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Texture Image Feature Extraction Based on Multi-Scale Transform Domain Hidden Markov Tree Model

Author: WangLingHua
Tutor: YangJiaHong
School: Hunan Normal University
Course: Circuits and Systems
Keywords: Denoising Curvelet Fourth-order partial differential equations LLT model Contourlet Weighted CHMT Texture image Feature Extraction
CLC: TP391.41
Type: Master's thesis
Year: 2010
Downloads: 119
Quote: 1
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Abstract


Multiscale geometric analysis of the wavelet analysis with respect to the the approximation performance improvement , its significance is no less than the pressing in the performance improvement of the wavelet analysis relative to the Fourier analysis . Curvelet transform not only multi-scale introduction of the direction parameter to highly anisotropic , superior skills on the edges of the image , with fewer non-zero coefficient expressed image edge information . Apply the Curvelet denoising noise removal thorough, high efficiency denoising , edge protection ability , but there is a \Well cut a striking figure in the field of image processing based on partial differential equations (PDE) Image Denoising image smoothing. The fourth-order partial differential equations denoising well restore the smooth area of ??protection of fine texture , and to avoid the \To noisy image denoising efficient image feature extraction and retrieval , we propose the use of the weight function Curvelet denoising denoising model and the the LLT model combined fourth-order partial differential equations . The experiments show that the model can play both advantages PSNR and visual effects are superior to single the Curvelet or LLT method . The Contourlet Transform analysis by multi-scale analysis and direction which with anisotropic scaling relations , its decomposition coefficients with non-Gaussian and persistent . Good correlation to describe the texture image features for Contourlet domain hidden Markov tree model (CHMT) equal consideration to the impact of the sub - node of the parent node adjacent node , the paper presents a weighted Contourlet domain hidden Markov tree model of the texture image feature extraction . Analysis sub- node status , not only to consider the parent node , but also take advantage of the right to re- evaluation parent node sibling pairs junction point , and manifested through additional state transition matrix , making the new model more accurate description of Contourlet coefficient and HMT intrinsically linked . While the use of the KL distance to calculate the similarity between the images , the experimental results show that the model than the the CHMT average retrieval rate of 7% -46 % .

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Information processing (information processing) > Pattern Recognition and devices > Image recognition device
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