Hierarchical Chamfer Matching: A Parametric Edge Matching Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Pattern Recognition Letters - Special issue on genetic algorithms
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
A genetic algorithm for affine invariant recognition of object shapes from broken boundaries
Pattern Recognition Letters
Partial shape matching using genetic algorithms
Pattern Recognition Letters
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Geometric Primitive Extraction Using a Genetic Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition Letters
Genetic algorithm for affine point pattern matching
Pattern Recognition Letters
The particle swarm optimization algorithm: convergence analysis and parameter selection
Information Processing Letters
Coarse-to-Fine Multiscale Affine Invariant Shape Matching and Classification
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
Applying Genetic Algorithms on Pattern Recognition: An Analysis and Survey
ICPR '96 Proceedings of the 13th International Conference on Pattern Recognition - Volume 2
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
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Affine invariant matching of broken image contours with model shapes is an important but difficult research topic in computer vision. One of the effective approaches to date encapsulates the process as an optimization problem which determines, with the use of a Simple Genetic Algorithm (SGA), the best matching score between pairs of object boundaries. Despite the moderate success of methods developed in this direction, the overall success rate is generally low and inconsistent amongst test trials. This unfavorable outcome could be due to the lack of adequate exploitation in an enormous and erratic search space, which is rather common in the context of shape matching. In this paper, a novel scheme based on Particle Swarm Optimization (PSO) is presented to overcome these problems. Experimental results reveal that the proposed method has outperformed SGA and Real Coded Genetic Algorithm (RCGA) in terms of speed, stability and success rate. In addition, the evolutionary behavior of PSO also permits the use of repeated trials to further enhance the success rate towards perfection with relatively fewer iterations.