Zhang, Zhengnan
(2011)
Food Volume Estimation From A Single Image Using Virtual Reality Technology.
Master's Thesis, University of Pittsburgh.
(Unpublished)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.
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Details
| Item Type: |
University of Pittsburgh ETD
|
| Status: |
Unpublished |
| Creators/Authors: |
|
| ETD Committee: |
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| 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; Release the ETD for access worldwide immediately. |
| Institution: |
University of Pittsburgh |
| Schools and Programs: |
Swanson School of Engineering > Electrical Engineering |
| Degree: |
MSEE - Master of Science in Electrical Engineering |
| Thesis Type: |
Master's Thesis |
| Refereed: |
Yes |
| Uncontrolled Keywords: |
obesity |
| Other ID: |
http://etd.library.pitt.edu/ETD/available/etd-11232010-155747/, etd-11232010-155747 |
| Date Deposited: |
10 Nov 2011 20:06 |
| Last Modified: |
19 Dec 2016 14:37 |
| URI: |
http://d-scholarship.pitt.edu/id/eprint/9780 |
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