Recognition of multiple configurations of objects with limited data

Authors:
Yuexing Han;Bing Wang;Masanori Idesawa;Hiroyuki Shimai
Affiliations:
The University of Electro-Communications, 1-5-1, Chofugaoka, Chofu-Shi, Tokyo 182-8585, Japan;Institute of Industrial Science, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8505, Japan;The University of Electro-Communications, 1-5-1, Chofugaoka, Chofu-Shi, Tokyo 182-8585, Japan;The University of Electro-Communications, 1-5-1, Chofugaoka, Chofu-Shi, Tokyo 182-8585, Japan
Venue:
Pattern Recognition
Year:
2010

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Abstract

One of the main goals of image understanding and computer vision applications is to recognize an object from various images. A lot of studies on recognizing objects based on invariable shapes have been explored, however, in reality, there are many objects with multiple configurations, which are very difficult to be recognized. We call this kind of problem as the recognition of multiple configurations of objects (RMCO). To achieve RMCO, firstly we obtain a shortest path (the Geodesic distance path) between two feature vectors in pre-shape spaces; along this obtained path, we can generate a series of data which can be used to recognize the observed objects by using shape space theories. In other words, we may augment the database content with very limited data to recognize more objects.