{"title":"用于膳食评价的食品尺寸和体积的自动双目视觉测量","authors":"Zhi Liu, chao-qun xiang, Tefang Chen","doi":"10.1109/MCSE.2018.243113429","DOIUrl":null,"url":null,"abstract":"We propose a novel binocular vision technique to obtain food dimensions for dietary evaluation. A wearable device acquired images of food samples, which were segmented using the improved Chan-Vese model and compared with virtual objects. Results showed this method to be robust, achieving higher accuracy than conventional correspondence techniques.","PeriodicalId":100659,"journal":{"name":"IMPACT of Computing in Science and Engineering","volume":"2 1","pages":"1-1"},"PeriodicalIF":0.0000,"publicationDate":"2018-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"Automated Binocular Vision Measurement of Food Dimensions and Volume for Dietary Evaluation\",\"authors\":\"Zhi Liu, chao-qun xiang, Tefang Chen\",\"doi\":\"10.1109/MCSE.2018.243113429\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We propose a novel binocular vision technique to obtain food dimensions for dietary evaluation. A wearable device acquired images of food samples, which were segmented using the improved Chan-Vese model and compared with virtual objects. Results showed this method to be robust, achieving higher accuracy than conventional correspondence techniques.\",\"PeriodicalId\":100659,\"journal\":{\"name\":\"IMPACT of Computing in Science and Engineering\",\"volume\":\"2 1\",\"pages\":\"1-1\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-05-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IMPACT of Computing in Science and Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/MCSE.2018.243113429\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IMPACT of Computing in Science and Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MCSE.2018.243113429","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Automated Binocular Vision Measurement of Food Dimensions and Volume for Dietary Evaluation
We propose a novel binocular vision technique to obtain food dimensions for dietary evaluation. A wearable device acquired images of food samples, which were segmented using the improved Chan-Vese model and compared with virtual objects. Results showed this method to be robust, achieving higher accuracy than conventional correspondence techniques.
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