An introduction to genetic algorithms
An introduction to genetic algorithms
Constraint, optimization, and hierarchy: reviewing stereoscopic correspondence of complex features
Computer Vision and Image Understanding
Determining local natural scales of curves
Pattern Recognition Letters
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Proceedings of the 3rd International Conference on Genetic Algorithms
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Using the registration of remote imagery as an example domain, this work describes an efficient approach to the structural matching of multi-resolution representations where the scale Difference, rotation and translation are unknown. The matching process is posed within an optimisation framework in which the parameter space is the probability hyperspace of all possible matches. In this application, searching for corresponding features at all scales generates a parameter space of enormous dimensions - typically 1-10 million. In this work we use feature's hierarchical relationships to decompose the parameter space into a series of smaller subspaces over which optimisation is computationally feasible.