The Experts below are selected from a list of 171 Experts worldwide ranked by ideXlab platform
Xiaofeng Wang - One of the best experts on this subject based on the ideXlab platform.
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multi scale local region based level set method for image segmentation in the presence of intensity inhomogeneity
Neurocomputing, 2015Co-Authors: Xiaofeng Wang, Yigang ZhangAbstract:Abstract Intensity inhomogeneity arising from the imperfect image acquisition process is a major challenge for image segmentation. Most of widely used image segmentation methods usually fail to segment the image with intensity inhomogeneity due to the assumption of intensity homogeneity. In this paper, an efficient multi-scale local region based level set method is proposed to segment the image with intensity inhomogeneity, which is based on the multi-scale segmentation and statistical analysis for intensities of local region. Firstly, the local region is defined in circular shape for capturing more local intensity information. The statistical analysis can be performed on intensities of local circular regions centered in each pixel by using multi-scale low-pass filtering. Then, the data term of level set energy functional can be constructed by approximating the normalized weighted image divided by multi-scale local intensity information in a piecewise constant way. In addition, the regularization term is built to control the smoothness of evolving curve and avoid the over-segmentation phenomenon and re-Initialization Step. Finally, the multi-scale segmentation is performed by minimizing the total level set energy functional by using the finite difference scheme. The experiments on synthetic and real images with slight or severe intensity inhomogeneity can demonstrate the efficiency and robustness of the proposed method. In addition, the comparisons with the recently popular local binary fitting (LBF) model and local Chan-Vese (LCV) model also show that our method has obvious superiority over the traditional local region based methods.
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a novel level set method for image segmentation by incorporating local statistical analysis and global similarity measurement
Pattern Recognition, 2015Co-Authors: Xiaofeng Wang, Yigang ZhangAbstract:This paper presents a novel level set method for complex image segmentation, where the local statistical analysis and global similarity measurement are both incorporated into the construction of energy functional. The intensity statistical analysis is performed on local circular regions centered in each pixel so that the local energy term is constructed in a piecewise constant way. Meanwhile, the Bhattacharyya coefficient is utilized to measure the similarity between probability distribution functions for intensities inside and outside the evolving contour. The global energy term can be formulated by minimizing the Bhattacharyya coefficient. To avoid the time-consuming re-Initialization Step, the penalty energy term associated with a new double-well potential is constructed to maintain the signed distance property of level set function. The experiments and comparisons with four popular models on synthetic and real images have demonstrated that our method is efficient and robust for segmenting noisy images, images with intensity inhomogeneity, texture images and multiphase images. The intensity statistical analysis is performed on local circular regions.The global energy term is formulated by minimizing the Bhattacharyya coefficient.The penalty energy term associated with a new double-well potential is proposed.The proposed method is efficient and robust for segmenting complex images.
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an efficient local chan vese model for image segmentation
Pattern Recognition, 2010Co-Authors: Xiaofeng Wang, Deshuang HuangAbstract:In this paper, a new local Chan-Vese (LCV) model is proposed for image segmentation, which is built based on the techniques of curve evolution, local statistical function and level set method. The energy functional for the proposed model consists of three terms, i.e., global term, local term and regularization term. By incorporating the local image information into the proposed model, the images with intensity inhomogeneity can be efficiently segmented. In addition, the time-consuming re-Initialization Step widely adopted in traditional level set methods can be avoided by introducing a new penalizing energy. To avoid the long iteration process for level set evolution, an efficient termination criterion is presented which is based on the length change of evolving curve. Particularly, we proposed constructing an extended structure tensor (EST) by adding the intensity information into the classical structure tensor for texture image segmentation. It can be found that by combining the EST with our LCV model, the texture image can be efficiently segmented no matter whether it presents intensity inhomogeneity or not. Finally, experiments on some synthetic and real images have demonstrated the efficiency and robustness of our model. Moreover, comparisons with the well-known Chan-Vese (CV) model and recent popular local binary fitting (LBF) model also show that our LCV model can segment images with few iteration times and be less sensitive to the location of initial contour and the selection of governing parameters.
