Wavelet-domain hidden Markov models (HMMs), in particular hidden Markov tree (HMT), were recently proposed and applied to image processing, where it was usually assumed that three subbands of the 2-D discrete wavelet transform (DWT), i.e. HL, LH, and HH, are independent. In this paper, we study wavelet-based texture analysis and synthesis using HMMs. Particularly, we develop a new HMM, called H...

Purpose – The purpose of this paper is to review and provide a detailed performance evaluation of a number of texture descriptors that analyse texture at micro-level such as local binary patterns (LBP) and a number of standard filtering techniques that sample the texture information using either a bank of isotropic filters or Gabor filters. Design/methodology/approach – The experimental tests w...

Texture as a measure of spatial features has been useful as supplementary information to improve image classification in many areas of research fields. This study focuses on assessing the ability of different textural vectors and their combinations to aid spectral features in the classification of silicate rocks. Texture images were calculated from Landsat 8 imagery using a fractal dimension me...

In this work we apply a texture classification network to remote sensing image analysis. The goal is to extract the characteristics of the area depicted in the input image, thus achieving a segmented map of the region. We have recently proposed a combined neural network and rule-based framework for texture recognition. The framework uses unsupervised and supervised learning, and provides probab...

Human vision system has limitations in distinguishing the broad range of gray level values. Human eye can discriminate pixel intensities up to 15-30 gray levels. This restricts the qualitative analysis of radiological images. Hence quantitative analysis is preferred to reveal more information from the image. This article presents texture feature based approach for biomedical image analysis. Ima...