Incomplete Contour Representations and Shape Descriptors: ICR Test Studies

Inspired by psychophysical studies of the human cognitive abilities we propose a novel aspect and a method for performance evaluation of contour based shape recognition algorithms regarding their robustness to incompleteness of contours. We use complete contour representations of objects as a reference (training) set. Incomplete contour representations of the same objects are used as a test set. The performance of an algorithm is reported using the recognition rate as a function of the percentage of contour retained. We call this evaluation procedure the ICR test. We consider three types of contour incompleteness, viz. segment-wise contour deletion, occlusion and random pixel depletion. We illustrate the test procedure using two shape recognition algorithms. These algorithms use a shape context and a distance multiset as local shape descriptors. Both algorithms qualitatively mimic human visual perception in the sense that the recognition performance monotonously increases with the degree of completeness and that they perform best in the case of random depletion and worst in the case of occluded contours. The distance multiset method performs better than the shape context method in this evaluation framework.