Abstract

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.

Keywords

SilhouetteMatching (statistics)Artificial intelligencePattern recognition (psychology)Similarity (geometry)Computer scienceRepresentation (politics)Transformation (genetics)SimilitudeMeasure (data warehouse)Image (mathematics)MathematicsComputer visionData mining

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Publication Info

Year
1999
Type
article
Volume
21
Issue
12
Pages
1312-1328
Citations
236
Access
Closed

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Cite This

Yoram Gdalyahu, Daphna Weinshall (1999). Flexible syntactic matching of curves and its application to automatic hierarchical classification of silhouettes. IEEE Transactions on Pattern Analysis and Machine Intelligence , 21 (12) , 1312-1328. https://doi.org/10.1109/34.817410

Identifiers

DOI
10.1109/34.817410