Unbalanced Scaling and Rotation Invariant Recognition by

抄録

In the real world application, recognition of two-dimensional images regardless of their rotational orientation and size, i.e., smaller or larger, is one of the significant problems in computer vision. Techniques of third order neural network and Zernike moment have shown to be successful in solving the problem, but their limitaion is costly in term of computational time and network complexity. In this paper, we apply a technique of Fuzzy c-Mena to resolve the problem of invariant recognition under rotation and scaling regardless of its vertical and horizontal size of an image. The learning of Fuzzy c-Mean does not have the invariant capability; therefor we presented in this paper a new technique with some modificaiotns based on the concept of principal component analysis.