Image texture can arise not only from surface albedo variations (2D texture) but also from surface height variations (3D texture). Since the appearance of 3D texture depends on the illumination and viewing direction in a complicated manner, such image texture can be called a bidirectional texture function. A fundamental representation of image texture is the histogram of pixel intensities. Sinc...

This paper proposes a pixel-based texture classifier that integrates multiple texture feature extraction methods in order to identify the regions of an input image that belong to a given set of texture patterns. Experimental results with textured images of outdoor scenes show that the proposed technique yields lower classification errors than widely recognized texture classifiers based on speci...

This chapter aims to provide an overview of the foundations of texture processing for biomedical image analysis. Its purpose is to define precisely what biomedical texture is, how is it different from general texture information considered in computer vision, and what is the general problem formulation to translate 2D and 3D textured patterns from biomedical images to visually and biologically ...

Texture is a surface property which is used to identify and recognize the object. Texture analysis is important in many applications of computer image analysis for classification and segmentation of images based on local spatial patterns of intensity or color. In texture classification the goal is to assign an unknown sample image to one set of known texture classes. The proposed method is text...

Optimal texture analysis algorithms for describing degradation of carpets are identified. Experimental design is applied to select from a set of texture analysis algorithms those optimal for identifying texture changes due to degradation of carpets. The degree of wear of a degraded carpet is quantified by comparing its texture to the original texture. The set of texture algorithms is applied on...

A complete texture image retrieval system includes two techniques: texture feature extraction and similarity measurement. Specifically, similarity measurement is a key problem for texture image retrieval study. In this paper, we present an effective similarity measurement formula. The MIT vision texture database, the Brodatz texture database, and the Outex texture database were used to verify t...

The unique iris structures motivate the development of an automatic identification system based on iris characteristic. Texture analysis with multichannel filtering approach is meant to extract feature from iris texture images and encode them into a compact and unique information for each iris. One of the tools that can analyze texture image in multiresolution approach is wavelet transform. In ...

computer graphics, texture mapping, interpolation, texture coordinates, texture derivatives, anti-aliasing This paper describes an algorithm for calculating texture coordinates and their partial derivatives during scan conversion of planar polygons. These values are required in texture mapping for antialiasing algorithms, where a resampling filter combines texture elements (a.k.a. texels) in a ...

Texture is one of the important characteristics used in identifying objects. Texture analysis plays an important role in image analysis and pattern recognition. In this paper a new set of features obtained from the 1-D MRT is shown to be useful for texture analysis. Experiments performed on 30 samples of Brodatz texture images indicate that these features can be used for texture classification.

This paper presents new fixed-rate texture compression systems for RGB and RGBA images, which use variable precision differential encoding for the color codewords, resulting in reduced compression artifacts, in particular for smooth color variations. Experiments using hundreds of textures show that they achieve higher quality than the commonly used S3TC at the same compression ratio or (for RGB...