Wavelet-domain hidden Markov models (HMMs) are powerful tools for modeling the statistical properties of wavelet transforms. By characterizing the joint statistics of wavelet coe cients, HMMs e ciently capture the characteristics of many real-world signals. When applied to images, the model can characterize the joint statistics between pixels, providing a very good classi er for textures. Utili...

A new Face Recognition (FR) system based on Singular Values Decomposition (SVD) and pseudo 2D Hidden Markov Model (P2D-HMM) is proposed in this paper. The state sequence of the pseudo 2D HMM are modeled independently which gives superior results when compared to regular 2D HMMs. As a novel point presented here, we have maintained a limited number of quantized Singular Values Decomposition (SVD)...

Background: Accurate brain tissue segmentation from magnetic resonance (MR) images is an important step in analysis of cerebral images. There are software packages which are used for brain segmentation. These packages usually contain a set of skull stripping, intensity non-uniformity (bias) correction and segmentation routines. Thus, assessment of the quality of the segmented gray matter (GM), ...

In this work we propose a Bayesian framework for data fusion of multivariate signals which arises in imaging systems. More specifically, we consider the case where we have observed two images of the same object through two different imaging processes. The objective of this work is then to propose a coherent approach to combine these data sets to obtain a segmented image which can be considered ...

context-dependent modeling is a well-known approach to increase modeling accuracy in continuous speech recognition. the most common way to implement this approach is via triphone modeling. nevertheless, the large number of such models results in several problems in model training, whilst the robust training of such models is often hardly obtained. one approach to solve this problem is via param...

Audio segmentation is an essential problem in many audio signal processing tasks which tries to segment an audio signal into homogeneous chunks, or segments. Most current approaches rely on a change-point detection phase for finding segment boundaries, followed by a similarity matching phase which identifies similar segments. In this thesis, we focus instead on joint segmentation and clustering...

Diagnosing data or object detection in medical images is one of the important parts image segmentation especially those which less effective to identify MRI such as low-grade tumors cerebral spinal fluid (CSF) leaks brain. The aim study address problems associated with detecting tumor and CSF brain difficult magnetic resonance imaging (MRI) another problem also relates efficiency execution time...