Evolutionary computation approaches for shape modelling and fitting

  • Authors:

  • Sara Silva;Pierre-Alain Fayolle;Johann Vincent;Guillaume Pauron;Christophe Rosenberger;Christian Toinard

  • Affiliations:

  • Centro de Informática e Sistemas da Universidade de Coimbra, Polo II - Pinhal de Marrocos, Coimbra, Portugal;Software Department, University of Aizu, AizuWakamatsu, Fukushima ken, Japan;Laboratoire Vision et Robotique, UPRES EA 2078, ENSI de Bourges, Université d’Orléans, Bourges, France;Laboratoire Vision et Robotique, UPRES EA 2078, ENSI de Bourges, Université d’Orléans, Bourges, France;Laboratoire Vision et Robotique, UPRES EA 2078, ENSI de Bourges, Université d’Orléans, Bourges, France;Laboratoire d’informatique Fondamentale d’Orléans, CNRS FRE 2490, ENSI de Bourges, Université d’Orléans, Bourges, France

  • Venue:
  • EPIA'05 Proceedings of the 12th Portuguese conference on Progress in Artificial Intelligence
  • Year:
  • 2005

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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.