A Method for Registration of 3-D Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
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Image and Vision Computing - Special issue: BMVC 1991
A survey of image registration techniques
ACM Computing Surveys (CSUR)
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International Journal of Computer Vision
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Genetic algorithms + data structures = evolution programs (3rd ed.)
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Handbook of medical imaging
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Efficient Global Optimization for Image Registration
IEEE Transactions on Knowledge and Data Engineering
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ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 1
2-D and 3-D Image Registration: for Medical, Remote Sensing, and Industrial Applications
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Journal of Heuristics
A CHC evolutionary algorithm for 3d image registration
IFSA'03 Proceedings of the 10th international fuzzy systems association World Congress conference on Fuzzy sets and systems
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Proceedings of the 10th annual conference on Genetic and evolutionary computation
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Applied Soft Computing
Information Sciences: an International Journal
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Evolutionary Computation
ICIC'09 Proceedings of the Intelligent computing 5th international conference on Emerging intelligent computing technology and applications
Integrated Computer-Aided Engineering
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Expert Systems with Applications: An International Journal
A study of the suitability of evolutionary computation in 3D modeling of forensic remains
CAEPIA'11 Proceedings of the 14th international conference on Advances in artificial intelligence: spanish association for artificial intelligence
A survey on feature selection methods
Computers and Electrical Engineering
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Image registration has been a very active research area in the computer vision community. In the last few years, there is an increasing interest on the application of evolutionary computation in this field and several evolutionary approaches have been proposed obtaining promising results. In this contribution we introduce the use of an advanced evolutionary algorithm, CHC, to solve the 3D image registration problem. The new proposal will be validated using different shapes (both synthetic and magnetic resonance images, and with several of the latter affected by noise and occlusion), considering four different transformations for each of them, and comparing the results with those from ICP, from the usually applied binary-coded genetic algorithms, and from real-coded genetic algorithms.