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You searched for +publisher:"Rutgers University" +contributor:("Metaxas, Dimitris"). Showing records 1 – 30 of 51 total matches.

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Rutgers University

1. Kumar, Neelesh, 1993-. Vision based cognitive fatigue detection.

Degree: MS, Computer Science, 2017, Rutgers University

 Analyzing human activity is a basic component of any system, be it biological or artificial, that aims to predict future behavior. Tracking and recognizing voluntary… (more)

Subjects/Keywords: Computer vision

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APA (6th Edition):

Kumar, Neelesh, 1. (2017). Vision based cognitive fatigue detection. (Masters Thesis). Rutgers University. Retrieved from https://rucore.libraries.rutgers.edu/rutgers-lib/53648/

Chicago Manual of Style (16th Edition):

Kumar, Neelesh, 1993-. “Vision based cognitive fatigue detection.” 2017. Masters Thesis, Rutgers University. Accessed September 29, 2020. https://rucore.libraries.rutgers.edu/rutgers-lib/53648/.

MLA Handbook (7th Edition):

Kumar, Neelesh, 1993-. “Vision based cognitive fatigue detection.” 2017. Web. 29 Sep 2020.

Vancouver:

Kumar, Neelesh 1. Vision based cognitive fatigue detection. [Internet] [Masters thesis]. Rutgers University; 2017. [cited 2020 Sep 29]. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/53648/.

Council of Science Editors:

Kumar, Neelesh 1. Vision based cognitive fatigue detection. [Masters Thesis]. Rutgers University; 2017. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/53648/


Rutgers University

2. Tang, Guangzhi. Gridbot: towards a neuroinspired navigation system for robot planning.

Degree: MS, Computer Science, 2017, Rutgers University

 The ability to orient in an unknown, fast-changing, environment is an unmet challenge for robots but a seamlessly solved problem for the primate brain. This… (more)

Subjects/Keywords: Intelligent control systems; Robots; Space perception

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APA (6th Edition):

Tang, G. (2017). Gridbot: towards a neuroinspired navigation system for robot planning. (Masters Thesis). Rutgers University. Retrieved from https://rucore.libraries.rutgers.edu/rutgers-lib/54029/

Chicago Manual of Style (16th Edition):

Tang, Guangzhi. “Gridbot: towards a neuroinspired navigation system for robot planning.” 2017. Masters Thesis, Rutgers University. Accessed September 29, 2020. https://rucore.libraries.rutgers.edu/rutgers-lib/54029/.

MLA Handbook (7th Edition):

Tang, Guangzhi. “Gridbot: towards a neuroinspired navigation system for robot planning.” 2017. Web. 29 Sep 2020.

Vancouver:

Tang G. Gridbot: towards a neuroinspired navigation system for robot planning. [Internet] [Masters thesis]. Rutgers University; 2017. [cited 2020 Sep 29]. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/54029/.

Council of Science Editors:

Tang G. Gridbot: towards a neuroinspired navigation system for robot planning. [Masters Thesis]. Rutgers University; 2017. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/54029/


Rutgers University

3. Sanik, Kevin, 1985-. The relative effectiveness of line drawing algorithms at depicting 3D shape.

Degree: PhD, Computer Science, 2015, Rutgers University

Line drawings offer a way to depict 3D shape using a ``minimal'' representation. They can abstract away other features like shading, texture, color, and others… (more)

Subjects/Keywords: Computer graphics; Shapes

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APA (6th Edition):

Sanik, Kevin, 1. (2015). The relative effectiveness of line drawing algorithms at depicting 3D shape. (Doctoral Dissertation). Rutgers University. Retrieved from https://rucore.libraries.rutgers.edu/rutgers-lib/47579/

Chicago Manual of Style (16th Edition):

Sanik, Kevin, 1985-. “The relative effectiveness of line drawing algorithms at depicting 3D shape.” 2015. Doctoral Dissertation, Rutgers University. Accessed September 29, 2020. https://rucore.libraries.rutgers.edu/rutgers-lib/47579/.

MLA Handbook (7th Edition):

Sanik, Kevin, 1985-. “The relative effectiveness of line drawing algorithms at depicting 3D shape.” 2015. Web. 29 Sep 2020.

Vancouver:

Sanik, Kevin 1. The relative effectiveness of line drawing algorithms at depicting 3D shape. [Internet] [Doctoral dissertation]. Rutgers University; 2015. [cited 2020 Sep 29]. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/47579/.

Council of Science Editors:

Sanik, Kevin 1. The relative effectiveness of line drawing algorithms at depicting 3D shape. [Doctoral Dissertation]. Rutgers University; 2015. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/47579/


Rutgers University

4. Zhong, Lin, 1985-. Single image deblurring with or without face prior and its applications.

Degree: PhD, Computer Science, 2015, Rutgers University

The motion blur is one of the most difficult challenges in photography, which is generated from the relative motion between the sensor and the scene… (more)

Subjects/Keywords: Image processing – Digital techniques; Human face recognition (Computer science)

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APA (6th Edition):

Zhong, Lin, 1. (2015). Single image deblurring with or without face prior and its applications. (Doctoral Dissertation). Rutgers University. Retrieved from https://rucore.libraries.rutgers.edu/rutgers-lib/47622/

Chicago Manual of Style (16th Edition):

Zhong, Lin, 1985-. “Single image deblurring with or without face prior and its applications.” 2015. Doctoral Dissertation, Rutgers University. Accessed September 29, 2020. https://rucore.libraries.rutgers.edu/rutgers-lib/47622/.

MLA Handbook (7th Edition):

Zhong, Lin, 1985-. “Single image deblurring with or without face prior and its applications.” 2015. Web. 29 Sep 2020.

