Texture is an important spatial feature, useful for identifying objects or regions of interest in an image. One of the most popular statistical methods used to measure the textural information of images is the grey-level co-occurrence matrix (GLCM). The other statistical approach to texture analysis is the texture spectrum approach. The present paper combines the fuzzy texture unit and GLCM app...

Texture image analysis is one of the most important working realms of image processing in medical sciences and industry. Up to present, different approaches have been proposed for segmentation of texture images. In this paper, we offered unsupervised texture image segmentation based on Markov Random Field (MRF) model. First, we used Gabor filter with different parameters’ (frequency, orientatio...

The studies of texture perception focused mainly on displaying the textures with interior space gap in the millimeter level and below. We could call this kind of texture “fine texture”. In contrast, the “macro texture” has space gap in centimeters and above, and it is considered as the special texture with tiny texture pattern covered on curved surface. Macro texture contains richer surface inf...

Texture is one of the important features used in CBIR systems. The methods of characterizing texture fall into two major categories: Statistical and Structural. An experimental comparison of a number of different texture features for content-based image retrieval is presented in this paper. The primary goal is to determine which texture feature or combination of texture features is most efficie...

Image quilting is a texture synthesis technique to create a large texture by wrapping around patches of a small texture in a way that the repetition of small texture is not noticeable. The basic algorithm is to randomly select small patches in a given texture. These patches are then positioned in a large texture to be synthesized and blended across boundaries to remove the appearance of boundar...