A framework for learning the structure of textures is introduced in this work. A Bayesian network, whose structure and parameters are learned from a small texture sample, is used to characterize the high-dimensional probability density function (PDF) that models the given texture. The underlying network learning framework is based on the combination of tools such as kernel density estimation an...

We introduce a new image texture segmentation algorithm, HMTseg, based on wavelet-domain hidden Markov tree (HMT) models. The HMT model is a tree-structured probabilistic graph that captures the statistical properties of wavelet coeecients. Since the HMT is particularly well suited to images containing singularities (edges and ridges), it provides provides a good classiier for textures. Utilizi...

This paper is concerned to the application of a relatively new image texture segmentation algorithm named Hidden Markov Tree (HMT) to the detection of micro-calcification clusters in mammograms. The HMT is a wavelet-based tree-structured probabilistic graph that can capture the statistical properties of the coefficients of the wavelet transform. The aim of this approach is, on the one hand, to ...

We introduce a new image texture segmentation algorithm, HMTseg, based on wavelet-domain hidden Markov tree (HMT) models. The HMT model is a tree-structured probabilistic graph that captures the statistical properties of wavelet coeecients. Since the HMT is particularly well suited to images containing singularities (edges and ridges), it provides provides a good classiier for textures. Utilizi...

Texture plays an important role in human visual perception and offers crucial cues for solving a wide range of computer vision problems, such as image segmentation or scene analysis. The segmentation of texture is a key problem in computer vision and image understanding, the objective of which is to partition an image into several regions characterized by homogeneous texture attributes. Over th...

Computer-aided diagnosis schemes are being developed to assist radiologists in mammographic interpretation. In this study, we investigated whether texture features could be used to distinguish between mass and non-mass regions in clinical mammograms. Forty-five regions of interest (ROIs) containing true masses with various degrees of visibility and 135 ROIs containing normal breast parenchyma w...

introduction: estimation of cation exchange capacity (cec) with reliable soil properties can save time and cost. pedotransfer function (ptf) is a common method in estimating certain soil properties (e.g. cec) that has been wieldy used for many years. one of the common techniques that have been used to develop ptfs is multiple linear regressions. in this method, all easily obtained soil properti...

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...

MISHRA, D. K. 2005. Germplasm variability in neem (Azadirachta indica). A variant neem (Azadirachta indica) tree, growing in the Arid Forest Research Institute (AFRI), Jodhpur, India was compared with the nearby growing normal tree and a neem tree from Thailand of the same age. The tree has now attained the height of 6 m and collar diameter of 38 cm. The tree differed morphologically, viz. leaf...