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Psychophysical researches on the human visual system have shown that the points of high curvature on the contour of an object play an important role in the recognition process. Inspired by these studies we propose: (i) a novel algorithm to select points of high curvature on the contour of an object which can be used to construct a recognizable polygonal approximation, (ii) a test which evaluates the effect of deletion of contour segments containing such points on the performance of contour based object recognition algorithms. 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 consider two types of contour incompleteness obtained by deletion of contour segments of high or low curvature. We illustrate the test procedure using two shape recognition algorithms that deploy a shape context and a distance multiset as local shape descriptors. Both algorithms qualitatively mimic human visual perception in that the deletion of segments of high curvature has a stronger performance degradation effect than the deletion of other parts of the contour. This effect is more pronounced in the performance of the shape context method.

Most parameter-based online signature verification methods achieve correspondence between the points of two signatures by minimizing the accumulation of their local feature distances. The matching based on minimizing the local feature distances alone is not adequate since the point contains not only local features but the distribution of the remaining points relative to it. One useful way to get correspondences between points on two shapes and measure the similarity of the two shapes is to use the shape context, since this descriptor can be used to describe the distributive relationship between a reference point and the remaining points on a shape. In this paper, we introduce a shape context descriptor for describing an online signature point which contains both 2D spatial information and a time stamp. A common algorithm, dynamic time warp (DTW), is used for the elastic matching between two signatures. When combining shape contexts and local features, we achieve better results than when using only the local features. We evaluate the proposed method on a signature database from the First International Signature Verification Competition (SVC2004). Experimental results demonstrate that the shape context is a good feature and has available complementarity for describing the signature point. The best result by combining the shape contexts and the local features yields an Equal Error Rate (EER) of 6.77% for five references.