple.edu/u?/p245801coll10,413757.

MLA Handbook (7th Edition):

Liang, Pengpeng. “Techniques for Object Tracking: Algorithms and Benchmarks.” 2016. Web. 20 Oct 2020.

Vancouver:

Liang P. Techniques for Object Tracking: Algorithms and Benchmarks. [Internet] [Doctoral dissertation]. Temple University; 2016. [cited 2020 Oct 20]. Available from: http://digital.library.temple.edu/u?/p245801coll10,413757.

Council of Science Editors:

Liang P. Techniques for Object Tracking: Algorithms and Benchmarks. [Doctoral Dissertation]. Temple University; 2016. Available from: http://digital.library.temple.edu/u?/p245801coll10,413757


Temple University

2. Nuzhnaya, Tatyana. ANALYSIS OF ANATOMICAL BRANCHING STRUCTURES.

Degree: PhD, 2015, Temple University

Computer and Information Science

Development of state-of-the-art medical imaging modalities such as Magnetic Resonance Imaging, Computed Tomography, Galactography, MR Diffusion Tensor Imaging, and Tomosynthesis plays… (more)

Subjects/Keywords: Computer science; Artificial intelligence; Medical imaging and radiology;

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APA (6th Edition):

Nuzhnaya, T. (2015). ANALYSIS OF ANATOMICAL BRANCHING STRUCTURES. (Doctoral Dissertation). Temple University. Retrieved from http://digital.library.temple.edu/u?/p245801coll10,322471

Chicago Manual of Style (16th Edition):

Nuzhnaya, Tatyana. “ANALYSIS OF ANATOMICAL BRANCHING STRUCTURES.” 2015. Doctoral Dissertation, Temple University. Accessed October 20, 2020. http://digital.library.temple.edu/u?/p245801coll10,322471.

MLA Handbook (7th Edition):

Nuzhnaya, Tatyana. “ANALYSIS OF ANATOMICAL BRANCHING STRUCTURES.” 2015. Web. 20 Oct 2020.

Vancouver:

Nuzhnaya T. ANALYSIS OF ANATOMICAL BRANCHING STRUCTURES. [Internet] [Doctoral dissertation]. Temple University; 2015. [cited 2020 Oct 20]. Available from: http://digital.library.temple.edu/u?/p245801coll10,322471.

Council of Science Editors:

Nuzhnaya T. ANALYSIS OF ANATOMICAL BRANCHING STRUCTURES. [Doctoral Dissertation]. Temple University; 2015. Available from: http://digital.library.temple.edu/u?/p245801coll10,322471


Temple University

3. Yang, Xingwei. Shape Based Object Detection and Recognition in Silhouettes and Real Images.

Degree: PhD, 2011, Temple University

Computer and Information Science

Shape is very essential for detecting and recognizing objects. It is robust to illumination, color changes. Human can recognize objects just… (more)

Subjects/Keywords: Computer Science; jigsaw puzzle; object detection; Shape retrieval

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APA (6th Edition):

Yang, X. (2011). Shape Based Object Detection and Recognition in Silhouettes and Real Images. (Doctoral Dissertation). Temple University. Retrieved from http://digital.library.temple.edu/u?/p245801coll10,111091

Chicago Manual of Style (16th Edition):

Yang, Xingwei. “Shape Based Object Detection and Recognition in Silhouettes and Real Images.” 2011. Doctoral Dissertation, Temple University. Accessed October 20, 2020. http://digital.library.temple.edu/u?/p245801coll10,111091.

MLA Handbook (7th Edition):

Yang, Xingwei. “Shape Based Object Detection and Recognition in Silhouettes and Real Images.” 2011. Web. 20 Oct 2020.

Vancouver:

Yang X. Shape Based Object Detection and Recognition in Silhouettes and Real Images. [Internet] [Doctoral dissertation]. Temple University; 2011. [cited 2020 Oct 20]. Available from: http://digital.library.temple.edu/u?/p245801coll10,111091.

