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Food Volume Estimation From A Single Image Using Virtual Reality Technology

Zhang, Zhengnan (2011) Food Volume Estimation From A Single Image Using Virtual Reality Technology. Master's Thesis, University of Pittsburgh.

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    Abstract

    Obesity has become a widespread epidemic threatening the health of millions of Americans and costing billions of dollars in health care. In both obesity research and clinical intervention, an accurate tool for diet evaluation is required. In this thesis, a new approach to the estimation of the volume of food from a single input image is presented based on the virtual reality (VR) technology. A VR system is contracted for food image acquisition, camera parameters calibration, virtual reality modeling and construction, virtual object manipulation, and food volume estimation. Our system utilizes a checkerboard to calibrate the intrinsic and extrinsic parameters of the camera using image process techniques. Once these parameters are obtained, we establish a VR space in which a virtual 3D wireframe is projected into the food image in a well-defined proportional relationship. Within this space, the user is able to scale, deform, translate and rotate the virtual wireframe to fit the food in the image. Finally, the known volume of the wireframe is utilized to compute the food volume using the proportional relationship. Our experimental study has indicated that our VR system is highly accurate and robust in estimating volumes of both regularly and irregularly shaped foods, providing a powerful diet evaluation tool for both obesity research and treatment.


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    Item Type: University of Pittsburgh ETD
    ETD Committee:
    ETD Committee TypeCommittee MemberEmail
    Committee ChairSun, Minguidrsun@pitt.edu
    Committee MemberLi, Ching-Chungccl@pitt.edu
    Committee MemberFernstrom, John Dfernstromjd@upmc.edu
    Committee MemberSclabassi, Robert Jbobs@cdi.com
    Committee MemberMao, Zhi-Hongmaozh@engr.pitt.edu
    Title: Food Volume Estimation From A Single Image Using Virtual Reality Technology
    Status: Unpublished
    Abstract: Obesity has become a widespread epidemic threatening the health of millions of Americans and costing billions of dollars in health care. In both obesity research and clinical intervention, an accurate tool for diet evaluation is required. In this thesis, a new approach to the estimation of the volume of food from a single input image is presented based on the virtual reality (VR) technology. A VR system is contracted for food image acquisition, camera parameters calibration, virtual reality modeling and construction, virtual object manipulation, and food volume estimation. Our system utilizes a checkerboard to calibrate the intrinsic and extrinsic parameters of the camera using image process techniques. Once these parameters are obtained, we establish a VR space in which a virtual 3D wireframe is projected into the food image in a well-defined proportional relationship. Within this space, the user is able to scale, deform, translate and rotate the virtual wireframe to fit the food in the image. Finally, the known volume of the wireframe is utilized to compute the food volume using the proportional relationship. Our experimental study has indicated that our VR system is highly accurate and robust in estimating volumes of both regularly and irregularly shaped foods, providing a powerful diet evaluation tool for both obesity research and treatment.
    Date: 26 January 2011
    Date Type: Completion
    Defense Date: 22 November 2010
    Approval Date: 26 January 2011
    Submission Date: 23 November 2010
    Access Restriction: No restriction; The work is available for access worldwide immediately.
    Patent pending: No
    Institution: University of Pittsburgh
    Thesis Type: Master's Thesis
    Refereed: Yes
    Degree: MSEE - Master of Science in Electrical Engineering
    URN: etd-11232010-155747
    Uncontrolled Keywords: obesity
    Schools and Programs: Swanson School of Engineering > Electrical Engineering
    Date Deposited: 10 Nov 2011 15:06
    Last Modified: 14 May 2012 14:12
    Other ID: http://etd.library.pitt.edu/ETD/available/etd-11232010-155747/, etd-11232010-155747

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