Texture is a fundamental feature which provides significant information for image classification, and is an important content used in content-based image retrieval (CBIR) system. To implement texture-based image database retrieval, texture segmentation techniques are need to segment textured regions from arbitrary images in the database. Texture segmentation has been recognized as a difficult p...

A fast and robust type of unsupervised multispectral texture segmentation method with unknown number of classes is presented. Single decorrelated monospectral texture factors are represented by four local autoregressive random field models recursively evaluated for each pixel and for each spectral band. The segmentation algorithm is based on the underlying Gaussian mixture model and starts with...

The general problem of unsupervised textured image segmentation remains a fundamental but not entirely solved issue in image analysis. Many studies have proven that statistical model-based texture segmentation algorithms yield good results provided that the model parameters and the number of regions be known a priori. In this paper, we present an unsupervised texture segmentation method which d...

Texture analysis has been used extensively in the computer–assisted interpretation of SAR sea ice imagery. Provision of maps which distinguish relevant ice types is significant for monitoring global warming and ship navigation. Due to the abundance of SAR imagery available, there exists a need to develop an automated approach for SAR sea ice interpretation. Grey level co-occurrence probability ...

A new, computationally inexpensive, approach to texture recognition and segmentation is proposed based on a discriminating quantity called the texture energy tensor (TET). The TET is defined as the sum over a window of the products of pixel pairs at fixed vector displacement. For mathematical textures the TET gives easy to interpret results. Texture classification and segmentation using Laws ma...

Color segmentation is a very popular technique for real-time object tracking. However, even with adaptive color segmentation schemes, under varying environmental conditions in video sequences, the tracking tends to be unreliable. To overcome this problem, many multiple cue fusion techniques have been suggested. One of the cues that complements color nicely, is texture. However, texture segmenta...

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 iso-second order statistic...

Image segmentation is a procedure that partitions an image into disjointing segments with each segment sharing similar properties such as intensity, color, boundary and texture. In general, three main types of image features are used to guide image segmentation, which are intensity or color, edge, and texture. In other words, image segmentation methods generally fall into three main categories:...

Traditional approaches for texture segmentation via wavelet transform usually adopt textural features to achieve segmentation purposes. However, for a natural image, the characteristics of the pixels in a texture region are not similar everywhere from a global viewpoint, and over-segmentation often occurs. To deal with this issue, an unsupervised texture segmentation method based on determining...

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...