A human-perception-like image recognition system based on PAP vector representation with multi resolution concept

Abstract

A human-like robust image recognition system inspired by a psychological brain model [1] has been developed aiming at direct implementation in the VLSI hardware. The Principal Axis Projection (PAP) technique [2] has been employed for feature vector generation, which very well represents the human-perception of similarity in images while substantially reducing the dimensionality. In this study, we have introduced the concept of multi resolution and PAP kernel scanning in the PAP based vector matching algorithm. As a result, very robust image recognition has been demonstrated in gray scale images as well as in binary images. Interestingly, a large digit pattern formed as an aggregation of miniature digits like that shown in Fig. 1 can also be successfully recognized not only in the constituent small digits but also in the entire feature. Although the present algorithm is computationally very expensive, it has been designed fully compatible to execution on VLSI chips which we have developed for vector matching [3–5] and PAP vector generation.