Council of Science Editors:

Yang X. Shape Based Object Detection and Recognition in Silhouettes and Real Images. [Doctoral Dissertation]. Temple University; 2011. Available from: http://digital.library.temple.edu/u?/p245801coll10,111091


Temple University

4. An, Li. LARGE-SCALE DATA ANALYSIS OF GENE EXPRESSION MAPS OBTAINED BY VOXELATION.

Degree: PhD, 2012, Temple University

Computer and Information Science

Gene expression signatures in the mammalian brain hold the key to understanding neural development and neurological diseases, and gene expression profiles… (more)

Subjects/Keywords: Computer science; Bioinformatics; Data mining; Gene expression maps; Machine learning; Voxelation

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APA (6th Edition):

An, L. (2012). LARGE-SCALE DATA ANALYSIS OF GENE EXPRESSION MAPS OBTAINED BY VOXELATION. (Doctoral Dissertation). Temple University. Retrieved from http://digital.library.temple.edu/u?/p245801coll10,183204

Chicago Manual of Style (16th Edition):

An, Li. “LARGE-SCALE DATA ANALYSIS OF GENE EXPRESSION MAPS OBTAINED BY VOXELATION.” 2012. Doctoral Dissertation, Temple University. Accessed October 20, 2020. http://digital.library.temple.edu/u?/p245801coll10,183204.

MLA Handbook (7th Edition):

An, Li. “LARGE-SCALE DATA ANALYSIS OF GENE EXPRESSION MAPS OBTAINED BY VOXELATION.” 2012. Web. 20 Oct 2020.

Vancouver:

An L. LARGE-SCALE DATA ANALYSIS OF GENE EXPRESSION MAPS OBTAINED BY VOXELATION. [Internet] [Doctoral dissertation]. Temple University; 2012. [cited 2020 Oct 20]. Available from: http://digital.library.temple.edu/u?/p245801coll10,183204.

Council of Science Editors:

An L. LARGE-SCALE DATA ANALYSIS OF GENE EXPRESSION MAPS OBTAINED BY VOXELATION. [Doctoral Dissertation]. Temple University; 2012. Available from: http://digital.library.temple.edu/u?/p245801coll10,183204


Temple University

5. Ma, Tianyang. Graph-based Inference with Constraints for Object Detection and Segmentation.

Degree: PhD, 2013, Temple University

Computer and Information Science

For many fundamental problems of computer vision, adopting a graph-based framework can be straight-forward and very effective. In this thesis, I… (more)

Subjects/Keywords: Computer science;

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APA (6th Edition):

Ma, T. (2013). Graph-based Inference with Constraints for Object Detection and Segmentation. (Doctoral Dissertation). Temple University. Retrieved from http://digital.library.temple.edu/u?/p245801coll10,231622

Chicago Manual of Style (16th Edition):

Ma, Tianyang. “Graph-based Inference with Constraints for Object Detection and Segmentation.” 2013. Doctoral Dissertation, Temple University. Accessed October 20, 2020. http://digital.library.temple.edu/u?/p245801coll10,231622.

MLA Handbook (7th Edition):

Ma, Tianyang. “Graph-based Inference with Constraints for Object Detection and Segmentation.” 2013. Web. 20 Oct 2020.

Vancouver:

Ma T. Graph-based Inference with Constraints for Object Detection and Segmentation. [Internet] [Doctoral dissertation]. Temple University; 2013. [cited 2020 Oct 20]. Available from: http://digital.library.temple.edu/u?/p245801coll10,231622.

Council of Science Editors:

Ma T. Graph-based Inference with Constraints for Object Detection and Segmentation. [Doctoral Dissertation]. Temple University; 2013. Available from: http://digital.library.temple.edu/u?/p245801coll10,231622


Temple University

6. DU, LIANG. Exploiting Competition Relationship for Robust Visual Recognition.

Degree: PhD, 2015, Temple University

Computer and Information Science

Leveraging task relatedness has been proven to be beneficial in many machine learning tasks. Extensive researches has been done to exploit… (more)

Subjects/Keywords: Computer science; Information science; Information technology;

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APA (6th Edition):

DU, L. (2015). Exploiting Competition Relationship for Robust Visual Recognition. (Doctoral Dissertation). Temple University. Retrieved from http://digital.library.temple.edu/u?/p245801coll10,335545

Chicago Manual of Style (16th Edition):

DU, LIANG. “Exploiting Competition Relationship for Robust Visual Recognition.” 2015. Doctoral Dissertation, Temple University. Accessed October 20, 2020. http://digital.library.temple.edu/u?/p245801coll10,335545.

MLA Handbook (7th Edition):

DU, LIANG. “Exploiting Competition Relationship for Robust Visual Recognition.” 2015. Web. 20 Oct 2020.

Vancouver:

DU L. Exploiting Competition Relationship for Robust Visual Recognition. [Internet] [Doctoral dissertation]. Temple University; 2015. [cited 2020 Oct 20]. Available from: http://digital.library.temple.edu/u?/p245801coll10,335545.

