The Community for Technology Leaders
Green Image
<p>An algorithm is presented to recognize and locate partially distorted 2D shapes without regard to their orientation, location, and size. The 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 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 databank, and the other from the object being classified. The procedure tries to match the control points in the unknown shape to those of a shape from the template databank, 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 constitute the final step in measuring the similarity between the two shapes. The unknown shape is assigned to the class corresponding to the minimum distance. The algorithm 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.</p>
partial shape classification; local minima; boundary segmentation; translation estimation; rotation estimation; scaling factor estimation; boundary normalisation; contour matching; distance transformation; 2D shapes; curvature function; digitized image; local maxima; boundary-matching; chamfer 3/4 distance transformation; partial distance measurement; sharp corners; curve segments; noise; pattern recognition; picture processing

H. Liu and M. Srinath, "Partial Shape Classification Using Contour Matching in Distance Transformation," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 12, no. , pp. 1072-1079, 1990.
88 ms
(Ver 3.3 (11022016))