Texture measures are commonly used to increase the number of input bands in order to improve classification accuracy, especially for panchromatic or true colour imagery. While the use of texture measures in pixel-based analysis has been well documented, this is not the case for texture measures calculated in an object-based environment. Because texture calculations are computer intensive, fewer...

In practice, image segmentation can be performed in many areas like medical and satellite communications to detect objects and regions in the image.The texture enhancement methods representing all texture information using a single image component. In previous texture enhancement methods reduce noise or artifacts in the image to highlight the textures with the help of filters which reduces the ...

This paper presents a method which extends the use of genetic programming (GP) to a complex domain, texture segmentation. By this method, segmentation tasks are performed by texture classifiers which are evolved by GP. Small cutouts sampled from images of various textures are used for the evolution. The generated classifiers directly use pixel values as input. Based on these classifiers an algo...

The main goal of this paper is to introduce formally the concept of texture segmentation/identification in three dimensional images. A major problem in texture texture segmentation/identification is the lack of robustness to both translations and rotations. This problem is more difficult to overcome in 3D-images, such as those generated by modalities such as x-ray CT and MRI. To facilitate 3D-t...

Texture segmentation is the process of partitioning an image into regions with different textures containing a similar group of pixels. Detecting the discontinuity of the filter's output and their statistical properties help in segmenting and classifying a given image with different texture regions. In this proposed paper, chili x-ray image texture segmentation is performed by using Gabor filte...

Contourlet domain Hidden Markov Models can provide a powerful approach for statis tical modeling and processing of contourlet coefficients of natural textural images. This multiscale model captures the statistical structure of smooth, texture and edge regions of an image. Contourlets have emerged as a new mathematical tool for image processing. They provide a compact and decorrelated image repr...

The use of active contours for texture segmentation seems rather attractive in the recent research, indicating that such methodologies may provide more accurate results. In this paper, a novel model for texture segmentation is presented, combining advantages of the active contour approach with texture information acquired by the Local Binary Pattern (LBP) distribution. The proposed LBP scheme h...

This paper presents a method of evaluating unsupervised texture segmentation algorithms. The control scheme of texture segmentation has been conceptualized as two modular processes: [l) feature computation and (2) segmentation of homogeneous regions based on the feature values. Three feature extraction methods are considered: gray level co-occurrence matrax, Laws’ texture energy and Gabor multi...