Council of Science Editors:

DU L. Exploiting Competition Relationship for Robust Visual Recognition. [Doctoral Dissertation]. Temple University; 2015. Available from: http://digital.library.temple.edu/u?/p245801coll10,335545


Temple University

7. Shu, Le. Graph and Subspace Learning for Domain Adaptation.

Degree: PhD, 2015, Temple University

Computer and Information Science

In many practical problems, given that the instances in the training and test may be drawn from different distributions, traditional supervised… (more)

Subjects/Keywords: Computer science; Computer engineering

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APA (6th Edition):

Shu, L. (2015). Graph and Subspace Learning for Domain Adaptation. (Doctoral Dissertation). Temple University. Retrieved from http://digital.library.temple.edu/u?/p245801coll10,363757

Chicago Manual of Style (16th Edition):

Shu, Le. “Graph and Subspace Learning for Domain Adaptation.” 2015. Doctoral Dissertation, Temple University. Accessed October 20, 2020. http://digital.library.temple.edu/u?/p245801coll10,363757.

MLA Handbook (7th Edition):

Shu, Le. “Graph and Subspace Learning for Domain Adaptation.” 2015. Web. 20 Oct 2020.

Vancouver:

Shu L. Graph and Subspace Learning for Domain Adaptation. [Internet] [Doctoral dissertation]. Temple University; 2015. [cited 2020 Oct 20]. Available from: http://digital.library.temple.edu/u?/p245801coll10,363757.

Council of Science Editors:

Shu L. Graph and Subspace Learning for Domain Adaptation. [Doctoral Dissertation]. Temple University; 2015. Available from: http://digital.library.temple.edu/u?/p245801coll10,363757


Temple University

8. Huang, Xueli. Achieving Data Privacy and Security in Cloud.

Degree: PhD, 2016, Temple University

Computer and Information Science

The growing concerns in term of the privacy of data stored in public cloud have restrained the widespread adoption of cloud… (more)

Subjects/Keywords: Computer science;

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APA (6th Edition):

Huang, X. (2016). Achieving Data Privacy and Security in Cloud. (Doctoral Dissertation). Temple University. Retrieved from http://digital.library.temple.edu/u?/p245801coll10,372805

Chicago Manual of Style (16th Edition):

Huang, Xueli. “Achieving Data Privacy and Security in Cloud.” 2016. Doctoral Dissertation, Temple University. Accessed October 20, 2020. http://digital.library.temple.edu/u?/p245801coll10,372805.

MLA Handbook (7th Edition):

Huang, Xueli. “Achieving Data Privacy and Security in Cloud.” 2016. Web. 20 Oct 2020.

Vancouver:

Huang X. Achieving Data Privacy and Security in Cloud. [Internet] [Doctoral dissertation]. Temple University; 2016. [cited 2020 Oct 20]. Available from: http://digital.library.temple.edu/u?/p245801coll10,372805.

Council of Science Editors:

Huang X. Achieving Data Privacy and Security in Cloud. [Doctoral Dissertation]. Temple University; 2016. Available from: http://digital.library.temple.edu/u?/p245801coll10,372805


Temple University

9. Li, Peiyi. Exploration of 3D Images to Understand 3D Real World.

Degree: PhD, 2016, Temple University

Computer and Information Science

Our world is composed of 3-dimension objects. Every one of us is living in a world with X, Y and Z… (more)

Subjects/Keywords: Computer science; Artificial intelligence;

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APA (6th Edition):

Li, P. (2016). Exploration of 3D Images to Understand 3D Real World. (Doctoral Dissertation). Temple University. Retrieved from http://digital.library.temple.edu/u?/p245801coll10,413481

Chicago Manual of Style (16th Edition):

Li, Peiyi. “Exploration of 3D Images to Understand 3D Real World.” 2016. Doctoral Dissertation, Temple University. Accessed October 20, 2020. http://digital.library.temple.edu/u?/p245801coll10,413481.

MLA Handbook (7th Edition):

Li, Peiyi. “Exploration of 3D Images to Understand 3D Real World.” 2016. Web. 20 Oct 2020.

Vancouver:

Li P. Exploration of 3D Images to Understand 3D Real World. [Internet] [Doctoral dissertation]. Temple University; 2016. [cited 2020 Oct 20]. Available from: http://digital.library.temple.edu/u?/p245801coll10,413481.