Vancouver:

Zhong, Lin 1. Single image deblurring with or without face prior and its applications. [Internet] [Doctoral dissertation]. Rutgers University; 2015. [cited 2020 Sep 29]. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/47622/.

Council of Science Editors:

Zhong, Lin 1. Single image deblurring with or without face prior and its applications. [Doctoral Dissertation]. Rutgers University; 2015. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/47622/


Rutgers University

5. Yu, Xiang, 1986-. Unconstrained face landmark localization: algorithms and applications.

Degree: PhD, Computer Science, 2015, Rutgers University

Nowadays, facial landmark localization in unconstrained environments has attracted increasing attention in computer vision, which is a fundamental step in face recognition, expression recognition, face… (more)

Subjects/Keywords: Computer vision; Face perception

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APA (6th Edition):

Yu, Xiang, 1. (2015). Unconstrained face landmark localization: algorithms and applications. (Doctoral Dissertation). Rutgers University. Retrieved from https://rucore.libraries.rutgers.edu/rutgers-lib/48737/

Chicago Manual of Style (16th Edition):

Yu, Xiang, 1986-. “Unconstrained face landmark localization: algorithms and applications.” 2015. Doctoral Dissertation, Rutgers University. Accessed September 29, 2020. https://rucore.libraries.rutgers.edu/rutgers-lib/48737/.

MLA Handbook (7th Edition):

Yu, Xiang, 1986-. “Unconstrained face landmark localization: algorithms and applications.” 2015. Web. 29 Sep 2020.

Vancouver:

Yu, Xiang 1. Unconstrained face landmark localization: algorithms and applications. [Internet] [Doctoral dissertation]. Rutgers University; 2015. [cited 2020 Sep 29]. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/48737/.

Council of Science Editors:

Yu, Xiang 1. Unconstrained face landmark localization: algorithms and applications. [Doctoral Dissertation]. Rutgers University; 2015. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/48737/


Rutgers University

6. Yu, Yang. Sparsity-based methods for cardiac magnetic resonance image reconstruction and analysis.

Degree: PhD, Computer Science, 2015, Rutgers University

In signal processing, sparseness means that there are only small amounts of non-zero elements. This property has been widely observed in various types of signals.… (more)

Subjects/Keywords: Magnetic resonance imaging; Heart – Imaging

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APA (6th Edition):

Yu, Y. (2015). Sparsity-based methods for cardiac magnetic resonance image reconstruction and analysis. (Doctoral Dissertation). Rutgers University. Retrieved from https://rucore.libraries.rutgers.edu/rutgers-lib/48738/

Chicago Manual of Style (16th Edition):

Yu, Yang. “Sparsity-based methods for cardiac magnetic resonance image reconstruction and analysis.” 2015. Doctoral Dissertation, Rutgers University. Accessed September 29, 2020. https://rucore.libraries.rutgers.edu/rutgers-lib/48738/.

MLA Handbook (7th Edition):

Yu, Yang. “Sparsity-based methods for cardiac magnetic resonance image reconstruction and analysis.” 2015. Web. 29 Sep 2020.

Vancouver:

Yu Y. Sparsity-based methods for cardiac magnetic resonance image reconstruction and analysis. [Internet] [Doctoral dissertation]. Rutgers University; 2015. [cited 2020 Sep 29]. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/48738/.

Council of Science Editors:

Yu Y. Sparsity-based methods for cardiac magnetic resonance image reconstruction and analysis. [Doctoral Dissertation]. Rutgers University; 2015. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/48738/


Rutgers University

7. Dilsizian, Mark. Hybrid discriminative-generative methods for human pose reconstruction from monocular imagery.

Degree: PhD, Computer Science, 2016, Rutgers University

Estimating 3D human pose from monocular images is an important and challenging problem in computer vision with numerous applications including human-computer interaction, human activity recognition,… (more)

Subjects/Keywords: Computer vision

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APA (6th Edition):

Dilsizian, M. (2016). Hybrid discriminative-generative methods for human pose reconstruction from monocular imagery. (Doctoral Dissertation). Rutgers University. Retrieved from https://rucore.libraries.rutgers.edu/rutgers-lib/49953/

Chicago Manual of Style (16th Edition):

Dilsizian, Mark. “Hybrid discriminative-generative methods for human pose reconstruction from monocular imagery.” 2016. Doctoral Dissertation, Rutgers University. Accessed September 29, 2020. https://rucore.libraries.rutgers.edu/rutgers-lib/49953/.

MLA Handbook (7th Edition):

Dilsizian, Mark. “Hybrid discriminative-generative methods for human pose reconstruction from monocular imagery.” 2016. Web. 29 Sep 2020.

Vancouver:

Dilsizian M. Hybrid discriminative-generative methods for human pose reconstruction from monocular imagery. [Internet] [Doctoral dissertation]. Rutgers University; 2016. [cited 2020 Sep 29]. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/49953/.

Council of Science Editors:

Dilsizian M. Hybrid discriminative-generative methods for human pose reconstruction from monocular imagery. [Doctoral Dissertation]. Rutgers University; 2016. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/49953/


Rutgers University

8. Gao, Mingchen, 1988-. Cardiac reconstruction and analysis from high resolution CT images.

Degree: PhD, Computer Science, 2014, Rutgers University

Heart disease is a major cause of mortality worldwide. Detecting/diagnosing such diseases in their early stages is critical, and heavily depends on non-invasive imaging methods,… (more)

Subjects/Keywords: Heart – Tomography; Heart – Imaging; Heart – Diseases – Diagnosis

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APA (6th Edition):

Gao, Mingchen, 1. (2014). Cardiac reconstruction and analysis from high resolution CT images. (Doctoral Dissertation). Rutgers University. Retrieved from https://rucore.libraries.rutgers.edu/rutgers-lib/45272/

Chicago Manual of Style (16th Edition):

Gao, Mingchen, 1988-. “Cardiac reconstruction and analysis from high resolution CT images.” 2014. Doctoral Dissertation, Rutgers University. Accessed September 29, 2020. https://rucore.libraries.rutgers.edu/rutgers-lib/45272/.

