Co-occurrence Matrix and Fractal Dimension for Image Segmentation

TL;DR

This paper introduces a new image segmentation method using fractal dimension and co-occurrence matrix texture operators, demonstrating its effectiveness through comparative analysis on various images.

Contribution

A novel segmentation approach combining fractal dimension with co-occurrence matrix techniques for improved object recognition.

Findings

Effective segmentation results shown on multiple images

Comparative analysis highlights advantages over existing methods

Fractal dimension-based operator enhances texture analysis

Abstract

One of the most important tasks in image processing problem and machine vision is object recognition, and the success of many proposed methods relies on a suitable choice of algorithm for the segmentation of an image. This paper focuses on how to apply texture operators based on the concept of fractal dimension and cooccurence matrix, to the problem of object recognition and a new method based on fractal dimension is introduced. Several images, in which the result of the segmentation can be shown, are used to illustrate the use of each method and a comparative study of each operator is made.