Table 4:

Methods for food volume and calorie estimation.

Study (Year)	Approach	Performance	Reference
Noronha et al., 2011	Via crowdsourcing (e.g. users from Amazon Mechanical Turk)	Better performance than other commercial app using crowdsourcing but overall it is error prone since users estimate food portion by just looking at the picture	[42]
Chen, 2012	Use depth camera to acquire color and depth	Preliminary result showing some limitations when estimating quantity of cooked rice and water	[22]
Villalobos et al., 2012	Use Top+side view pictures with user’s finger as reference	Results change due to illumination conditions and image angle; standard error is in an acceptable range	[44]
Beijbom et al., 2015	Use menu items from nearby restaurants	Food calorie is from pre-defined restaurant’s menu	[20]
Meyers, 2015	3D volume estimation by capturing images with a depth camera and reconstructing image using Convolutional Neural Network and RANSAC	Using toy food; the CNN volume predictor is accurate for most of the meals; no calorie estimation outside a controlled environment.	[11]
Woo et al., 2010	Use a checkerboard as reference for camera calibration and 3D reconstruction	Mean volume error of 5.68% on a test of sever food items	[43]