MLA Handbook (7th Edition):

Gao, Mingchen, 1988-. “Cardiac reconstruction and analysis from high resolution CT images.” 2014. Web. 29 Sep 2020.

Vancouver:

Gao, Mingchen 1. Cardiac reconstruction and analysis from high resolution CT images. [Internet] [Doctoral dissertation]. Rutgers University; 2014. [cited 2020 Sep 29]. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/45272/.

Council of Science Editors:

Gao, Mingchen 1. Cardiac reconstruction and analysis from high resolution CT images. [Doctoral Dissertation]. Rutgers University; 2014. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/45272/


Rutgers University

9. Yan, Zhennan, 1983-. Robust medical image recognition and segmentation.

Degree: PhD, Computer Science, 2016, Rutgers University

In recent decades, with increasing amount of medical data, clinical trials are designed and conducted to explore whether a medical strategy, treatment, or device is… (more)

Subjects/Keywords: Diagnostic imaging; Computer vision

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APA (6th Edition):

Yan, Zhennan, 1. (2016). Robust medical image recognition and segmentation. (Doctoral Dissertation). Rutgers University. Retrieved from https://rucore.libraries.rutgers.edu/rutgers-lib/51515/

Chicago Manual of Style (16th Edition):

Yan, Zhennan, 1983-. “Robust medical image recognition and segmentation.” 2016. Doctoral Dissertation, Rutgers University. Accessed September 29, 2020. https://rucore.libraries.rutgers.edu/rutgers-lib/51515/.

MLA Handbook (7th Edition):

Yan, Zhennan, 1983-. “Robust medical image recognition and segmentation.” 2016. Web. 29 Sep 2020.

Vancouver:

Yan, Zhennan 1. Robust medical image recognition and segmentation. [Internet] [Doctoral dissertation]. Rutgers University; 2016. [cited 2020 Sep 29]. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/51515/.

Council of Science Editors:

Yan, Zhennan 1. Robust medical image recognition and segmentation. [Doctoral Dissertation]. Rutgers University; 2016. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/51515/


Rutgers University

10. Yoon, Sejong, 1980-. Generalized distributed learning under uncertainty for camera networks.

Degree: PhD, Computer Science, 2016, Rutgers University

Consensus-based distributed learning is a machine learning technique used to find the general consensus of local learning models to achieve a global objective. It is… (more)

Subjects/Keywords: Machine learning

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APA (6th Edition):

Yoon, Sejong, 1. (2016). Generalized distributed learning under uncertainty for camera networks. (Doctoral Dissertation). Rutgers University. Retrieved from https://rucore.libraries.rutgers.edu/rutgers-lib/51518/

Chicago Manual of Style (16th Edition):

Yoon, Sejong, 1980-. “Generalized distributed learning under uncertainty for camera networks.” 2016. Doctoral Dissertation, Rutgers University. Accessed September 29, 2020. https://rucore.libraries.rutgers.edu/rutgers-lib/51518/.

MLA Handbook (7th Edition):

Yoon, Sejong, 1980-. “Generalized distributed learning under uncertainty for camera networks.” 2016. Web. 29 Sep 2020.

Vancouver:

Yoon, Sejong 1. Generalized distributed learning under uncertainty for camera networks. [Internet] [Doctoral dissertation]. Rutgers University; 2016. [cited 2020 Sep 29]. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/51518/.

Council of Science Editors:

Yoon, Sejong 1. Generalized distributed learning under uncertainty for camera networks. [Doctoral Dissertation]. Rutgers University; 2016. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/51518/


Rutgers University

11. Kulp, Scott Andrew, 1986-. Ventricular blood flow simulation and analysis for cardiovascular diagnostics.

Degree: PhD, Computer Science, 2015, Rutgers University

The heart has long been seen as a symbol of life, due to its critical function of pumping blood throughout the body. However, despite its… (more)

Subjects/Keywords: Blood flow; Heart – Physiology; Heart – Computer simulation

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APA (6th Edition):

Kulp, Scott Andrew, 1. (2015). Ventricular blood flow simulation and analysis for cardiovascular diagnostics. (Doctoral Dissertation). Rutgers University. Retrieved from https://rucore.libraries.rutgers.edu/rutgers-lib/46376/

Chicago Manual of Style (16th Edition):

Kulp, Scott Andrew, 1986-. “Ventricular blood flow simulation and analysis for cardiovascular diagnostics.” 2015. Doctoral Dissertation, Rutgers University. Accessed September 29, 2020. https://rucore.libraries.rutgers.edu/rutgers-lib/46376/.

MLA Handbook (7th Edition):

Kulp, Scott Andrew, 1986-. “Ventricular blood flow simulation and analysis for cardiovascular diagnostics.” 2015. Web. 29 Sep 2020.

Vancouver:

Kulp, Scott Andrew 1. Ventricular blood flow simulation and analysis for cardiovascular diagnostics. [Internet] [Doctoral dissertation]. Rutgers University; 2015. [cited 2020 Sep 29]. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/46376/.

