Kim Ju Sung, Yoon Myoung Young

The Transactions of the Korea Information Processing Society (1994 ~ 2000), Vol. 3, No. 6, pp. 1636-1645, Nov. 1996
10.3745/KIPSTE.1996.3.6.1636,   PDF Download:

Abstract

In this paper we propose a new scheme for conditional two dimensional(2-D) moment-based classification of patterns on the basis of Gibbs random fields which are well suited for representing spatial continuity that is the characteristic of the most images. This implementation contains two parts:feature extraction and pattern classification. First of all, we extract feature vector which consists of conditional 2-D moments on the basis of estimated Gibbs parameter. Note that the extracted feature vectors are invariant under translation, rotation, size of patterns. Next, in the classification phase, the minimization of the discrimination cost function for a specification determines the corresponding template pattern. In order to evaluate th performance of the proposed scheme, classification experiments with training document sets of characters have been carried out on 486 66Mhz PC. Experiments reveal that the proposed scheme has high classification rate over 94%.

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