We discuss a novel method for recovering fundamental, perceptually motivated structural features of a texture pattern: anisotropy, symmetry, and regularity. The method is based on extended spatial grey-level difference statistics which describe pairwise pixel interactions and yield an interaction map used to assess the overall two-dimensional structure of interactions and extract the significant short- and long-range interactions (intersample spacings). The new approach extends, in digital images, the notion of greylevel difference to arbitrary spacing vectors (i.e. any angle at any displacement). This provides the necessary background for precise anisotropy (or directionality) and symmetry analysis. Experimental results are shown with a set of Brodatz images that range from highly regular to patterns with weak regularity or anisotropy. A few especially interesting examples of recovering hardly visible structural features are given. Finally, the approach is applied to rotation-invariant texture classification.

: Human texture perception relies on a few basic high-level features including directionality and symmetry. Today, research on oriented patterns finds its applications in various areas of applied machine vision. In this study, we present and investigate a new method for assessing pattern anisotropy via texture symmetry. Pattern orientation is viewed as the direction of persistent statistical texture symmetry. The proposed method uses the spatial dependence of an extended spatial gray-level difference feature to yield an interaction symmetry map which reflects the symmetry of both short- and long-range pixel interactions. Pattern orientation can then be assessed via the directions of global symmetry - the characteristic axes of the pattern. Experimental results are shown which support our claim that texture symmetry is deeply related to the perceived orientation. The results are compared to the orientations obtained in a recent study that uses the traditional filtering framework. The pr...

We have recently introduced a new tool for texture analysis called feature based interaction map (FBIM). The FBIM approach can be efficiently used to assess fundamental structural properties of textures such as anisotropy, symmetry, orientation and regularity [4]. It has been demonstrated [5] that the FBIM is suitable for rotation-invariant texture classification of patterns with regular, weak regular, or linear structure. In this paper, we show how the interaction map can be applied as a structural filter for segmentation, detection of textured objects and texture defects, analysis of oriented structures and shape-from-texture. The power of the FBIM filter is in its unique capability to grasp the structure of pixel interactions typical for a given texture pattern. To efficiently use this capability, we propose a fast running implementation of the FBIM algorithm and present pilot experimental results demonstrating the potential of the FBIM approach in diverse tasks and applications. 1...

Introduction Motivated by the discovery of the high level texture features responsible for perceptual grouping of textures [11] and the development of the Markov-Gibbs texture model with pairwise pixel interactions [9], we have recently proposed the method of feature based interaction maps (FBIM) and applied this new tool to the problem of pattern orientation [4] and rotation-invariant texture classification [7]. Experimental results have demonstrated that the FBIM approach can be used to recover the basic structural properties and orientation of a wide range of patterns, including weak structures. In [11], the fundamental, perceptually motivated high level texture features were identified as directionality (anisotropy) versus nondirectionality, periodicity versus irregularity and, probably, structural complexity. These features reflect the intrinsic symmetry properties of the process, natural or artificial, that generates the texture pattern. Figure 1 illustra

We have recently introduced a new tool for texture analysis called feature based interaction map (FBIM). The FBIM approach can be efficiently used to assess fundamental structural properties of textures such as anisotropy, symmetry, orientation and regularity [4]. It has been demonstrated [5] that the FBIM is suitable for rotation-invariant texture classification of patterns with regular, weak regular, or linear structure. In this paper, we show how the interaction map can be applied as a structural filter for segmentation, detection of textured objects and texture defects, analysis of oriented structures and shape-from-texture. The power of the FBIM filter is in its unique capability to grasp the structure of pixel interactions typical for a given texture pattern. To efficiently use this capability, we propose a fast running implementation of the FBIM algorithm and present pilot experimental results demonstrating the potential of the FBIM approach in diverse tasks and applications. 1 1.