Object recognition and localization via pose clustering
Computer Vision, Graphics, and Image Processing
Efficient Pose Clustering Using a Randomized Algorithm
International Journal of Computer Vision
Similarity Retrieval of Trademark Images
IEEE MultiMedia
Geometric Hashing: An Overview
IEEE Computational Science & Engineering
Gestalt-based feature similarity measure in trademark database
Pattern Recognition
Multimedia Retrieval Algorithmics
SOFSEM '07 Proceedings of the 33rd conference on Current Trends in Theory and Practice of Computer Science
Shape Matching by Random Sampling
WALCOM '09 Proceedings of the 3rd International Workshop on Algorithms and Computation
Similarity evaluation based on image primitives
SPPRA '08 Proceedings of the Fifth IASTED International Conference on Signal Processing, Pattern Recognition and Applications
Shape matching by random sampling
Theoretical Computer Science
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We present a novel matching and similarity evaluation method for planar geometric shapes represented by sets of polygonal curves. Given two shapes, the matching algorithm randomly generates a point sample from each shape and records a vote for a transformation which maps one sample to the other. The experiment is repeated many times. Clusters of votes in the transformation space indicate good candidate transformations for matching the two shapes. Unlike most voting schemes, though, the samples taken in one random experiment are extended as much as possible and the vote is weighted depending on the samples. The best clusters are those with a large total weight. The second part of the method is a resemblance evaluation of the two matched shapes. The definition of our resemblance function incorporates the proximity of line segments as well as the similarity of their slopes. The system is evaluated using the MPEG-7 shape silhouette database and a collection of 10 745 trade mark images. The experiments demonstrate a high performance of our algorithms for contour shapes as well as for trademark images.