Curve matching is one instance of the fundamental correspondence problem. Our flexible algorithm is designed to match curves under substantial deformations and arbitrary large scaling and rigid transformations. A syntactic representation is constructed for both curves and an edit transformation which maps one curve to the other is found using dynamic programming. We present extensive experiments where we apply the algorithm to silhouette matching. In these experiments, we examine partial occlusion, viewpoint variation, articulation, and class matching (where silhouettes of similar objects are matched). Based on the qualitative syntactic matching, we define a dissimilarity measure and we compute it for every pair of images in a database of 121 images. We use this experiment to objectively evaluate our algorithm: First, we compare our results to those reported by others. Second, we use the dissimilarity values in order to organize the image database into shape categories. The veridical hierarchical organization stands as evidence to the quality of our matching and similarity estimation.
|Number of pages
|IEEE Transactions on Pattern Analysis and Machine Intelligence
|Published - 1 Jan 1999
Bibliographical noteFunding Information:
The authors would like to thank Ben Kimia and Daniel Sharvit for the 31 silhouette database and Davi Geiger for the human limbs data. This research is partially funded by the Israeli Ministry of Science.