Council of Science Editors:

Kulp, Scott Andrew 1. Ventricular blood flow simulation and analysis for cardiovascular diagnostics. [Doctoral Dissertation]. Rutgers University; 2015. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/46376/


Rutgers University

12. Uzunbas, Mustafa Gokhan, 1983-. Automatic and interactive segmentations using deformable and graphical models.

Degree: PhD, Computer Science, 2015, Rutgers University

Image segmentation i.e. dividing an image into regions and categories is a classic yet still challenging problem. The key to success is to use/develop the… (more)

Subjects/Keywords: Image segmentation; Image analysis; Electron microscopy

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APA (6th Edition):

Uzunbas, Mustafa Gokhan, 1. (2015). Automatic and interactive segmentations using deformable and graphical models. (Doctoral Dissertation). Rutgers University. Retrieved from https://rucore.libraries.rutgers.edu/rutgers-lib/46449/

Chicago Manual of Style (16th Edition):

Uzunbas, Mustafa Gokhan, 1983-. “Automatic and interactive segmentations using deformable and graphical models.” 2015. Doctoral Dissertation, Rutgers University. Accessed September 29, 2020. https://rucore.libraries.rutgers.edu/rutgers-lib/46449/.

MLA Handbook (7th Edition):

Uzunbas, Mustafa Gokhan, 1983-. “Automatic and interactive segmentations using deformable and graphical models.” 2015. Web. 29 Sep 2020.

Vancouver:

Uzunbas, Mustafa Gokhan 1. Automatic and interactive segmentations using deformable and graphical models. [Internet] [Doctoral dissertation]. Rutgers University; 2015. [cited 2020 Sep 29]. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/46449/.

Council of Science Editors:

Uzunbas, Mustafa Gokhan 1. Automatic and interactive segmentations using deformable and graphical models. [Doctoral Dissertation]. Rutgers University; 2015. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/46449/


Rutgers University

13. Banga, Karansingh, 1991-. Development of an adaptive serious game for assessing cognitive engagement.

Degree: MS, Computer Science, 2019, Rutgers University

 Cognitive Engagement is defined as "The act of beginning and carrying on of activity with a sense of emotional involvement or commitment and the deliberate… (more)

Subjects/Keywords: Nervous system  – Diseases  – Patients  – Rehabilitation  – Technological innovations; Games  – Therapeutic use

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APA (6th Edition):

Banga, Karansingh, 1. (2019). Development of an adaptive serious game for assessing cognitive engagement. (Masters Thesis). Rutgers University. Retrieved from https://rucore.libraries.rutgers.edu/rutgers-lib/60588/

Chicago Manual of Style (16th Edition):

Banga, Karansingh, 1991-. “Development of an adaptive serious game for assessing cognitive engagement.” 2019. Masters Thesis, Rutgers University. Accessed September 29, 2020. https://rucore.libraries.rutgers.edu/rutgers-lib/60588/.

MLA Handbook (7th Edition):

Banga, Karansingh, 1991-. “Development of an adaptive serious game for assessing cognitive engagement.” 2019. Web. 29 Sep 2020.

Vancouver:

Banga, Karansingh 1. Development of an adaptive serious game for assessing cognitive engagement. [Internet] [Masters thesis]. Rutgers University; 2019. [cited 2020 Sep 29]. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/60588/.

Council of Science Editors:

Banga, Karansingh 1. Development of an adaptive serious game for assessing cognitive engagement. [Masters Thesis]. Rutgers University; 2019. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/60588/


Rutgers University

14. Shah, Arpit, 1996-. Development of an astrocytic module for spiking neural networks on neuromorphic hardware.

Degree: MS, Spiking neural networks, 2019, Rutgers University

 Astrocytes have long been neglected in application to neuronal networks due to being electrically silent. While these glial cells have been hypothesized to serve as… (more)

Subjects/Keywords: Computer Science; Neural networks (Computer science); Astrocytes

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APA (6th Edition):

Shah, Arpit, 1. (2019). Development of an astrocytic module for spiking neural networks on neuromorphic hardware. (Masters Thesis). Rutgers University. Retrieved from https://rucore.libraries.rutgers.edu/rutgers-lib/61054/

Chicago Manual of Style (16th Edition):

Shah, Arpit, 1996-. “Development of an astrocytic module for spiking neural networks on neuromorphic hardware.” 2019. Masters Thesis, Rutgers University. Accessed September 29, 2020. https://rucore.libraries.rutgers.edu/rutgers-lib/61054/.

MLA Handbook (7th Edition):

Shah, Arpit, 1996-. “Development of an astrocytic module for spiking neural networks on neuromorphic hardware.” 2019. Web. 29 Sep 2020.

Vancouver:

Shah, Arpit 1. Development of an astrocytic module for spiking neural networks on neuromorphic hardware. [Internet] [Masters thesis]. Rutgers University; 2019. [cited 2020 Sep 29]. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/61054/.

Council of Science Editors:

Shah, Arpit 1. Development of an astrocytic module for spiking neural networks on neuromorphic hardware. [Masters Thesis]. Rutgers University; 2019. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/61054/


Rutgers University

15. Gindra, Rushin Hitesh, 1996-. Improvements in cardiac segmentation for cross-modality domain adaptation.

Degree: MS, Unsupervised domain adaptation, 2020, Rutgers University

 In medical image computing, the problem of heterogeneous domain shift is quite common and severe, causing many deep convolutional networks to under-perform on various imaging… (more)

Subjects/Keywords: Diagnostic imaging; Computer Science

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APA (6th Edition):

Gindra, Rushin Hitesh, 1. (2020). Improvements in cardiac segmentation for cross-modality domain adaptation. (Masters Thesis). Rutgers University. Retrieved from https://rucore.libraries.rutgers.edu/rutgers-lib/64100/

Chicago Manual of Style (16th Edition):

Gindra, Rushin Hitesh, 1996-. “Improvements in cardiac segmentation for cross-modality domain adaptation.” 2020. Masters Thesis, Rutgers University. Accessed September 29, 2020. https://rucore.libraries.rutgers.edu/rutgers-lib/64100/.

MLA Handbook (7th Edition):

Gindra, Rushin Hitesh, 1996-. “Improvements in cardiac segmentation for cross-modality domain adaptation.” 2020. Web. 29 Sep 2020.

