Texture mapping is a common operation to increase the realism of three-dimensional meshes at low cost. We propose a new texture optimization algorithm based on the reduction of the physical space allotted to the texture image. Our algorithm optimizes the use of texture space by computing a warping function for the image and new texture coordinates. Neither the mesh geometry nor its connectivity...

In this paper, a new texture classification method is provided for dividing texture images into three classes: periodic, directional, and random. The method is based on the fact that for a directional texture image, the magnitudes of its Fourier spectrum will concentrate on a certain direction; for periodic, on several directions; and for random, spread out over all directions. To use this fact...

This paper utilizes a spatial texture correlation and the intelligent classification algorithm (ICA) search strategy to speed up the encoding process and improve the bit rate for fractal image compression. Texture features is one of the most important properties for the representation of an image. Entropy and maximum entry from co-occurrence matrices are used for representing texture features i...

Texture segmentation is an important task in image processing. The objective is to assign the same value to those pixels in an image which belong to the same texture. This is usually called pixel-classification. There exist two fundamental approaches to solve this task. The first and most general approach we call classification oriented by which we mean that the actual labeling is done by some ...

The browning kinetic was recorded using color data information from images of avocado slices. GLCM image texture was used to describe the reaction. In the experiment, images of avocado slices stored at 4°C were captured and saved in tiff format. The classical color intensity index (the mean L* value) and some statistics GLCM image textures were used. Results showed that it is possible to use GL...

An algorithm for the simultaneous filling-in of texture and structure in regions of missing image information is presented in this paper. The basic idea is to first decompose the image into the sum of two functions with different basic characteristics, and then reconstruct each one of these functions separately with structure and texture filling-in algorithms. The first function used in the dec...