Monte Carlo Method with Heuristic Adjustment for Irregularly Shaped Food Product Volume Measurement

摘要

Volume measurement plays an important role in the production and processing of food products. Various methods have been\udproposed to measure the volume of food products with irregular shapes based on 3D reconstruction. However, 3D reconstruction\udcomes with a high-priced computational cost. Furthermore, some of the volume measurement methods based on 3D reconstruction\udhave a low accuracy. Another method for measuring volume of objects uses Monte Carlo method. Monte Carlo method performs\udvolume measurements using random points. Monte Carlo method only requires information regarding whether random points\udfall inside or outside an object and does not require a 3D reconstruction. This paper proposes volume measurement using a\udcomputer vision system for irregularly shaped food products without 3D reconstruction based on Monte Carlo method with\udheuristic adjustment. Five images of food product were captured using five cameras and processed to produce binary images.\udMonte Carlo integration with heuristic adjustment was performed to measure the volume based on the information extracted from\udbinary images. The experimental results show that the proposed method provided high accuracy and precision compared to the\udwater displacement method. In addition, the proposed method is more accurate and faster than the space carving method.