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Journal of Information Science and Engineering, Vol. 24 No. 3, pp. 785-800 (May 2008)

Fundamental Matrix Estimation Using Evolutionary Algorithms with Multi-Objective Functions*

Cheng-Yuan Tang, Yi-Leh Wu+ and Yueh-Hung Lai
Department of Information Management
Huafan University
Taipei, 223 Taiwan
+Department of Computer Science and Information Engineering
National Taiwan University of Science and Technology
Taipei, 106 Taiwan

In this paper, we present the use of two evolutionary algorithms to estimate fundamental matrices. We first propose a modification of the Hybrid Taguchi Genetic Algorithm (HTGA) that employs a single objective function, either geometric or algebraic distance, for optimization. We then propose to use a multi-objective optimization algorithm, Intelligent Multi-Objective Evolutionary Algorithm (IMOEA), to optimize both geometric and algebraic distances concurrently. Our experiments show that the proposed modified HTGA (MHTGA) and IMOEA produce more accurate estimation of fundamental matrices than the traditional Genetic Algorithm (GA) and the original HTGA do.

Keywords: evolutionary computation, genetic algorithm, Taguchis method, fundamental matrix, multi-objective optimization

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Received June 12, 2006; revised September 15, 2006 & March 2, 2007; accepted April 10, 2007.
Communicated by Ming-Syan Chen.
*This work was partially supported by the National Science Council of Taiwan, R.O.C. under the grants No. NSC 94-2213-E-211-012-, NSC 95-3114-P-001-002-Y02, NSC 95-3114-P-001-001-Y02 (the iCAST project), and NSC 95-2221-E-011-059.