Obtaining polyhedral model by integration of multiview images via genetic algorithms

Hideo Saito, Satoshi Kirihara

Research output: Contribution to journalConference article

Abstract

Shape modeling is a very important issue for many study, for example, object recognition for robot vision, virtual environment construction, and so on. In this paper, a new method for obtaining polyhedral model from multiview images using genetic algorithms (GAs) is proposed. In this method, a similarity between model and every input image is calculated, and then the model which has the maximum similarity is found. For finding the model of maximum similarity, genetic algorithms are used as the optimization method. In the genetic algorithm, the sharing scheme is employed for efficient detection of multiple solution, because some shape may be represented by multiple shape models. Some results of modeling experiments from real multiple images demonstrate that the proposed method can robustly generate model by using the GA.

Original languageEnglish
Pages (from-to)174-184
Number of pages11
JournalProceedings of SPIE - The International Society for Optical Engineering
Volume3204
DOIs
Publication statusPublished - 1997 Dec 1
EventThree-Dimensional Imaging and Laser-based Systems for Metrology and Inspection III - Pittsburgh, PA, United States
Duration: 1997 Oct 141997 Oct 14

Keywords

  • Computer vision
  • Genetic algorithms
  • Multiview images
  • Polyhedral
  • Shape modeling

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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