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a level set based segmentation method for images with intensity inhomogeneity
International Conference on Intelligent Computing, 2009Co-Authors: Xiaofeng Wang, Hai MinAbstract:In this paper, an efficient hybrid level set (HLS) model is proposed for segmenting the images with intensity inhomogeneity, which is a difficult problem for traditional region-based level set methods. The total energy functional for the proposed model consists of three terms, i.e., global term, local term and regularization term. By incorporating the local image information into the proposed model, the images with intensity inhomogeneity can be efficiently segmented. In addition, the time-consuming re-Initialization Step widely adopted in traditional level set methods can be avoided by introducing a penalizing energy. Finally, experiments on some synthetic and real images have demonstrated the efficiency and robustness of the proposed model.
Yigang Zhang - One of the best experts on this subject based on the ideXlab platform.
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multi scale local region based level set method for image segmentation in the presence of intensity inhomogeneity
Neurocomputing, 2015Co-Authors: Xiaofeng Wang, Yigang ZhangAbstract:Abstract Intensity inhomogeneity arising from the imperfect image acquisition process is a major challenge for image segmentation. Most of widely used image segmentation methods usually fail to segment the image with intensity inhomogeneity due to the assumption of intensity homogeneity. In this paper, an efficient multi-scale local region based level set method is proposed to segment the image with intensity inhomogeneity, which is based on the multi-scale segmentation and statistical analysis for intensities of local region. Firstly, the local region is defined in circular shape for capturing more local intensity information. The statistical analysis can be performed on intensities of local circular regions centered in each pixel by using multi-scale low-pass filtering. Then, the data term of level set energy functional can be constructed by approximating the normalized weighted image divided by multi-scale local intensity information in a piecewise constant way. In addition, the regularization term is built to control the smoothness of evolving curve and avoid the over-segmentation phenomenon and re-Initialization Step. Finally, the multi-scale segmentation is performed by minimizing the total level set energy functional by using the finite difference scheme. The experiments on synthetic and real images with slight or severe intensity inhomogeneity can demonstrate the efficiency and robustness of the proposed method. In addition, the comparisons with the recently popular local binary fitting (LBF) model and local Chan-Vese (LCV) model also show that our method has obvious superiority over the traditional local region based methods.
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a novel level set method for image segmentation by incorporating local statistical analysis and global similarity measurement
Pattern Recognition, 2015Co-Authors: Xiaofeng Wang, Yigang ZhangAbstract:This paper presents a novel level set method for complex image segmentation, where the local statistical analysis and global similarity measurement are both incorporated into the construction of energy functional. The intensity statistical analysis is performed on local circular regions centered in each pixel so that the local energy term is constructed in a piecewise constant way. Meanwhile, the Bhattacharyya coefficient is utilized to measure the similarity between probability distribution functions for intensities inside and outside the evolving contour. The global energy term can be formulated by minimizing the Bhattacharyya coefficient. To avoid the time-consuming re-Initialization Step, the penalty energy term associated with a new double-well potential is constructed to maintain the signed distance property of level set function. The experiments and comparisons with four popular models on synthetic and real images have demonstrated that our method is efficient and robust for segmenting noisy images, images with intensity inhomogeneity, texture images and multiphase images. The intensity statistical analysis is performed on local circular regions.The global energy term is formulated by minimizing the Bhattacharyya coefficient.The penalty energy term associated with a new double-well potential is proposed.The proposed method is efficient and robust for segmenting complex images.
Xiaodong Wang - One of the best experts on this subject based on the ideXlab platform.
