Obtaining polyhedral model by integration of multiview images via genetic algorithms

Hideo Saito, Satoshi Kirihara

研究成果: Conference article

抜粋

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.

元の言語English
ページ(範囲)174-184
ページ数11
ジャーナルProceedings of SPIE - The International Society for Optical Engineering
3204
DOI
出版物ステータスPublished - 1997 12 1
イベントThree-Dimensional Imaging and Laser-based Systems for Metrology and Inspection III - Pittsburgh, PA, United States
継続期間: 1997 10 141997 10 14

ASJC Scopus subject areas

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

フィンガープリント Obtaining polyhedral model by integration of multiview images via genetic algorithms' の研究トピックを掘り下げます。これらはともに一意のフィンガープリントを構成します。

  • これを引用