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...

We introduce a new method of image texture analysis that is based on techniques that compress images in the form of weighted finite automata (WFA). We show that the WFA representation of images contain information about an image that is useful in analysing local texture and define a simple texture analysis method based on WFA. This method is the first texture analysis method that uses weighted ...

Texture image segmentation plays an important role in texture analysis. This paper presents an approach to image segmentation by texture classification based on fractional discrimination functions. The idea behind this method is to enhance the texture edge points b y means of image decomposition and contextual filtering in terms of the proposed fractional function. In addition, such function is...

Retexturing is the process of realistically replacing the texture of an object or surface in a given image by a new, synthetic one, such that texture distortion as well as lighting conditions of the original image are preserved. The key challenge is to separate the shading information from the actual local texture and to retrieve the texture distortion from an image without any knowledge of the...

Local Binary Pattern (LBP) are considered as a classical descriptor for texture analysis, it has mostly been used in pattern recognition and computer vision applications. However, the LBP gets information from restricted number of local neighbors which is not enough to describe information, other descriptors that get large suffer dimensionality consume much time. In this regard, we propose nove...

Texture image segmentation plays an important role in texture analysis. This paper presents an approach to image segmentation by texture classification based on fractional discrimination functions. The idea behind this method is to enhance the texture edge points b y means of image decomposition and contextual filtering in terms of the proposed fractional function. In addition, such function is...

Age classification based on computer vision has widespread applications. Most of previous works only utilize texture feature or use contour and texture feature separately. In this paper, we proposed an age classification system that integrate contour and texture information. Besides, we improve the traditional Local Binary Pattern(LBP) feature extraction method and get pure texture feature. Sup...

In this report, we compare the performance of four texture synthesis algorithms on synthesizing regular and near-regular textures. Our results show that near-regular texture synthesis remains a challenging problem. This is because a near-regular texture demonstrates both global regularity and local randomness in its texture pattern. It is difficult to preserve both properties in the synthetic t...