Texture boundary detection (or segmentation) is an important capability in human vision. Usually, texture segmentation is viewed as a 2D problem, as the definition of the problem itself assumes a 2D substrate. However, an interesting hypothesis emerges when we ask a question regarding the nature of textures: What are textures, and why did the ability to discriminate texture evolve or develop? A...

Texture segmentation is one of the early steps towards identifying surfaces and objects 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 segmented binary images containing textures with identical second-order sta...

Segmentation of images based in texture is a fundamental task in many computer vision environments. There are many objects in the real word whose main characteristic is not their mean gray level but other features related with their texture (like grain size, orientation, etc.) The existing methods for texture segmentation differ in the definition of the texture descriptors, and they range from ...

1. Texture segmentation can lead to multiscale outputs in which the partitions at successive scales are nested. 2. We can incorporate hierarchical segmentation into a K-Means clustering technique by steadily relaxing inter-cluster distances. 3. Thus, it is possible to hierarchically segment images based solely on texture measurements. 4. This hierarchical, multiscale segmentation is useful in i...

In this paper, we propose a texture feature extraction scheme at multiple scales and discuss the issues of rotation and gray-scale transform invariance as well as noise tolerance of a texture analysis system. The nonseparable discrete wavelet frame analysis is employed which gives an overcomplete wavelet decomposition of the image. The texture is decomposed into a set of frequency channels by a...

This thesis is concerned with example based image processing. Example based image processing is a general term for any class of image processing operation where the manipulation and analysis of the image in question is guided by some set of example images. This thesis focuses on two applications, texture synthesis and image segmentation, in which example based image processing is proposed. Give...

The major aim of this paper consists of a comprehensive quantitative evaluation of adaptive texture descriptors when integrated into an unsupervised image segmentation framework. The techniques involved in this evaluation are: the standard and rotation invariant Local Binary Pattern (LBP) operators, multichannel texture decomposition based on Gabor filters and a recently proposed technique that...

The pixels of an image are grouped into several regions for segmentation. In segmentation technique the texture feature parameter is an image analysis technique in the field of Computer vision. In the Segmentation field, there are many techniques are used to segment the images .The proposed approach is to analyze and compare the gray level texture feature techniques, number of clusters, Fuzzy C...

This paper presents an unsupervised dynamic colour texture segmentation method with unknown and variable number of texture classes. Single regions with dynamic textures can furthermore change their location as well as their shape. Individual dynamic multispectral texture mosaic frames are locally represented by Markovian features derived from four directional multispectral Markovian models recu...

In this study, we propose two descriptors on color and texture for content based segmentation. The descriptors are called “the most similar colors in the same neighborhood” and “the most similar textures in the same neighborhood“, respectively. These descriptors are applied on a multi-level segmentation algorithm proposed by the authors of this study [1]. The segmentation algorithm decomposes t...