PARTIAL SHAPE CLASSIFICATION USING CONTOUR MATCHING IN DISTANCE TRANSFORMATION

In this paper, an algorithm is presented to recognize and locate partially distorted two-dimensional shapes without regard to their orientation, location, and size. The proposed algorithm first calculates the curvature function from the digitized image of an object. The points of local maxima and minima extracted from the smooth curvature function are used as control points to segment the boundary and to guide the boundary matching procedure. The boundary matching procedure considers two shapes at a time, one shape from the template data bank, and the other being the object under classification. The procedure tries to match the control points in the unknown shape to those of a shape from the template data bank, and estimates the translation, rotation, and scaling factors to be used to normalize the boundary of the unknown shape. The chamfer 3/4 distance transformation and a partial distance measurement scheme are used as the final step to measure the similarity between these two shapes. The unknown shape is assigned to the class corresponding to the minimum distance. The algorithm developed in this paper has been successfully tested on partial shapes using two sets of data, one with sharp corners, and the other with curve segments. This algorithm not only is computationally simple, but also works reasonably well in the presence of a moderate amount of noise. © 1990 IEEE