Dynamic textures are temporally continuous and infinitely varying sequences of images with certain spatial and temporal stationarity properties and have many potential applications such as computer graphics, computer vision, animation, and computer games. These applications usually require dynamic texture segmentation that is not an easy task especially when the background is cluttered and text...

Biometric identification systems recognize persons by a digital signature derived from a particular physiological attribute. One such attribute is the unique patterns that exist in the texture of an iris. These patterns provide sufficient information to uniquely identify an individual. Segmentation of the iris texture from an acquired digital image is not always accurate the image contains nois...

In this paper, we propose a novel variational method for unsupervised texture segmentation. We use a Gabor filter bank to extract texture features. Some of the filtered channels form a multidimensional Gaborian feature space. To avoid deforming contours directly in a vector-valued space we use a Gaussian mixture model to describe the statistical distribution of this space and get the boundary a...

We study the applicability of a set of texture descriptors introduced in recent work by the author to texture-based segmentation of images. The texture descriptors under investigation result from applying graph indices from quantitative graph theory to graphs encoding the local structure of images. The underlying graphs arise from the computation of morphological amoebas as structuring elements...

This chapter presents a novel and generic framework for image segmentation using a compound image descriptor that encompasses both colour and texture information in an adaptive fashion. The developed image segmentation method extracts the texture information using low-level image descriptors (such as the Local Binary Patterns (LBP)) and colour information by using colour space partitioning. The...

The adaptive integration of the colour and texture attributes in the development of complex image descriptors is one of the most investigated topics of research in computer vision. The substantial interest shown by the research community in colour–texture-based segmentation is mainly motivated by two factors. The first is related to the observation that the imaged objects are often described at...

Texture boundary segmentation is conventionally thought to be mediated by global differences in Fourier energy, i.e., low-order texture statistics. Here, we have examined the importance of higher order statistical structure of textures in a simple second-order segmentation task. We measured modulation depth thresholds for contrast boundaries imposed on texture samples extracted from natural sce...

In this paper we present a novel technique for unsupervised texture segmentation of wireless capsule endoscopic images of the human gastrointestinal tract. Our approach integrates local polynomial approximation algorithm with the well-founded methods of color texture analysis and clustering (k-means) leading to a robust segmentation procedure which produces fine-grained segments well matched to...

This paper deals with region based watershed segmentation for developing “MemberingFilters”.Watershed transformation is a common technique for image segmentation. Region based segmentation classify a particular image into a number of regions or classes. Region-based segmentation methods attempt to partition or group regions according to Intensity values from original images, Textures or pattern...

T exture analysis is the extraction of textural features from images (Tuceryan and Jain, 1998). The meaning of texture varies, depending on the area of science in which it is used. In general, texture refers to the physical character of an object or the appearance of an image. In image analysis, texture is defined as a function of the spatial variation in intensities of pixels (Tuceryan and Jai...