Vancouver:

Gindra, Rushin Hitesh 1. Improvements in cardiac segmentation for cross-modality domain adaptation. [Internet] [Masters thesis]. Rutgers University; 2020. [cited 2020 Sep 29]. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/64100/.

Council of Science Editors:

Gindra, Rushin Hitesh 1. Improvements in cardiac segmentation for cross-modality domain adaptation. [Masters Thesis]. Rutgers University; 2020. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/64100/


Rutgers University

16. Senlet, Turgay, 1981-. Visual localization, semantic video segmentation and labeling using satellite maps.

Degree: PhD, Computer Science, 2015, Rutgers University

In this dissertation, I propose vision-based geo-localization and segmentation methods that make use of semantic and appearance information from satellite images. First, I present a… (more)

Subjects/Keywords: Image segmentation; Visual perception

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APA (6th Edition):

Senlet, Turgay, 1. (2015). Visual localization, semantic video segmentation and labeling using satellite maps. (Doctoral Dissertation). Rutgers University. Retrieved from https://rucore.libraries.rutgers.edu/rutgers-lib/47583/

Chicago Manual of Style (16th Edition):

Senlet, Turgay, 1981-. “Visual localization, semantic video segmentation and labeling using satellite maps.” 2015. Doctoral Dissertation, Rutgers University. Accessed September 29, 2020. https://rucore.libraries.rutgers.edu/rutgers-lib/47583/.

MLA Handbook (7th Edition):

Senlet, Turgay, 1981-. “Visual localization, semantic video segmentation and labeling using satellite maps.” 2015. Web. 29 Sep 2020.

Vancouver:

Senlet, Turgay 1. Visual localization, semantic video segmentation and labeling using satellite maps. [Internet] [Doctoral dissertation]. Rutgers University; 2015. [cited 2020 Sep 29]. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/47583/.

Council of Science Editors:

Senlet, Turgay 1. Visual localization, semantic video segmentation and labeling using satellite maps. [Doctoral Dissertation]. Rutgers University; 2015. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/47583/


Rutgers University

17. Kanaujia, Atul, 1979-. Conditional models for 3D human pose estimation:.

Degree: PhD, Computer Science, 2010, Rutgers University

Human 3d pose estimation from monocular sequence is a challenging problem, owing to highly articulated structure of human body, varied anthropometry, self occlusion, depth ambiguities… (more)

Subjects/Keywords: Image processing; Three-dimensional imaging

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APA (6th Edition):

Kanaujia, Atul, 1. (2010). Conditional models for 3D human pose estimation:. (Doctoral Dissertation). Rutgers University. Retrieved from http://hdl.rutgers.edu/1782.2/rucore10001600001.ETD.000052120

Chicago Manual of Style (16th Edition):

Kanaujia, Atul, 1979-. “Conditional models for 3D human pose estimation:.” 2010. Doctoral Dissertation, Rutgers University. Accessed September 29, 2020. http://hdl.rutgers.edu/1782.2/rucore10001600001.ETD.000052120.

MLA Handbook (7th Edition):

Kanaujia, Atul, 1979-. “Conditional models for 3D human pose estimation:.” 2010. Web. 29 Sep 2020.

Vancouver:

Kanaujia, Atul 1. Conditional models for 3D human pose estimation:. [Internet] [Doctoral dissertation]. Rutgers University; 2010. [cited 2020 Sep 29]. Available from: http://hdl.rutgers.edu/1782.2/rucore10001600001.ETD.000052120.

Council of Science Editors:

Kanaujia, Atul 1. Conditional models for 3D human pose estimation:. [Doctoral Dissertation]. Rutgers University; 2010. Available from: http://hdl.rutgers.edu/1782.2/rucore10001600001.ETD.000052120


Rutgers University

18. Huang, Yuchi, 1979-. Hypergraph based visual categorization and segmentation.

Degree: PhD, Computer Science, 2010, Rutgers University

This dissertation explores original techniques for the construction of hypergraph models for computer vision applications. A hypergraph is a generalization of a pairwise simple graph,… (more)

Subjects/Keywords: Hypergraphs; Computer vision – Mathematical models

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

APA (6th Edition):

Huang, Yuchi, 1. (2010). Hypergraph based visual categorization and segmentation. (Doctoral Dissertation). Rutgers University. Retrieved from http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000056373

Chicago Manual of Style (16th Edition):

Huang, Yuchi, 1979-. “Hypergraph based visual categorization and segmentation.” 2010. Doctoral Dissertation, Rutgers University. Accessed September 29, 2020. http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000056373.

MLA Handbook (7th Edition):

Huang, Yuchi, 1979-. “Hypergraph based visual categorization and segmentation.” 2010. Web. 29 Sep 2020.

Vancouver:

Huang, Yuchi 1. Hypergraph based visual categorization and segmentation. [Internet] [Doctoral dissertation]. Rutgers University; 2010. [cited 2020 Sep 29]. Available from: http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000056373.

Council of Science Editors:

Huang, Yuchi 1. Hypergraph based visual categorization and segmentation. [Doctoral Dissertation]. Rutgers University; 2010. Available from: http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000056373


Rutgers University

19. Li, Zhiguo, 1977-. Video-based facial expression analysis.

Degree: PhD, Computer Science, 2010, Rutgers University

Recognizing facial expressions from facial video sequences is an important and unsolved problem. Among many factors that contribute to the challenges of this task are:… (more)

Subjects/Keywords: Facial expression – Testing; Face perception – Testing

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APA (6th Edition):

Li, Zhiguo, 1. (2010). Video-based facial expression analysis. (Doctoral Dissertation). Rutgers University. Retrieved from http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000056509

Chicago Manual of Style (16th Edition):

Li, Zhiguo, 1977-. “Video-based facial expression analysis.” 2010. Doctoral Dissertation, Rutgers University. Accessed September 29, 2020. http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000056509.

