In this pater, we propose a method for matching of two images (stereo, motion stereo, etc.) using relaxation. We have already proposed an algebraic expression of line images using unit vectors, and matching method based on similarity measure between two image graphs. This similarity measure of images is insensitive to scaling, rotation, gray level modification and small motion between the two images in the case when we examine image registration or image matching. The approach based on the line structural similarity results in high rate of correspondence between nodes of the two images. In order to obtain higher rate of correspondence, we introduce a relaxation method when examine the degree of similarity between the two images. Our relaxation method improves a relational similarity of correspondence between two image graphs in an iterative manner. The relational similarity is defined as a correct likelihood of correspondence between nodes in consideration of connective relationship of the image graphs. Finally, we show several experimental results which confirm effectiveness of our approach.

Understanding unknown objects in images is one of the most important fields of the computer vision. We are confronted with the problem of dealing with the ambiguity of the image information about unknown objects in the scene. The purpose of this paper is to propose a new object recognition method based on the fuzzy relation system and the fuzzy integral. In order to deal with the ambiguity of the image information, we apply the fuzzy theory to object recognition subjects. Firstly, we define the degree of similarity based on the fuzzy relation system among input images and object models. In the next, to avoid the uncertainty of relations between the input image and the 2-D aspects of models, we integrate the degree of similarity obtained from several input images by the fuzzy integral. This proposing method makes it possible to recognize the unknown objects correctly under the ambiguity of the image information. And the validity of our method is confirmed by the experiments with six kinds of chairs.

In case of object recognition using 3-D configuration data, the scale and poses of the object are important factors. If they are not known, we can not compare the object with the models in the database. Hence we propose a strategy for object recognition independently of its scale and poses, which is based on Hopfield neural network. And we also propose a strategy for estimation of the camera motion to reconstruct 3-D configuration of the object. In this strategy, the camera motion is estimated only with the sequential images taken by a moving camera. Consequently, the 3-D configuration of the object is reconstructed only with the sequential images. And we adopt the multiple regression analysis for estimation of the camera motion parameters so as to reduce the errors of them.