A novel texture detecting analysis for medical pathological tissue images was developed by fractal Brown model. According to fractal Brown random field model, a discrete fractal random field for image texture detection on a definite scale could be derived from the Brown model. Using fractal dimensions in partial region of the image and gray difference between adjacent pixels and relevant Hurst ...

Dynamic texture description has been studied extensively due to its wide applications in the field of computer vision. Local binary pattern (LBP) and various variants account for a large part dynamic methods because advantages, such as good discriminability low computational complexity. However, many LBP-based directly extract feature from pixel intensities only use proportion pixels local neig...

A non-parametric method for texture synthesis is proposed. The texture synthesis process grows a new image outward from an initial seed, one pixel at a time. A Markov random field model is assumed, and the conditional distribution of a pixel given all its neighbors synthesized so far is estimated by querying the sample image and finding all similar neighborhoods. The degree of randomness is con...

The Bidirectional Texture Function (BTF) is the recent most advanced representation of material surface visual properties. BTF specifies the changes of its visual appearance due to varying illumination and viewing angles. Such a function might be represented by thousands of images of given material surface. Original data cannot be used due to its size and some compression is necessary. This pap...

Texture mapping is a common technique used to increase the visual quality of 3D scenes. As texture mapping requires large memory to deal with large textures generally r e quired in the current visual systems, we propose an algorithm for compressing a pyramid texture used for mipmapping. Vector quantization is used to compress all levels of the pyramid texture to one representative value databoo...

In this paper we address the problem of detecting different type of defects on random textured tiles. We adopt Gabor wavelet for analysis of random textured surfaces. Unlike the existing methods which arrange the Gabor filter Bank in such a way that the half-magnitude contour of neighboring filters in frequency domain touch each other, we allows the bandwidth of filters to vary. This flexibilit...

This paper presents a simple, novel, yet very powerful approach for texture classification based on compressed sensing. At the feature extraction stage, a small set of random features is extracted from local image patches. The random features are embedded into a bag-of-words model to perform texture classification, thus learning and classification are carried out in the compressed domain. The p...

Unmanned aerial vehicle (UAV) remote sensing has great potential for vegetation mapping in complex urban landscapes due to the ultra-high resolution imagery acquired at low altitudes. Because of payload capacity restrictions, off-the-shelf digital cameras are widely used on medium and small sized UAVs. The limitation of low spectral resolution in digital cameras for vegetation mapping can be re...

This chapter reviews and discusses various aspects of texture analysis. The concentration is on the various methods of extracting textural features from images. The geometric, random field, fractal, and signal processing models of texture are presented. The major classes of texture processing problems such as segmentation, classification, and shape from texture are discussed. The possible appli...

We present an algorithm based on statistical learning for synthesizing static and time-varying textures matching the appearance of an input texture. Our algorithm is general and automatic, and it works well on various types of textures including 1D sound textures, 2D texture images and 3D texture movies. The same method is also used to generate 2D texture mixtures that simultaneously capture th...