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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Puri, M. Zhiwei Zhu Qian Yu Divakaran, A. Sawhney, H. |
| Copyright Year | 2009 |
| Description | Author affiliation: Sarnoff Corporation, 201 Washington Rd, Princeton, NJ, 08540, USA (Puri, M.; Zhiwei Zhu; Qian Yu; Divakaran, A.; Sawhney, H.) |
| Abstract | We present a system that improves accuracy of food intake assessment using computer vision techniques. Traditional dietetic method suffers from the drawback of either inaccurate assessment or complex lab measurement. Our solution is to use a mobile phone to capture images of foods, recognize food types, estimate their respective volumes and finally return quantitative nutrition information. Automated and accurate food recognition presents the following challenges. First, there exist a large variety of food types that people consume in everyday life. Second, a single category of food may contain large variations due to different ways of preparation. Also, diverse lighting conditions may lead to varying visual appearance of foods. All of these pose a challenge to the state of the art recognition approaches. Moreover, the low quality images captured using cellphones make the task of 3D reconstruction difficult. In this paper, we combine several vision techniques (visual recognition and 3D reconstruction) to achieve quantitative food intake estimation. Evaluation of both recognition and reconstruction is provided in the experimental results. |
| Starting Page | 1 |
| Ending Page | 8 |
| File Size | 447678 |
| Page Count | 8 |
| File Format | |
| ISBN | 9781424454976 |
| ISSN | 15505790 |
| e-ISBN | 9781424454983 |
| DOI | 10.1109/WACV.2009.5403087 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2009-12-07 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Cellular phones Image reconstruction Cameras Speech recognition Image databases Image segmentation Mobile computing Computer vision Mobile handsets Image recognition |
| Content Type | Text |
| Resource Type | Article |
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