在有约束和无约束的环境中分析和识别食物

M. Buzzelli, G. Ciocca, Paolo Napoletano, R. Schettini
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引用次数: 0

摘要

近年来,基于计算机视觉的图像分析技术被用于开发自动饮食监测应用,引起了人们的广泛关注。食物识别是一项相当具有挑战性的任务:它是一个非刚性对象,其特点是内在的高等级和等级内变异性。一个基于计算机视觉的食品识别系统的合理设计应该包含几个分析阶段。本文报道了过去12年来,影像与视觉实验室在利用计算机视觉进行食品自动识别领域的最新解决方案。我们介绍并讨论了在食品定位、分割、识别和分析方面开发的主要解决方案和取得的成果。食品定位和分割的目的是识别图像中与食品对应的区域,食品识别的目的是用所描绘的食品的身份标记每个食品区域,食品分析的目的是确定食品的数量或成分等属性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Analyzing and Recognizing Food in Constrained and Unconstrained Environments
Recently, Computer Vision based image analysis techniques have attracted a lot of attention because they are used to develop automatic dietary monitoring applications. Food recognition is a quite challenging task: it is a non-rigid object, and is characterized by intrinsic high iter- and intra-class variability. The proper design of a food recognition system based on Computer Vision should contain several analysis stages. This paper reports on the most recent solutions in the field of automatic food recognition using computer vision developed at the Imaging and Vision Laboratory in the last 12 years. We present and discuss the main solutions developed and results achieved for food localization, segmentation, recognition and analysis. Food localization and segmentation aim at identifying the regions in the image corresponding to food items, food recognition aims at labeling each food region with the identity of the depicted food, and food analysis aims at determining properties of the food such as its quantity or ingredients.
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