A statistical approach in enhancing the volume prediction of ellipsoidal ham

Abstract In literature, there exist many attempts to determine the surface area and volume of an irregular object using automated image processing techniques. This paper expanded previous work on predicting the volume of ellipsoidal hams by using both image processing techniques and numerical methods. Novel algorithms were proposed to improve the prediction accuracy and robustness of the volume estimation mechanism. Particularly, the work focused on the ham’s position in the horizontal viewpoint. An industrial robotic arm was utilized to lift the ham object and rotate it at a fixed controlled speed to maximize data consistency. Then, a Mask Region-based convolutional neural network approach was used to extract the ham object’s features. Experiments were conducted on 16 newly collected ham datasets. In this paper, performance comparisons between this and the previous work were reported and detailed analyses presented. Particularly, three numerical algorithms (i.e., based on the minor axis, Y-direction, and k-nearest neighbor) were introduced to enhance volume prediction in the two databases. The new algorithm exhibited a 27% higher performance than that of the previous work’s algorithm. Related theoretical and conceptual frameworks were discussed to further provide evidence and insights on the proposed mechanism.