Wavelet-domain Hidden Markov Tree Research Articles

Texture Segmentation Using Laplace Distribution-Based Wavelet-Domain Hidden Markov Tree Models

Multiresolution models such as the wavelet-domain hidden Markov tree (HMT) model provide a powerful approach for image modeling and processing because it captures the key features of the wavelet coefficients of real-world data. It is observed that the Laplace distribution is peakier in the center and has heavier tails compared with the Gaussian distribution. Thus we propose a new HMT model based on the two-state, zero-mean Laplace mixture model (LMM), the LMM-HMT, which provides significantly potential for characterizing real-world textures. By using the HMT segmentation framework, we develop LMM-HMT based segmentation methods for image textures and dynamic textures. The experimental results demonstrate the effectiveness of the introduced model and segmentation methods.

Automated defect detection in textured materials using wavelet-domain hidden Markov models

An approach that addresses defect detection in textured surfaces based on the wavelet-domain hidden Markov tree (HMT) model is proposed. The proposed scheme includes two successive stages, i.e., training and inspection. During the training process, an HMT for the wavelet transform (WT) of an a priori acquired defect-free template image is modeled using the expectation-maximization (EM) algorithm. With the trained HMT, a log-likelihood map (LLM) that consists of the likelihood of each coefficient can be efficiently constructed. This LLM provides a good classifier for discriminating defects from regular textures. By comparing the LLM of any defective sample under inspection with that of the template, a thresholding process can typically set the coefficients corresponding to the regular texture background to zero, while preserving those corresponding to defective regions. Therefore, in a reconstructed image obtained by the inverse two-dimensional WT of the modified coefficients, the texture patterns will be significantly eliminated, whereas the defective regions will be distinctly highlighted. The performance of the proposed method has been extensively evaluated by a variety of samples with different defect types, shapes, sizes, and texture backgrounds. Experimental results in comparison with other methods demonstrate the effectiveness of the proposed method on defect detection in textured surfaces.

InSAR Interferogram Filtering Using Wavelet-Domain Hidden Markov Tree Model

Speckle noise is an intrinsic property of InSAR interferograms,and InSAR interferograms are sensitive to it.In order to obtain a more accurate interferometric product,a noise filtering step must be performed.According to the statistical property of speckle noise,SAR interferograms filtering using wavelet-domain hidden markov tree(HMT) model is studied.It is employed to filter the real component and the imaginary component of interferogram respectively.The result shows that the new method is powerful to interferogram speckle noise suppression and residues reduction,as well as it can be preserved fine phase details and fringes' structures.The method proposed can improve the accuracy of interferometric products.

Global optimization of wavelet-domain hidden Markov tree for image segmentation

This work presents a global energy minimization method for multiscale image segmentation using convex optimization theory. The construction of energy function is motivated by the intuition that the larger the entropy, the less a priori information one has on the value of the random variables. First, we represent the wavelet-domain hidden Markov tree (WHMT) model of the original image as a structured energy function, which is proved convex in marginal distributions. Next, we derive the maximum lower bound of the energy function through Lagrange dual transform for the purpose of incorporating marginal constraints into optimization. Finally, a modified belief propagation optimization algorithm is used to perform global energy minimization of the dual convex energy function. Experiments on real image segmentation problems demonstrate the superior performance of this new algorithm when compared with nonconvex ones.

Image Segmentation Based on Contextual Label Tree for Retrieval

Image segmentation is an important constituent portion in image processing and retrieval. Based on the traditional Wavelet-domain Hidden Markov Tree (HMT) Multi-scale Segmentation method, this paper presents a Contextual Label Tree (CLT) method according to the dependency information between image blocks belong to different scales, including the relation from the father node, the neighbor nodes and the neighbor nodes of the father. This method calculates the maximal similarity using context vectors that exit on every tree node and realizes image segmentation from coarse-scale to fine-scale. Experiments show that this method is satisfied with its segmentation performance.

Microcalcification detection based on wavelet domain hidden Markov tree model: Study for inclusion to computer aided diagnostic prompting system

In this paper we investigate the performance of statistical modeling of digital mammograms by means of wavelet domain hidden Markov trees for its inclusion to a computer-aided diagnostic prompting system. The system is designed for detecting clusters of microcalcifications. Their further discrimination as benign or malignant is to be done by radiologists. The model is used for segmenting images based on the maximum likelihood classifier enhanced by the weighting technique. Further classification incorporates spatial filtering for a single microcalcification (MC) and microcalcification cluster (MCC) detection. Contrast filtering applied for the digital database for screening mammography (DDSM) dataset prior to spatial filtering greatly improves the classification accuracy. For all MC clusters of 40 mammograms from the mini-MIAS database of Mammographic Image Analysis Society, 92.5%-100% of true positive cases can be detected under 2-3 false positives per image. For 150 cases of DDSM cases, the designed system is capable to detect up to 98% of true positives under 3.3% of false positive cases.

Digital Radiographic Image Denoising Via Wavelet-Based Hidden Markov Model Estimation

This paper presents a technique for denoising digital radiographic images based upon the wavelet-domain Hidden Markov tree (HMT) model. The method uses the Anscombe's transformation to adjust the original image, corrupted by Poisson noise, to a Gaussian noise model. The image is then decomposed in different subbands of frequency and orientation responses using the dual-tree complex wavelet transform, and the HMT is used to model the marginal distribution of the wavelet coefficients. Two different correction functions were used to shrink the wavelet coefficients. Finally, the modified wavelet coefficients are transformed back into the original domain to get the denoised image. Fifteen radiographic images of extremities along with images of a hand, a line-pair, and contrast-detail phantoms were analyzed. Quantitative and qualitative assessment showed that the proposed algorithm outperforms the traditional Gaussian filter in terms of noise reduction, quality of details, and bone sharpness. In some images, the proposed algorithm introduced some undesirable artifacts near the edges.

Feature Extraction with Combination of HMT-Based Denoising and Weighted Filter Bank Analysis for Robust Speech Recognition

In this paper, we propose a new feature extraction method that combines both HMT-based denoising and weighted filter bank analysis for robust speech recognition. The proposed method is made up of two stages in cascade. The first stage is denoising process based on the wavelet domain Hidden Markov Tree model, and the second one is the filter bank analysis with weighting coefficients obtained from the residual noise in the first stage. To evaluate performance of the proposed method, recognition experiments were carried out for additive white Gaussian and pink noise with signal-to-noise ratio from 25 dB to 0 dB. Experiment results demonstrate the superiority of the proposed method to the conventional ones.

Hidden Markov Bayesian texture segmentation using complex wavelet transform

The authors propose a multiscale Bayesian texture segmentation algorithm that is based on a complex wavelet domain hidden Markov tree (HMT) model and a hybrid label tree (HLT) model. The HMT model is used to characterise the statistics of the magnitudes of complex wavelet coefficients. The HLT model is used to fuse the interscale and intrascale context information. In the HLT, the interscale information is fused according to the label transition probability directly resolved by an EM algorithm. The intrascale context information is also fused so as to smooth out the variations in the homogeneous regions. In addition, the statistical model at pixel-level resolution is formulated by a Gaussian mixture model (GMM) in the complex wavelet domain at scale 1, which can improve the accuracy of the pixel-level model. The experimental results on several texture images are used to evaluate the algorithm.