MLA Handbook (7th Edition):

Li, Zhiguo, 1977-. “Video-based facial expression analysis.” 2010. Web. 29 Sep 2020.

Vancouver:

Li, Zhiguo 1. Video-based facial expression analysis. [Internet] [Doctoral dissertation]. Rutgers University; 2010. [cited 2020 Sep 29]. Available from: http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000056509.

Council of Science Editors:

Li, Zhiguo 1. Video-based facial expression analysis. [Doctoral Dissertation]. Rutgers University; 2010. Available from: http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000056509


Rutgers University

20. Parag, Toufiq U, 1979-. Labeling hypergraph-structured data using Markov network.

Degree: PhD, Computer Science, 2011, Rutgers University

The goal of this dissertation is to label datapoints into two groups utilizing higher order information among them. More specifically, given likelihood (or error) measures… (more)

Subjects/Keywords: Markov processes; Computer science – Mathematics

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APA (6th Edition):

Parag, Toufiq U, 1. (2011). Labeling hypergraph-structured data using Markov network. (Doctoral Dissertation). Rutgers University. Retrieved from http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000057655

Chicago Manual of Style (16th Edition):

Parag, Toufiq U, 1979-. “Labeling hypergraph-structured data using Markov network.” 2011. Doctoral Dissertation, Rutgers University. Accessed September 29, 2020. http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000057655.

MLA Handbook (7th Edition):

Parag, Toufiq U, 1979-. “Labeling hypergraph-structured data using Markov network.” 2011. Web. 29 Sep 2020.

Vancouver:

Parag, Toufiq U 1. Labeling hypergraph-structured data using Markov network. [Internet] [Doctoral dissertation]. Rutgers University; 2011. [cited 2020 Sep 29]. Available from: http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000057655.

Council of Science Editors:

Parag, Toufiq U 1. Labeling hypergraph-structured data using Markov network. [Doctoral Dissertation]. Rutgers University; 2011. Available from: http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000057655


Rutgers University

21. Wang, Xiaoxu, 1977-. Meshless deformable models for LV motion and strain computation from tagged MRI.

Degree: PhD, Computer Science, 2011, Rutgers University

Tagged MRI(TMRI) provides a direct and noninvasive way to reveal the in-wall deformation of the myocardium. Due to the through-plane motion, the 3D trajectories and… (more)

Subjects/Keywords: Myocardium—Diseases; Heart—Magnetic resonance imaging

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

APA (6th Edition):

Wang, Xiaoxu, 1. (2011). Meshless deformable models for LV motion and strain computation from tagged MRI. (Doctoral Dissertation). Rutgers University. Retrieved from http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000061538

Chicago Manual of Style (16th Edition):

Wang, Xiaoxu, 1977-. “Meshless deformable models for LV motion and strain computation from tagged MRI.” 2011. Doctoral Dissertation, Rutgers University. Accessed September 29, 2020. http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000061538.

MLA Handbook (7th Edition):

Wang, Xiaoxu, 1977-. “Meshless deformable models for LV motion and strain computation from tagged MRI.” 2011. Web. 29 Sep 2020.

Vancouver:

Wang, Xiaoxu 1. Meshless deformable models for LV motion and strain computation from tagged MRI. [Internet] [Doctoral dissertation]. Rutgers University; 2011. [cited 2020 Sep 29]. Available from: http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000061538.

Council of Science Editors:

Wang, Xiaoxu 1. Meshless deformable models for LV motion and strain computation from tagged MRI. [Doctoral Dissertation]. Rutgers University; 2011. Available from: http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000061538


Rutgers University

22. Yang, Peng. Facial expression recognition and expression intensity estimation.

Degree: PhD, Computer Science, 2011, Rutgers University

Seventy years ago, psychologist categorized the facial expression into seven categories: angry, disgust, fear, happiness, sadness, surprise and neutral. Through analyzing the expression, psychologists want… (more)

Subjects/Keywords: Facial expression; Facial expression—Testing; Computer vision

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

APA (6th Edition):

Yang, P. (2011). Facial expression recognition and expression intensity estimation. (Doctoral Dissertation). Rutgers University. Retrieved from http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000061545

Chicago Manual of Style (16th Edition):

Yang, Peng. “Facial expression recognition and expression intensity estimation.” 2011. Doctoral Dissertation, Rutgers University. Accessed September 29, 2020. http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000061545.

MLA Handbook (7th Edition):

Yang, Peng. “Facial expression recognition and expression intensity estimation.” 2011. Web. 29 Sep 2020.

Vancouver:

Yang P. Facial expression recognition and expression intensity estimation. [Internet] [Doctoral dissertation]. Rutgers University; 2011. [cited 2020 Sep 29]. Available from: http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000061545.

Council of Science Editors:

Yang P. Facial expression recognition and expression intensity estimation. [Doctoral Dissertation]. Rutgers University; 2011. Available from: http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000061545


Rutgers University

23. Liu, Jingjing, 1985-. Exploiting multispectral and contextual information to improve human detection.

Degree: PhD, Computer Science, 2017, Rutgers University

Human detection has various applications, e.g., autonomous driving car, surveillance system, and retail. In this dissertation, we first exploit multispectral images (i.e., RGB and thermal… (more)

Subjects/Keywords: Robotics – Human factors; Human-robot interaction

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APA (6th Edition):

Liu, Jingjing, 1. (2017). Exploiting multispectral and contextual information to improve human detection. (Doctoral Dissertation). Rutgers University. Retrieved from https://rucore.libraries.rutgers.edu/rutgers-lib/55564/

Chicago Manual of Style (16th Edition):

Liu, Jingjing, 1985-. “Exploiting multispectral and contextual information to improve human detection.” 2017. Doctoral Dissertation, Rutgers University. Accessed September 29, 2020. https://rucore.libraries.rutgers.edu/rutgers-lib/55564/.

