Copyright © 2008 The Institute of Electronics, Information and Communication Engineers
Regular Section -- Papers -- Image Recognition, Computer Vision |
Structural Object Recognition Using Entropy Correspondence Measure of Line Features
1 The author is with Korea Aerospace Research Institute, Daejon, 305–333 Korea. E-mail: kyoungmu{at}snu.ac.kr, 2 The author is with the Department of Electrical Engineering and Computer Science, Seoul National University, ASRI, Seoul, 151–742 Korea.
In this paper we propose an efficient line feature-based 2D object recognition algorithm using a novel entropy correspondence measure (ECM) that encodes the probabilistic similarity between two line feature sets. Since the proposed ECM-based method uses the whole structural information of objects simultaneously for matching, it overcomes the common drawbacks of the conventional techniques that are based on feature to feature correspondence. Moreover, since ECM is endowed with probabilistic attribute, it shows quite robust performance in the noisy environment. In order to enhance the recognition performance and speed, line features are pre-clustered into several groups according to their inclination by an eigen analysis, and then ECM is applied to each corresponding group individually. Experimental results on real images demonstrate that the proposed algorithm has superior performance to those of the conventional algorithms in both the accuracy and the computational efficiency, in the noisy environment.
Key Words: object recognition, entropy correspondence measure, line features, clustering
Manuscript received June 29, 2007. Manuscript revised August 22, 2007.
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