Abstract
This paper describes a method which gives new pattern descriptions of observed images for computer vision with robustness to perturbation in the observed images. Although experimental results support that biological pattern vision should extract information about the observed images from signals with a variety of spatial frequencies over its broad receptive field, it is difficult to implement such an extraction due to the uncertainty relationship between the signal and its Fourier Transform; therefore, the biological pattern vision is considered to possess a function for suppression of this uncertainty relationship. We regard this function as fusion and consider that this fusion brings the robustness to the biological pattern vision; then, we propose a method of generating pattern descriptions of the observed images via this fusion. We first describe this fusion as a mathematical constraint. We second introduce a stochastic model satisfying this constraint. We consequently arrive at a Linear Programming; therefore, the pattern descriptions of the observed images can effectively be generated by the simplex method. We have made experiments on pattern matching with the proposed pattern descriptions. Their results have demonstrated that the proposed pattern descriptions are robust to the perturbations in contrast to a conventional method.
