Image-Based Volume Estimation for Food in a Bowl.

Image-assisted dietary assessment has become popular in dietary monitoring studies in recent years. However, food volume estimation is still a challenging problem due to the lack of 3D information in a 2D image and the occlusion of the food by itself or container (e.g., bowl, cup). This study aims to investigate the relationship between the observable surface of food in a bowl and a normalized index (i.e., bowl fullness) to represent its volume. A mathematical model is established for describing different shapes of bowls, and a convenient experimental method is proposed to determine the bowl shape. An image feature called Food Area Ratio (FAR) is used to estimate the volume of food in a bowl based on the relationship between bowl fullness and the FAR calculated from the image. Both simulations and experiments with real food/liquid demonstrate the feasibility and accuracy of the proposed approach.