Authors:
Trung H. Duong
and
Larry Hoberock
Affiliation:
Oklahoma State University, United States
Keyword(s):
Dishware identification, Dishware inspection, Partitioning, Adaptive thresholding, Global thresholding.
Related
Ontology
Subjects/Areas/Topics:
Informatics in Control, Automation and Robotics
;
Robotics and Automation
;
Vision, Recognition and Reconstruction
Abstract:
We propose automatically identifying dishes in mixed batches by using statistics of shape descriptors of dish pieces. Experiments were conducted on 725 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. For dish inspection, we propose a new technique using partitioning and adaptive thresholding, combined with global thresholding. For practical purposes, the algorithm should be fast, simple, and produce results invariant with lighting conditions and dish rotation about the camera-dish axis. Such an algorithm is described in this work. Matlab® R14 and Image Processing Toolbo
x V5.0 were used.
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