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You searched for +publisher:"University of North Carolina" +contributor:("Bregler, Chris"). One record found.

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University of North Carolina

1. 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 deformation of each patient. Studies have shown that the tissue stiffness described by the tissue properties may indicate abnormal pathological process. The (recovered) elasticity parameters can assist surgeons to perform better pre-op surgical planning and enable medical robots to carry out personalized surgical procedures. Traditional elasticity parameters estimation methods rely largely on known external forces measured by special devices and strain field estimated by landmarks on the deformable bodies. Or they are limited to mechanical property estimation for quasi-static deformation. For virtual reality applications such as virtual try-on, garment material capturing is of equal significance as the geometry reconstruction. In this thesis, I present novel approaches for automatically estimating the material properties of soft bodies from images or from a video capturing the motion of the deformable body. I use a coupled simulation-optimization-identification framework to deform one soft body at its original, non-deformed state to match the deformed geometry of the same object in its deformed state. The optimal set of material parameters is thereby determined by minimizing the error metric function. This method can simultaneously recover the elasticity parameters of multiple regions of soft bodies using Finite Element Method-based simulation (of either linear or nonlinear materials undergoing large deformation) and particle-swarm optimization methods. I demonstrate the effectiveness of this approach on real-time interaction with virtual organs in patient-specific surgical simulation, using parameters acquired from low-resolution medical images. With the recovered elasticity parameters and the age of the prostate cancer patients as features, I build a cancer grading and staging classifier. The classifier achieves up to 91% for predicting cancer T-Stage and 88% for predicting Gleason score. To recover the mechanical properties of soft bodies from a video, I propose a method which couples statistical graphical model with FEM simulation. Using this method, I can recover the material properties of a soft ball from a high-speed camera video that captures the motion of the ball. Furthermore, I extend the material recovery framework to fabric material identification. I propose a novel method for garment material extraction from a single-view image and a learning based cloth material recovery method from a video recording the motion of the cloth. Most recent garment capturing techniques rely on acquiring multiple views of clothing, which may not always be readily available, especially in the case of pre-existing photographs from the web. As an alternative, I propose a method that can compute a 3D model of a human body and its outfit from a single photograph with little human interaction. My proposed learning-based cloth material type… Advisors/Committee Members: YANG, SHAN, Lin, Ming, Berg, Tamara, Bregler, Chris, Manocha, Dinesh, Jojic, Vladimir, University of North Carolina at Chapel Hill.

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 January 16, 2021. 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. 16 Jan 2021.

Vancouver:

YANG S. NON-RIGID BODY MECHANICAL PROPERTY RECOVERY FROM IMAGES AND VIDEOS. [Internet] [Thesis]. University of North Carolina; 2018. [cited 2021 Jan 16]. 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

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