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Publications (8)0 Total impact

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    ABSTRACT: Since Otsu algorithm does not take the image spatial neighbor information into consideration, we combine the Markov random field with Otsu algorithm to integrate gray level information and spatial correlation information for the pixels. In this paper, Otsu thresholding algorithm based on Markov Random Field is proposed. In this algorithm, the neighborhood rejectability function is imported to Otsu algorithm and an threshold selection function is improved. The experiment results verify that applying our algorithm to road image segmentation can achieve good effects.
    Seventh International Conference on Natural Computation, ICNC 2011, Shanghai, China, 26-28 July, 2011; 01/2011
  • Seventh International Conference on Natural Computation, ICNC 2011, Shanghai, China, 26-28 July, 2011; 01/2011
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    ABSTRACT: The contour analysis and identification are the important aspects in visual surveillance research. The paper proposes a fuzzy identification method of contours. First, according to the description of a contour based on the chain-code method, the proposed method utilizes the statistical features of contours including the chain-code entropy and chain-code space distribution entropies, from which the feature vector of a contour is composed. Then, the method generates the contour pattern from some contour samples and uses the approaching principle to identify a contour. Since our method integrates effectively multiple statistical feathers of its chain-code and employs a fuzzy pattern recognition technique, the experiments show quantitatively that it can achieve good results from various metrics.
    01/2011;
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    ABSTRACT: We propose a robust approach of producing a high-resolution (HR) image from a sequence of low-resolution (LR), blurred and noisy images. In the proposed approach, we specifically focus on the motion model of Gaussian Pyramid Optical Flow (GPOF) registration which achieves the sub-pixel precision and enables large pixel motions, while keeping the size of the integration window relatively small. In the process of super-resolution reconstruction, our method is based on the use of L1-norm both in the measurement term and the regularization term called Bilateral Total Variation (BTV) as the prior model to penalize high spatial frequency signals and preserve edges. Specially, we introduce the Median “shift and add” idea to initialize the HR image value in the iterative steps for the optimization of the objective function, when the motions between LR frames are pure translations and the blur is space invariant.
    Sixth International Conference on Natural Computation, ICNC 2010, Yantai, Shandong, China, 10-12 August 2010; 01/2010
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    ABSTRACT: A single image super resolution algorithm for license plate preprocessing is proposed in this paper. The image to be enhanced is modeled as a Markov Random Field and is estimated from the input low resolution image by image patch pairs. From the input image and the training set, observation function and compatibility function can be calculated. Then Bayesian Belief Propagation is used to select the most probable high resolution patches candidate in the MRF model. The experiment shows that using this method can get better license plate with more information for further recognition.
    Sixth International Conference on Natural Computation, ICNC 2010, Yantai, Shandong, China, 10-12 August 2010; 01/2010
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    ABSTRACT: We propose a new algorithm about multi-scale-based super-resolution on face image. First, steerable pyramid is used to capture low-level local features in face images, and then these features are combined with pyramid-like parent structure and image patch synthetic approach based on neighborhood to predict the best prior. After that, the prior is integrated into Bayesian maximum a posteriori (MAP) framework. Finally, the optimal high-resolution face image is obtained by a global linear smoothing operator. It is can be seen from the experimental result that oriented facial features in the high-resolution face are recovered well. The most crucial is that our algorithm significantly reduces the computational complexity.
    01/2010;
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    ABSTRACT: We address a learning-based method for super resolution. Training sample set provide a candidate high resolution interpretation for the low-resolution images. Modeling image patches as Markov network node, and we learn the parameters of the network from training set,compute probability distribution by K-means algorithm. Given a new low-resolution image to enhance, we select from the training data a set of 10 candidate high-resolution patches for each patch of low-resolution image. In Bayesian belief propagation, we use compatibility relationship between neighboring candidate patches to select the most probable high-resolution candidate. The experimental results show that this method can obtain better result.
    Fifth International Conference on Natural Computation, ICNC 2009, Tianjian, China, 14-16 August 2009, 6 Volumes; 01/2009
  • Yanjie Ma, Hua Zhang, Yanbing Xue
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    ABSTRACT: We address a novel method for super resolution based on Markov random field (MRF). Modeling image patches as MRF node, and we learn the parameters from training samples. Training sample set provide a candidate high-resolution interpretation for the low-resolution images. Given a new low-resolution image to enhance, we select from the training data a set of 10 candidate high-resolution patches for each patch of low-resolution image. In Bayesian belief propagation, we use compatibility relationships between neighboring candidate patches to select the most probable high-resolution candidate. The experimental results show that this method can obtain the better result.
    01/2009;