Abstract
This paper proposes and analyzes different evolutionary computation techniques for conjointly determining a model and its associated parameters. The context of 3D reconstruction of objects by a functional representation illustrates the ability of the proposed approaches to perform this task using real data, a set of 3D points on or near the surface of the real object. The final recovered model can then be used efficiently in further modelling, animation or analysis applications. The first approach is based on multiple genetic algorithms that find the correct model and parameters by successive approximations. The second approach is based on a standard strongly-typed implementation of genetic programming. This study shows radical differences between the results produced by each technique on a simple problem, and points toward future improvements to join the best features of both approaches.
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Silva, S., Fayolle, PA., Vincent, J., Pauron, G., Rosenberger, C., Toinard, C. (2005). Evolutionary Computation Approaches for Shape Modelling and Fitting. In: Bento, C., Cardoso, A., Dias, G. (eds) Progress in Artificial Intelligence. EPIA 2005. Lecture Notes in Computer Science(), vol 3808. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11595014_15
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DOI: https://doi.org/10.1007/11595014_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-30737-2
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