# A New Approach to Image-Based Estimation of Food Volume
## 1 Introduction  
People's eating behavior has been shown to affect health issues [1]. This fact has fostered the emergence of many approaches to monitoring diet whose targets include the food items comprised in each meal, as well as the general eating habits of a person. However, most currently used self-reporting techniques generally cause an underestimation of food intake, related with diverse psychological implications [2]. While such an attitude is rather obvious in people overweight, under-reporting of dietary energy intake has been shown to be common even in non-obese adults [3]. Another relevant problem is the different degree of motivation with which individuals fill the reports. Differences relate to factors like awareness of food intake (particularly with snacks), literacy level, and memory and perception capabilities [4].  
Thus, self-reported food intake data should be interpreted with caution unless their validity is assessed by independent methods [3]. Because of this, many researchers have started developing affordable non-invasive automatic solutions that help users measure and analyze their food intake [5].  
Estimating the size or volume of food items, once the food type is known, is the most direct way of estimating the nutrition properties of the food pictured in an image.  
Several methods try to estimate food size from a single image. [PERSON] et al. assume that a correlation exists between the area of the region where food is visible in an image and its actual volume [6]. To achieve the same goal, the system described in [7] first detects the camera pose using a fiducial marker placed within the scene and then tries to match the food item by generating several pre-defined 3D shape models of which the best-matching one is adopted. However, this approach works only for the items that can be approximated by the regular shapes it takes into consideration.  
Many studies have tried to use more than one image to overcome the problems related with the lack of depth information in two-dimensional images and with occlusions that may hide some objects. In [8], two images provide the top and side views of the dish. In [9], the user takes three pictures from which the system computes a dense 3D point-cloud including all points on the dish and then estimates the volume using Delaunay triangulation. [PERSON] et al. [10] also suggest a computationally affordable method for volume estimation based on a dense 3D point-cloud.  
In our previous work, we developed two of the basic modules needed to accomplish food volume estimation. In [11], we proposed an interactive segmentation solution for smartphones, which provided a robust and highly accurate approach to segmentation in spite of its simplicity and practicality. In [12], we designed a quick and accurate stochastic model-based solution for locating small checkerboards to be used as size references within an image.  
In this work, we introduce a complete solution to estimate food item volume based on a short video. It integrates our previously-developed methods with a new approach to image acquisition and image-based modeling. This end-to-end solution is designed to be usable in practical food intake monitoring solutions. We first describe the method, which is composed of four main steps: acquisition, segmentation, size/ground reference detection, and modeling. Then, we discuss the evaluation procedure and the results we obtained on a test set that includes images of different food items.