MLA Handbook (7th Edition):

Liu, Jingjing, 1985-. “Exploiting multispectral and contextual information to improve human detection.” 2017. Web. 29 Sep 2020.

Vancouver:

Liu, Jingjing 1. Exploiting multispectral and contextual information to improve human detection. [Internet] [Doctoral dissertation]. Rutgers University; 2017. [cited 2020 Sep 29]. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/55564/.

Council of Science Editors:

Liu, Jingjing 1. Exploiting multispectral and contextual information to improve human detection. [Doctoral Dissertation]. Rutgers University; 2017. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/55564/


Rutgers University

24. Zhu, Yan, 1986-. Towards active and interactive visual learning.

Degree: PhD, Computer Science, 2017, Rutgers University

Modern computer vision models mostly rely on massive human annotated datasets for supervised training. The models are typically learned from the supervision of static datasets… (more)

Subjects/Keywords: Computer vision

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APA (6th Edition):

Zhu, Yan, 1. (2017). Towards active and interactive visual learning. (Doctoral Dissertation). Rutgers University. Retrieved from https://rucore.libraries.rutgers.edu/rutgers-lib/55812/

Chicago Manual of Style (16th Edition):

Zhu, Yan, 1986-. “Towards active and interactive visual learning.” 2017. Doctoral Dissertation, Rutgers University. Accessed September 29, 2020. https://rucore.libraries.rutgers.edu/rutgers-lib/55812/.

MLA Handbook (7th Edition):

Zhu, Yan, 1986-. “Towards active and interactive visual learning.” 2017. Web. 29 Sep 2020.

Vancouver:

Zhu, Yan 1. Towards active and interactive visual learning. [Internet] [Doctoral dissertation]. Rutgers University; 2017. [cited 2020 Sep 29]. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/55812/.

Council of Science Editors:

Zhu, Yan 1. Towards active and interactive visual learning. [Doctoral Dissertation]. Rutgers University; 2017. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/55812/


Rutgers University

25. Peng, Xi, 1986-. Learning disentangled representations in deep visual analysis.

Degree: PhD, Computer Science, 2018, Rutgers University

Learning reliable and interpretable representations is one of the fundamental challenges in machine learning and computer vision. Over the last decade, deep neural networks have… (more)

Subjects/Keywords: Machine learning

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APA (6th Edition):

Peng, Xi, 1. (2018). Learning disentangled representations in deep visual analysis. (Doctoral Dissertation). Rutgers University. Retrieved from https://rucore.libraries.rutgers.edu/rutgers-lib/56078/

Chicago Manual of Style (16th Edition):

Peng, Xi, 1986-. “Learning disentangled representations in deep visual analysis.” 2018. Doctoral Dissertation, Rutgers University. Accessed September 29, 2020. https://rucore.libraries.rutgers.edu/rutgers-lib/56078/.

MLA Handbook (7th Edition):

Peng, Xi, 1986-. “Learning disentangled representations in deep visual analysis.” 2018. Web. 29 Sep 2020.

Vancouver:

Peng, Xi 1. Learning disentangled representations in deep visual analysis. [Internet] [Doctoral dissertation]. Rutgers University; 2018. [cited 2020 Sep 29]. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/56078/.

Council of Science Editors:

Peng, Xi 1. Learning disentangled representations in deep visual analysis. [Doctoral Dissertation]. Rutgers University; 2018. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/56078/


Rutgers University

26. Liu, Bo, 1984-. Optimization in sparse learning: from convexity to non-convexity.

Degree: PhD, Computer Science, 2019, Rutgers University

 Nowadays, the explosive data scale increase provides an unprecedented opportunity to apply machine learning methods in various application domains. The high-dimension data representation proposes curse… (more)

Subjects/Keywords: Machine learning

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

APA (6th Edition):

Liu, Bo, 1. (2019). Optimization in sparse learning: from convexity to non-convexity. (Doctoral Dissertation). Rutgers University. Retrieved from https://rucore.libraries.rutgers.edu/rutgers-lib/60032/

Chicago Manual of Style (16th Edition):

Liu, Bo, 1984-. “Optimization in sparse learning: from convexity to non-convexity.” 2019. Doctoral Dissertation, Rutgers University. Accessed September 29, 2020. https://rucore.libraries.rutgers.edu/rutgers-lib/60032/.

MLA Handbook (7th Edition):

Liu, Bo, 1984-. “Optimization in sparse learning: from convexity to non-convexity.” 2019. Web. 29 Sep 2020.

Vancouver:

Liu, Bo 1. Optimization in sparse learning: from convexity to non-convexity. [Internet] [Doctoral dissertation]. Rutgers University; 2019. [cited 2020 Sep 29]. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/60032/.

Council of Science Editors:

Liu, Bo 1. Optimization in sparse learning: from convexity to non-convexity. [Doctoral Dissertation]. Rutgers University; 2019. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/60032/


Rutgers University

27. Pham, Hai, 1985-. Learning human facial performance: analysis and synthesis.

Degree: PhD, Computer Science, 2019, Rutgers University

Human faces convey a large range of semantic meaning through facial expressions, which reflect both actions and affective states. More importantly, in the coming age… (more)

Subjects/Keywords: Human face recognition (Computer science)

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APA (6th Edition):

Pham, Hai, 1. (2019). Learning human facial performance: analysis and synthesis. (Doctoral Dissertation). Rutgers University. Retrieved from https://rucore.libraries.rutgers.edu/rutgers-lib/60045/

Chicago Manual of Style (16th Edition):

Pham, Hai, 1985-. “Learning human facial performance: analysis and synthesis.” 2019. Doctoral Dissertation, Rutgers University. Accessed September 29, 2020. https://rucore.libraries.rutgers.edu/rutgers-lib/60045/.