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spectral method for phase retrieval an expectation propagation perspective
arXiv: Information Theory, 2019Co-Authors: Rishabh Dudeja, Arian Maleki, Xiaodong WangAbstract:Phase retrieval refers to the problem of recovering a signal $\mathbf{x}_{\star}\in\mathbb{C}^n$ from its phaseless measurements $y_i=|\mathbf{a}_i^{\mathrm{H}}\mathbf{x}_{\star}|$, where $\{\mathbf{a}_i\}_{i=1}^m$ are the measurement vectors. Many popular phase retrieval algorithms are based on the following two-Step procedure: (i) initialize the algorithm based on a spectral method, (ii) refine the initial estimate by a local search algorithm (e.g., gradient descent). The quality of the spectral Initialization Step can have a major impact on the performance of the overall algorithm. In this paper, we focus on the model where the measurement matrix $\mathbf{A}=[\mathbf{a}_1,\ldots,\mathbf{a}_m]^{\mathrm{H}}$ has orthonormal columns, and study the spectral Initialization under the asymptotic setting $m,n\to\infty$ with $m/n\to\delta\in(1,\infty)$. We use the expectation propagation framework to characterize the performance of spectral Initialization for Haar distributed matrices. Our numerical results confirm that the predictions of the EP method are accurate for not-only Haar distributed matrices, but also for realistic Fourier based models (e.g. the coded diffraction model). The main findings of this paper are the following: (1) There exists a threshold on $\delta$ (denoted as $\delta_{\mathrm{weak}}$) below which the spectral method cannot produce a meaningful estimate. We show that $\delta_{\mathrm{weak}}=2$ for the column-orthonormal model. In contrast, previous results by Mondelli and Montanari show that $\delta_{\mathrm{weak}}=1$ for the i.i.d. Gaussian model. (2) The optimal design for the spectral method coincides with that for the i.i.d. Gaussian model, where the latter was recently introduced by Luo, Alghamdi and Lu.
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em based iterative receiver design with carrier frequency offset estimation for mimo ofdm systems
IEEE Transactions on Communications, 2005Co-Authors: Yong Sun, Zixiang Xiong, Xiaodong WangAbstract:In this letter, we study the design of expectation-maximization (EM)-based iterative receivers for multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing systems with the presence of carrier-frequency offset (CFO). Motivated by the spirit of maximum-likelihood estimation in the EM algorithm, we first present a pilot-aided CFO estimation scheme that allows fast Fourier transform-based fast implementation. Then this CFO estimation is incorporated into the Initialization Step of the iterative receiver. Experimental results show the effectiveness of our receiver design in combating CFO.
Yong Sun - One of the best experts on this subject based on the ideXlab platform.
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em based iterative receiver design with carrier frequency offset estimation for mimo ofdm systems
IEEE Transactions on Communications, 2005Co-Authors: Yong Sun, Zixiang Xiong, Xiaodong WangAbstract:In this letter, we study the design of expectation-maximization (EM)-based iterative receivers for multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing systems with the presence of carrier-frequency offset (CFO). Motivated by the spirit of maximum-likelihood estimation in the EM algorithm, we first present a pilot-aided CFO estimation scheme that allows fast Fourier transform-based fast implementation. Then this CFO estimation is incorporated into the Initialization Step of the iterative receiver. Experimental results show the effectiveness of our receiver design in combating CFO.
Deshuang Huang - One of the best experts on this subject based on the ideXlab platform.
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an efficient local chan vese model for image segmentation
Pattern Recognition, 2010Co-Authors: Xiaofeng Wang, Deshuang HuangAbstract:In this paper, a new local Chan-Vese (LCV) model is proposed for image segmentation, which is built based on the techniques of curve evolution, local statistical function and level set method. The energy functional for the proposed model consists of three terms, i.e., global term, local term and regularization term. By incorporating the local image information into the proposed model, the images with intensity inhomogeneity can be efficiently segmented. In addition, the time-consuming re-Initialization Step widely adopted in traditional level set methods can be avoided by introducing a new penalizing energy. To avoid the long iteration process for level set evolution, an efficient termination criterion is presented which is based on the length change of evolving curve. Particularly, we proposed constructing an extended structure tensor (EST) by adding the intensity information into the classical structure tensor for texture image segmentation. It can be found that by combining the EST with our LCV model, the texture image can be efficiently segmented no matter whether it presents intensity inhomogeneity or not. Finally, experiments on some synthetic and real images have demonstrated the efficiency and robustness of our model. Moreover, comparisons with the well-known Chan-Vese (CV) model and recent popular local binary fitting (LBF) model also show that our LCV model can segment images with few iteration times and be less sensitive to the location of initial contour and the selection of governing parameters.