COMBINED CONTOUR DETECTION AND POINT CLOUD OF RGB-DEPTH IMAGE FOR FOOD VOLUME ESTIMATION

Assessing nutritional consumption entails a procedure that enables nutritionists and dietitians to track the eating habits of patients within healthcare settingsTraditionally, this measurement relies on manual observations by specialists utilizing visual analysis. However, this approach is prone to subjectivity due to the risk of expert fatigue, which can result in inaccuracies. Furthermore, the evaluations may differ among experts based on varying viewpoints. In a decision support system, a more objective analysis is necessary. Previous research has utilized the area captured in a food image to estimate the weight of food on a plate. Nonetheless, this technique still results in numerous prediction errors. To tackle this issue, we propose a novel method to calculate the volume of food from a camera image, which aims to provide a more accurate weight prediction. In this paper, we introduce a new approach that combines contour detection with a point cloud derived from RGB depth images to capture height information. The Root Mean Square Error (RMSE) for height prediction is 1.04 and 1.55 when viewed from the first and second sides, respectively, while the volume prediction reaches an RMSE of 45.08. This suggests that the differences between the predicted and actual values for volume and height are suitable for practical applications.