235 Copyright © 2013, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. Chapter 14 DOI: 10.4018/978-1-4666-3994-2.ch014 INTRODUCTION Image texture, defined as a function of the spa- tial variation in pixel intensities (grey values), is useful in a variety of applications and has been a subject of intense study by many researchers. It is very hard to define rigorously the texture in an image. The texture can be considered like a structure which is composed of many similar elements (patterns) named textons or texels in some regular or continual relationship. Wilson (1988) points out that textured regions are spatially extended patterns based on more or less accurate repetition of some unit cell; the origin of the term is related to the weaving craft. Gonzalez (1992) relates texture to other concepts like smoothness, Radu Dobrescu Politehnica University of Bucharest, Romania Dan Popescu Politehnica University of Bucharest, Romania Image Processing Applications Based on Texture and Fractal Analysis ABSTRACT Texture analysis research attempts to solve two important kinds of problems: texture segmentation and texture classification. In some applications, textured image segmentation can be solved by classification of small regions obtained from image partition. Two classes of features are proposed in the decision theoretic recognition problem for textured image classification. The first class derives from the mean co-occurrence matrices: contrast, energy, entropy, homogeneity, and variance. The second class is based on fractal di- mension and is derived from a box-counting algorithm. For the purpose of increasing texture classification performance, the notions “mean co-occurrence matrix” and “effective fractal dimension” are introduced and utilized. Some applications of the texture and fractal analyses are presented: road analysis for moving objective, defect detection in textured surfaces, malignant tumour detection, remote land classification, and content based image retrieval. The results confirm the efficiency of the proposed methods and algorithms.