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Food volume estimation for quantifying dietary intake with a wearable camera

机译:通过可穿戴式摄像机估算食物量以量化饮食摄入量

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A novel food volume measurement technique is proposed in this paper for accurate quantification of the daily dietary intake of the user. The technique is based on simultaneous localisation and mapping (SLAM), a modified version of convex hull algorithm, and a 3D mesh object reconstruction technique. This paper explores the feasibility of applying SLAM techniques for continuous food volume measurement with a monocular wearable camera. A sparse map will be generated by SLAM after capturing the images of the food item with the camera and the multiple convex hull algorithm is applied to form a 3D mesh object. The volume of the target object can then be computed based on the mesh object. Compared to previous volume measurement techniques, the proposed method can measure the food volume continuously with no prior information such as pre-defined food shape model. Experiments have been carried out to evaluate this new technique and showed the feasibility and accuracy of the proposed algorithm in measuring food volume.
机译:本文提出了一种新颖的食物量测量技术,以准确量化用户的日常饮食摄入量。该技术基于同时定位和映射(SLAM),凸包算法的改进版本以及3D网格对象重建技术。本文探讨了将SLAM技术应用于单眼可穿戴式摄像机连续食物量测量的可行性。在使用相机捕获食物的图像后,SLAM将生成一个稀疏地图,并将多重凸包算法应用于形成3D网格对象。然后可以基于网格物体计算目标物体的体积。与先前的体积测量技术相比,所提出的方法可以在没有诸如预先定义的食物形状模型的先验信息的情况下连续地测量食物的体积。已经进行了实验以评估该新技术,并证明了该算法在测量食物量中的可行性和准确性。

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