Obtaining a polyhedral model by integration of multiview images via genetic algorithms
Hideo Saitô, Satoshi Kirihara · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1997
Shape modeling is a very important issue for many studies, 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.