Texture Recognition using Hybrid Fractal and Blocking Approach

Abstract

Texture analysis is an important and useful area of study in machine vision. Most natural surfaces exhibit texture and a successful vision system must be able to deal with such like surfaces. Many natural surfaces have a statistical quality of roughness and self-similarity at different scales. Fractals are very useful and have become popular in modeling these properties in image processing. This work adopts analyzing samples by three methods fractal dimension, block approach and Hybrid method (fractal dimension method with block approach). The fractal dimension get a highest recognition rate among remaining used methods, it obtain a rate 95% as compare with 40% Block Approach model, 65.5% Hybrid method. The results show the efficiency of fractal dimension recognition than blocking approach recognition and hybrid recognition in textures. General Terms Image Processing, Texture Recognition and Binarization method.