AUTOMATIC MULTILEVEL IMAGE SEGMENTATION BASED ON FUZZY REASONING

Liang Tang, Wei-Xin Xie, Jian-Jun Huang

Abstract

An automatic multilevel image segmentation method based on sup-star fuzzy reasoning (SSFR) is presented. Using the well-known sup-star fuzzy reasoning technique, the proposed algorithm combines the global statistical information implied in the histogram with the local information represented by the fuzzy sets of gray-levels, and aggregates all the gray-levels into several classes characterized by the local maximum values of the histogram. The presented method has the merits of determining the number of the segmentation classes automatically, and avoiding to calculating thresholds of segmentation. Emulating and real image segmentation experiments demonstrate that the SSFR is effective.

Keywords
automatic multilevel image segmentation; histogram; sup-star fuzzy reasoning

Full Text:

PDF


DOI: 10.5566/ias.v23.p23-31

Image Analysis & Stereology
EISSN 1854-5165 (Electronic version)
ISSN 1580-3139 (Printed version)