MLA Handbook (7th Edition):

Pham, Hai, 1985-. “Learning human facial performance: analysis and synthesis.” 2019. Web. 29 Sep 2020.

Vancouver:

Pham, Hai 1. Learning human facial performance: analysis and synthesis. [Internet] [Doctoral dissertation]. Rutgers University; 2019. [cited 2020 Sep 29]. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/60045/.

Council of Science Editors:

Pham, Hai 1. Learning human facial performance: analysis and synthesis. [Doctoral Dissertation]. Rutgers University; 2019. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/60045/


Rutgers University

28. Yang, Dong, 1987-. Deformable models and machine learning for large-scale cardiac MRI image analytics.

Degree: PhD, Computer Science, 2019, Rutgers University

The analysis of left ventricle (LV) wall motion is an important step for understanding cardiac functioning mechanisms, and clinical diagnosis of ventricular diseases. For example,… (more)

Subjects/Keywords: Heart  – Magnetic resonance imaging; Machine learning

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

APA (6th Edition):

Yang, Dong, 1. (2019). Deformable models and machine learning for large-scale cardiac MRI image analytics. (Doctoral Dissertation). Rutgers University. Retrieved from https://rucore.libraries.rutgers.edu/rutgers-lib/61044/

Chicago Manual of Style (16th Edition):

Yang, Dong, 1987-. “Deformable models and machine learning for large-scale cardiac MRI image analytics.” 2019. Doctoral Dissertation, Rutgers University. Accessed September 29, 2020. https://rucore.libraries.rutgers.edu/rutgers-lib/61044/.

MLA Handbook (7th Edition):

Yang, Dong, 1987-. “Deformable models and machine learning for large-scale cardiac MRI image analytics.” 2019. Web. 29 Sep 2020.

Vancouver:

Yang, Dong 1. Deformable models and machine learning for large-scale cardiac MRI image analytics. [Internet] [Doctoral dissertation]. Rutgers University; 2019. [cited 2020 Sep 29]. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/61044/.

Council of Science Editors:

Yang, Dong 1. Deformable models and machine learning for large-scale cardiac MRI image analytics. [Doctoral Dissertation]. Rutgers University; 2019. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/61044/


Rutgers University

29. Tan, Chaowei, 1983-. Machine learning based image segmentation for large-scale osteoarthritis analysis.

Degree: PhD, Computer Science, 2020, Rutgers University

Osteoarthritis (OA) is the most common degenerative joint disease worldwide, tending to occur in the joints of hip and knee. Large adult population in the… (more)

Subjects/Keywords: Osteoarthritis analysis; Osteoarthritis  – Imaging

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APA (6th Edition):

Tan, Chaowei, 1. (2020). Machine learning based image segmentation for large-scale osteoarthritis analysis. (Doctoral Dissertation). Rutgers University. Retrieved from https://rucore.libraries.rutgers.edu/rutgers-lib/62751/

Chicago Manual of Style (16th Edition):

Tan, Chaowei, 1983-. “Machine learning based image segmentation for large-scale osteoarthritis analysis.” 2020. Doctoral Dissertation, Rutgers University. Accessed September 29, 2020. https://rucore.libraries.rutgers.edu/rutgers-lib/62751/.

MLA Handbook (7th Edition):

Tan, Chaowei, 1983-. “Machine learning based image segmentation for large-scale osteoarthritis analysis.” 2020. Web. 29 Sep 2020.

Vancouver:

Tan, Chaowei 1. Machine learning based image segmentation for large-scale osteoarthritis analysis. [Internet] [Doctoral dissertation]. Rutgers University; 2020. [cited 2020 Sep 29]. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/62751/.

Council of Science Editors:

Tan, Chaowei 1. Machine learning based image segmentation for large-scale osteoarthritis analysis. [Doctoral Dissertation]. Rutgers University; 2020. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/62751/


Rutgers University

30. Kalampratsidou, Vilelmini. Co-adaptive multimodal interface guided by real-time multisensory stochastic feedback.

Degree: PhD, Computer Science, 2018, Rutgers University

 In this work, we present new data-types, analytics, and human-computer interfaces as a platform to enable a new type of co-adaptive-behavioural analyses to track neuroplasticity.… (more)

Subjects/Keywords: Afferent pathways; Human-computer interaction

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APA (6th Edition):

Kalampratsidou, V. (2018). Co-adaptive multimodal interface guided by real-time multisensory stochastic feedback. (Doctoral Dissertation). Rutgers University. Retrieved from https://rucore.libraries.rutgers.edu/rutgers-lib/57627/

Chicago Manual of Style (16th Edition):

Kalampratsidou, Vilelmini. “Co-adaptive multimodal interface guided by real-time multisensory stochastic feedback.” 2018. Doctoral Dissertation, Rutgers University. Accessed September 29, 2020. https://rucore.libraries.rutgers.edu/rutgers-lib/57627/.

MLA Handbook (7th Edition):

Kalampratsidou, Vilelmini. “Co-adaptive multimodal interface guided by real-time multisensory stochastic feedback.” 2018. Web. 29 Sep 2020.

Vancouver:

Kalampratsidou V. Co-adaptive multimodal interface guided by real-time multisensory stochastic feedback. [Internet] [Doctoral dissertation]. Rutgers University; 2018. [cited 2020 Sep 29]. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/57627/.

Council of Science Editors:

Kalampratsidou V. Co-adaptive multimodal interface guided by real-time multisensory stochastic feedback. [Doctoral Dissertation]. Rutgers University; 2018. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/57627/

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