Council of Science Editors:

Li P. Exploration of 3D Images to Understand 3D Real World. [Doctoral Dissertation]. Temple University; 2016. Available from: http://digital.library.temple.edu/u?/p245801coll10,413481


Temple University

10. Deng, Zhuo. RGB-DEPTH IMAGE SEGMENTATION AND OBJECT RECOGNITION FOR INDOOR SCENES.

Degree: PhD, 2016, Temple University

Computer and Information Science

With the advent of Microsoft Kinect, the landscape of various vision-related tasks has been changed. Firstly, using an active infrared structured… (more)

Subjects/Keywords: Computer science

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APA (6th Edition):

Deng, Z. (2016). RGB-DEPTH IMAGE SEGMENTATION AND OBJECT RECOGNITION FOR INDOOR SCENES. (Doctoral Dissertation). Temple University. Retrieved from http://digital.library.temple.edu/u?/p245801coll10,427631

Chicago Manual of Style (16th Edition):

Deng, Zhuo. “RGB-DEPTH IMAGE SEGMENTATION AND OBJECT RECOGNITION FOR INDOOR SCENES.” 2016. Doctoral Dissertation, Temple University. Accessed October 20, 2020. http://digital.library.temple.edu/u?/p245801coll10,427631.

MLA Handbook (7th Edition):

Deng, Zhuo. “RGB-DEPTH IMAGE SEGMENTATION AND OBJECT RECOGNITION FOR INDOOR SCENES.” 2016. Web. 20 Oct 2020.

Vancouver:

Deng Z. RGB-DEPTH IMAGE SEGMENTATION AND OBJECT RECOGNITION FOR INDOOR SCENES. [Internet] [Doctoral dissertation]. Temple University; 2016. [cited 2020 Oct 20]. Available from: http://digital.library.temple.edu/u?/p245801coll10,427631.

Council of Science Editors:

Deng Z. RGB-DEPTH IMAGE SEGMENTATION AND OBJECT RECOGNITION FOR INDOOR SCENES. [Doctoral Dissertation]. Temple University; 2016. Available from: http://digital.library.temple.edu/u?/p245801coll10,427631


Temple University

11. Li, Nan. Algorithms for NP-hard Optimization Problems and Cluster Analysis.

Degree: PhD, 2017, Temple University

Computer and Information Science

The set cover problem, weighted set cover problem, minimum dominating set problem and minimum weighted dominating set problem are all classical… (more)

Subjects/Keywords: Computer science

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APA (6th Edition):

Li, N. (2017). Algorithms for NP-hard Optimization Problems and Cluster Analysis. (Doctoral Dissertation). Temple University. Retrieved from http://digital.library.temple.edu/u?/p245801coll10,482725

Chicago Manual of Style (16th Edition):

Li, Nan. “Algorithms for NP-hard Optimization Problems and Cluster Analysis.” 2017. Doctoral Dissertation, Temple University. Accessed October 20, 2020. http://digital.library.temple.edu/u?/p245801coll10,482725.

MLA Handbook (7th Edition):

Li, Nan. “Algorithms for NP-hard Optimization Problems and Cluster Analysis.” 2017. Web. 20 Oct 2020.

Vancouver:

Li N. Algorithms for NP-hard Optimization Problems and Cluster Analysis. [Internet] [Doctoral dissertation]. Temple University; 2017. [cited 2020 Oct 20]. Available from: http://digital.library.temple.edu/u?/p245801coll10,482725.

Council of Science Editors:

Li N. Algorithms for NP-hard Optimization Problems and Cluster Analysis. [Doctoral Dissertation]. Temple University; 2017. Available from: http://digital.library.temple.edu/u?/p245801coll10,482725


Temple University

12. Cheng, Erkang. Learning based Curvilinear Structure Analysis in Medical Images.

Degree: PhD, 2014, Temple University

Computer and Information Science

Analysis of curvilinear structures is an important problem in computer-aided diagnosis and image guided interventions with applications such as vessel structure… (more)

Subjects/Keywords: Computer science

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APA (6th Edition):

Cheng, E. (2014). Learning based Curvilinear Structure Analysis in Medical Images. (Doctoral Dissertation). Temple University. Retrieved from http://digital.library.temple.edu/u?/p245801coll10,284852

Chicago Manual of Style (16th Edition):

Cheng, Erkang. “Learning based Curvilinear Structure Analysis in Medical Images.” 2014. Doctoral Dissertation, Temple University. Accessed October 20, 2020. http://digital.library.temple.edu/u?/p245801coll10,284852.

