-Texture classification is a major issue in image analysis and pattern recognition. A number of methods are proposed in the literature including Local Binary Pattern (LBP). The LBP variant (s) plays an active role to extract texture features for texture classification. These are rotation invariant, noise sensitive or noise insensitive mehods. Each method has its own advantages and disadvantages...

the study was performed for assess the environmental factors affecting weed species diversity and composition of wheat (triticum aestivum l.) fields in jajarm, north-khorasan of iran, in 2009. the crop management (herbicide, amount of nitrogen applied and preceding crop type) and edaphic factors (ph, texture and content of phosphorus) of 16 fields were collected. shannon’s diversity index, spec...

A family of novel texture representations called Ffirst, the Fast Features Invariant to Rotation and Scale of Texture, is introduced. New rotation invariants are proposed, extending the LBP-HF features, improving the recognition accuracy. Using the full set of LBP features, as opposed to uniform only, leads to further improvement. Linear Support Vector Machines with an approximate χ-kernel map ...

Image texture is a complex visual perception. With the everincreasing spatial resolution of remotely sensed data, the role of image texture in image classification has increased. Current approaches to image texture analysis rely on a single band of spatial information to characterize texture. This paper presents a multiscale approach to image texture where first and secondorder statistical meas...

This paper addresses the issue of real time gender classification through texture analysis. The purpose is to perform sensitivity analysis over a number of ROI-Regions of Interest defined over face images. The determination of the smaller ROI yielding robust classification results will be used for fast computation of texture parameters allowing gender classification to operate in real-time. Res...

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 is often considered as a repetitive pattern and the constructing structure is known as texel. The granularity of a texture, i.e. the size of a texel, is different from one texture to another and hence inspiring us applying scale space techniques to texture classification. In this paper Gaussian kernels with different variances (s ) are convolved with the textures from Brodatz album to g...

We investigate the potential of mammographic parenchymal texture as a surrogate marker of the risk to develop Estrogen Receptor (ER) sub-type specific breast cancer. A case-control study was performed, including 118 cancer cases stratified by ER receptor status and 354 age-matched controls. Digital mammographic (DM) images were retrospectively collected and analyzed under HIPAA and IRB approval...

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 ...