Abstract
This paper describes a method of object recognition based on aspect graph using the Dempster-Shafer theory. The method deals with several sequential images as input images, and extracts basic probability of the model objects for each input image. The basic probability represents ambiguous information for object discrimination. Our approach combines basic probability which is extracted from each image based on Dempster-Shafer's rule for combining. We have applied our approach to the discrimination of objects. The experimental results show higher reliability than conventional Baysian approach.
