CONNECTIONIST MODEL BINARIZATION

Author:

BABAGUCHI NOBORU1,YAMADA KOJI1,KISE KOICHI2,TEZUKA YOSHIKAZU1

Affiliation:

1. Department of Communication Engineering, Faculty of Engineering, Osaka University, 2-1 Yamadaoka, Suita, Osaka, 565, Japan

2. Department of Electrical Engineering, College of Engineering, University of Osaka Prefecture, 804 Mozu-Umemachi 4-cho, Sakai, Osaka, 591, Japan

Abstract

Image binarization is a task to convert gray-level images into bi-level ones. Its underlying notion can be simply thought of as threshold selection. However, the result of binarization will cause significant influence on the process of image recognition or understanding. In this paper we discuss a new binarization method, named CMB (connectionist model binarization), which uses the connectionist model. In the method a gray-level histogram is input to a multilayer network trained with the back-propagation algorithm to obtain a threshold which gives a visually suitable binarized image. From the experimental results, it was verified that CMB is an effective binarization method in comparison with other methods.

Publisher

World Scientific Pub Co Pte Lt

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Software

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