MLA Handbook (7th Edition):

Cheng, Erkang. “Learning based Curvilinear Structure Analysis in Medical Images.” 2014. Web. 20 Oct 2020.

Vancouver:

Cheng E. Learning based Curvilinear Structure Analysis in Medical Images. [Internet] [Doctoral dissertation]. Temple University; 2014. [cited 2020 Oct 20]. Available from: http://digital.library.temple.edu/u?/p245801coll10,284852.

Council of Science Editors:

Cheng E. Learning based Curvilinear Structure Analysis in Medical Images. [Doctoral Dissertation]. Temple University; 2014. Available from: http://digital.library.temple.edu/u?/p245801coll10,284852


Temple University

13. Koknar-Tezel, Suzan. OPTIMAL SUBSEQUENCE BIJECTION AND CLASSIFICATION OF IMBALANCED DATA SETS.

Degree: PhD, 2011, Temple University

Computer and Information Science

Time series are common in many research fields. Since both a query and a target sequence may be noisy, i.e., contain… (more)

Subjects/Keywords: Computer Science

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APA (6th Edition):

Koknar-Tezel, S. (2011). OPTIMAL SUBSEQUENCE BIJECTION AND CLASSIFICATION OF IMBALANCED DATA SETS. (Doctoral Dissertation). Temple University. Retrieved from http://digital.library.temple.edu/u?/p245801coll10,107595

Chicago Manual of Style (16th Edition):

Koknar-Tezel, Suzan. “OPTIMAL SUBSEQUENCE BIJECTION AND CLASSIFICATION OF IMBALANCED DATA SETS.” 2011. Doctoral Dissertation, Temple University. Accessed October 20, 2020. http://digital.library.temple.edu/u?/p245801coll10,107595.

MLA Handbook (7th Edition):

Koknar-Tezel, Suzan. “OPTIMAL SUBSEQUENCE BIJECTION AND CLASSIFICATION OF IMBALANCED DATA SETS.” 2011. Web. 20 Oct 2020.

Vancouver:

Koknar-Tezel S. OPTIMAL SUBSEQUENCE BIJECTION AND CLASSIFICATION OF IMBALANCED DATA SETS. [Internet] [Doctoral dissertation]. Temple University; 2011. [cited 2020 Oct 20]. Available from: http://digital.library.temple.edu/u?/p245801coll10,107595.

Council of Science Editors:

Koknar-Tezel S. OPTIMAL SUBSEQUENCE BIJECTION AND CLASSIFICATION OF IMBALANCED DATA SETS. [Doctoral Dissertation]. Temple University; 2011. Available from: http://digital.library.temple.edu/u?/p245801coll10,107595


Temple University

14. Oyini Mbouna, Ralph. 3-D Face Modeling from a 2-D Image with Shape and Head Pose Estimation.

Degree: PhD, 2014, Temple University

Electrical Engineering

This paper presents 3-D face modeling with head pose and depth information estimated from a 2-D query face image. Many recent approaches to… (more)

Subjects/Keywords: Electrical engineering;

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

APA (6th Edition):

Oyini Mbouna, R. (2014). 3-D Face Modeling from a 2-D Image with Shape and Head Pose Estimation. (Doctoral Dissertation). Temple University. Retrieved from http://digital.library.temple.edu/u?/p245801coll10,305734

Chicago Manual of Style (16th Edition):

Oyini Mbouna, Ralph. “3-D Face Modeling from a 2-D Image with Shape and Head Pose Estimation.” 2014. Doctoral Dissertation, Temple University. Accessed October 20, 2020. http://digital.library.temple.edu/u?/p245801coll10,305734.

MLA Handbook (7th Edition):

Oyini Mbouna, Ralph. “3-D Face Modeling from a 2-D Image with Shape and Head Pose Estimation.” 2014. Web. 20 Oct 2020.

Vancouver:

Oyini Mbouna R. 3-D Face Modeling from a 2-D Image with Shape and Head Pose Estimation. [Internet] [Doctoral dissertation]. Temple University; 2014. [cited 2020 Oct 20]. Available from: http://digital.library.temple.edu/u?/p245801coll10,305734.

Council of Science Editors:

Oyini Mbouna R. 3-D Face Modeling from a 2-D Image with Shape and Head Pose Estimation. [Doctoral Dissertation]. Temple University; 2014. Available from: http://digital.library.temple.edu/u?/p245801coll10,305734

.