Abstract
Fukushima's Selective Attention Model is a model of biological vision system. It has engineering merit of deformation and position shift tolerant recognition and recollection of the recognized object in an input image. Based on this feature, we have proposed a method for hybrid image understanding in which each object is recognized, recollected and segmented sequentially even when the objects are overlapped and occluded. However, parameter setting for the fine object recollection is difficult with the Selective Attention Model and parameters search is necessary to tune the given image. In this paper, we propose a method for the parameter search based on an evaluation of image recollection precision in the hybrid image understanding process. A computer simulation result demonstrates the validity of the proposed method.
