1 Moment Based Texture Segmentation 1 Mihran Tuceryan European Computer-Industry Research Centre (ECRC) Arabellastraße 17 8000 München 81, Germany Abstract Texture segmentation is one of the early steps towards identifying surfaces and ob- jects in an image. In this paper a moment based texture segmentation algorithm is presented. The moments in small windows of the image are used as texture features which are then used to segment the textures. The algorithm has successfully seg- mented binary images containing textures with iso-second order statistics as well as a number of gray level texture images. 1. INTRODUCTION The natural world abounds with textured surfaces. Any realistic vision system that is expected to work successfully, therefore, must be able to handle such input. The process of identifying regions with similar texture and separating regions with different texture is one of the early steps towards identifying surfaces and objects. This process is called texture segmentation and is the major focus of this paper. Texture analysis has been studied for a long time using various approaches. Various methods perform texture analysis directly upon the gray levels in an image. These include gray level co-occurrence matrix (GLCM) [11], autocorrelation function analysis [11], generalized co- occurrence matrices (GCM) [8], second order spatial averages [10], and two-dimensional fil- tering in the spatial and frequency domain [6,5,26,27]. Other approaches operate at a sym- bolic level where a textured image is organized or represented in terms of primitives. Examples of this can be seen in Julesz’s work [17,18] and in syntactic texture analysis. Some texture analysis methods, for example, Beck et al. have examined the role of spatial frequency 1. This research was supported in part by the US National Science Foundation Grant no. CDA 8806599. The facilities of the Pattern Recognition and Image Processing Laboratory at Michigan State University are also gratefully acknowledged. Appeared in Pattern Recognition Letters, vol. 15, pp. 659-668, July 1994.