Model-based food volume estimation using 3D pose

We are developing a dietary assessment system to automatically identify and quantify foods and beverages consumed by analyzing meal images captured with a mobile device. After food items are segmented and identified, accurately estimating the volume of the food in the image is important for determining the nutrient content of the food. In this paper, we proposed a novel food portion size estimation method for rigid food items using a single image. First, we create a 3D graphical model during the training step using 3D reconstruction from multiple views. Then, for each food image, we determine the translation and elevation parameters of each of the food items, which are relative to the camera coordinate through camera calibration. Using these geometric parameters we project the pre-built 3D model of each food item back to the image plane. Subsequently, the remaining degrees-of-freedom (DOF) for the final pose is estimated by image similarity measure. The experimental results of our volume estimation method for four food categories validate the accuracy and reliability of our model-based approach.