Artificial Intelligence - Special volume on computer vision
Robust epipolar geometry estimation using genetic algorithm
Pattern Recognition Letters
Computer Vision: A Modern Approach
Computer Vision: A Modern Approach
Advances in Computational Stereo
IEEE Transactions on Pattern Analysis and Machine Intelligence
Correspondence Matching with Modal Clusters
IEEE Transactions on Pattern Analysis and Machine Intelligence
An orthogonal genetic algorithm for multimedia multicast routing
IEEE Transactions on Evolutionary Computation
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
An orthogonal genetic algorithm with quantization for globalnumerical optimization
IEEE Transactions on Evolutionary Computation
Hybrid Taguchi-genetic algorithm for global numerical optimization
IEEE Transactions on Evolutionary Computation
A robust stochastic genetic algorithm (StGA) for global numerical optimization
IEEE Transactions on Evolutionary Computation
Intelligent evolutionary algorithms for large parameter optimization problems
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
Hybrid methods using genetic algorithms for global optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A multiagent genetic algorithm for global numerical optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Tuning the structure and parameters of a neural network by using hybrid Taguchi-genetic algorithm
IEEE Transactions on Neural Networks
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We propose a multiobjective genetic algorithm to compute the fundamental matrix, which are the foundation of multiview geometry and calibration in many 3D applications such as 3D reconstruction. The proposed method is a modification of the Intelligent Multiobjective Evolutionary Algorithm (IMOEA) [7] coupled with Taguchi's method [14]. Our design focuses are the fitness assignment of multiple objective functions, the diversity preservation, and the addition of an elite set. Moreover, we propose to include an additional random population besides the original initial population in genetic algorithms. In each generation we replace the random population and select only the non-dominated individuals into the elite set. The proposed method can explore more general solution space and can locate better solutions. We validate the proposed methods by demonstrating the effectiveness of the proposed methods to estimate of the fundamental matrices.