| dc.description.abstract | Commercial dishwashing systems currently involve human loading, sorting, inspecting, and unloading dishes and silverware pieces before and after washing, in hot and humid environments. Automation is desirable, especially in large scale kitchens, to improve safety and efficiency. We propose automatically identifying dishes in mixed batches by using statistics of shape descriptors of dish pieces. Experiments were conducted on 1225 images of ceramic and plastic dishes taken in different lighting conditions using different positions of 84 separate dishes of 5 different styles and shapes. In order to find the minimum set of descriptors to produce fast, adaptable and efficient automatic dish recognition, we employed several shape-based properties, including area, perimeter, ratio of length to width, extension, and minimum bounding box, together with some properties based on gray level and color of dish images. Selected set of descriptors were area, ratio of length to width, and ratio of area to area of the oriented bounding box of dish images. For dish inspection, we propose a new technique using partitioning and adaptive thresholding, combined with global thresholding. Matlab ® R14 and Image Processing Toolbox V5.0 were used. The machine vision algorithms, developed in this study, are fast, simple, and produce results invariant with lighting conditions and dish rotation about the camera-dish axis. | |
