The Experts below are selected from a list of 312 Experts worldwide ranked by ideXlab platform
Nasser Kehtarnavaz - One of the best experts on this subject based on the ideXlab platform.
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Computationally Tractable Stochastic Image Modeling Based on Symmetric Markov Mesh Random Fields
IEEE Transactions on Image Processing, 2013Co-Authors: Siamak Yousefi, Nasser KehtarnavazAbstract:In this paper, the properties of a new class of causal Markov random fields, named symmetric Markov mesh random field, are initially discussed. It is shown that the symmetric Markov mesh random fields from the upper corners are equivalent to the symmetric Markov mesh random fields from the lower corners. Based on this new random field, a symmetric, corner-independent, and isotropic Image model is then derived which incorporates the dependency of a pixel on all its neighbors. The introduced Image model comprises the product of several local 1D density and 2D joint density functions of pixels in an Image thus making it computationally tractable and practically feasible by allowing the use of histogram and joint histogram approximations to estimate the model parameters. An Image restoration application is also presented to confirm the effectiveness of the model developed. The experimental results demonstrate that this new model provides an improved tool for Image Modeling purposes compared to the conventional Markov random field models.
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A new stochastic Image model based on Markov random fields and its application to texture Modeling
2011 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2011Co-Authors: Siamak Yousefi, Nasser KehtarnavazAbstract:Stochastic Image Modeling based on conventional Markov random fields is extensively discussed in the literature. A new stochastic Image model based on Markov random fields is introduced in this paper which overcomes the shortcomings of the conventional models easing the computation of the joint density function of Images. As an application, this model is used to generate texture patterns. The lower computational complexity and easily controllable parameters of the model makes it a more useful model as compared to the conventional Markov random field-based models.
Siamak Yousefi - One of the best experts on this subject based on the ideXlab platform.
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Computationally Tractable Stochastic Image Modeling Based on Symmetric Markov Mesh Random Fields
IEEE Transactions on Image Processing, 2013Co-Authors: Siamak Yousefi, Nasser KehtarnavazAbstract:In this paper, the properties of a new class of causal Markov random fields, named symmetric Markov mesh random field, are initially discussed. It is shown that the symmetric Markov mesh random fields from the upper corners are equivalent to the symmetric Markov mesh random fields from the lower corners. Based on this new random field, a symmetric, corner-independent, and isotropic Image model is then derived which incorporates the dependency of a pixel on all its neighbors. The introduced Image model comprises the product of several local 1D density and 2D joint density functions of pixels in an Image thus making it computationally tractable and practically feasible by allowing the use of histogram and joint histogram approximations to estimate the model parameters. An Image restoration application is also presented to confirm the effectiveness of the model developed. The experimental results demonstrate that this new model provides an improved tool for Image Modeling purposes compared to the conventional Markov random field models.
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A new stochastic Image model based on Markov random fields and its application to texture Modeling
2011 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2011Co-Authors: Siamak Yousefi, Nasser KehtarnavazAbstract:Stochastic Image Modeling based on conventional Markov random fields is extensively discussed in the literature. A new stochastic Image model based on Markov random fields is introduced in this paper which overcomes the shortcomings of the conventional models easing the computation of the joint density function of Images. As an application, this model is used to generate texture patterns. The lower computational complexity and easily controllable parameters of the model makes it a more useful model as compared to the conventional Markov random field-based models.
R.g. Baraniuk - One of the best experts on this subject based on the ideXlab platform.
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Bayesian tree-structured Image Modeling using wavelet-domain hidden Markov models
IEEE Transactions on Image Processing, 2001Co-Authors: J. Romberg, Hyeokho Choi, R.g. BaraniukAbstract:Wavelet-domain hidden Markov models have proven to be useful tools for statistical signal and Image processing. The hidden Markov tree (HMT) model captures the key features of the joint probability density of the wavelet coefficients of real-world data. One potential drawback to the HMT framework is the need for computationally expensive iterative training to fit an HMT model to a given data set (e.g., using the expectation-maximization algorithm). We greatly simplify the HMT model by exploiting the inherent self-similarity of real-world Images. The simplified model specifies the HMT parameters with just nine meta-parameters (independent of the size of the Image and the number of wavelet scales). We also introduce a Bayesian universal HMT (uHMT) that fixes these nine parameters. The uHMT requires no training of any kind, while extremely simple, we show using a series of Image estimation/denoising experiments that these new models retain nearly all of the key Image structure modeled by the full HMT. Finally, we propose a fast shift-invariant HMT estimation algorithm that outperforms other wavelet-based estimators in the current literature, both visually and in mean square error.
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Bayesian tree-structured Image Modeling
4th IEEE Southwest Symposium on Image Analysis and Interpretation, 2000Co-Authors: J. Romberg, Hyeokho Choi, R.g. BaraniukAbstract:Wavelet-domain hidden Markov models have proven to be useful tools for statistical signal and Image processing. The hidden Markov tree (HMT) model captures the key features of the joint statistics of the wavelet coefficients of real-world data. One potential drawback to the HMT framework is the need for computationally expensive iterative training (using the EM algorithm, for example). In this paper, we propose two reduced-parameter HMT models that capture the general structure of a broad class of grayscale Images. The Image HMT (iHMT) model leverages the fact that for a large class of Images the structure of the HMT is self-similar across scale. This allows us to reduce the complexity of the iHMT to just nine easily trained parameters (independent of the size of the Image and the number of wavelet scales). In the universal HMT (uHMT) we take a Bayesian approach and fix these nine parameters. The uHMT requires no training of any kind. While simple, we show using a series of Image estimation/denoising experiments that these two new models retain nearly all of the key structures modeled by the full HMT. Based on these new models, we develop a shift-invariant wavelet denoising scheme that outperforms all algorithms in the current literature.
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bayesian tree structured Image Modeling using wavelet domain hidden markov models
Proceedings of the 1999 Mathematical Modeling Bayesian Estimation and Inverse Problems, 1999Co-Authors: J. Romberg, Hyeokho Choi, R.g. BaraniukAbstract:ABSTRACT Wavelet-domain hidden Markov models have proven to be useful tools for statistical signal and Image processing. The hiddenMarkov tree (HMT) model captures the key features of the joint density of the wavelet coefficients of real-world data. Onepotential drawback to the HMT framework is the need for cornputationallv expensive iterative training (using the Expectation-Iaximization algorithm, for example). In this paper, we propose two reduced-parameter HMT models that capture the generalstructure of a broad class of real-world Images. In the Image HMT (iHMT) model we use the fact that for a large class ofImages the structure of the HMT is self-similar across scale. This allows us to reduce the complexity of the iHMT to justnine easily trained parameters (independent of the size of the Image and the number of wavelet scales). In the universal HMT(uHMT) we take a Bayesian approach and fix these nine parameters. The uHMT requires no training of any kind. Whilesimple, we show using a series of Image estimation/denoising experiments that these two new models retain nearly all of thekey structure modeled by the full HvIT. Finally, we propose a fast shift-invariant HMT estimation algorithm that outperformsall other wavelet-based estimators in the current literature. both in mean-square error and visual metrics.
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Bayesian wavelet-domain Image Modeling using hidden Markov trees
Proceedings 1999 International Conference on Image Processing (Cat. 99CH36348), 1999Co-Authors: J. Romberg, Hyeokho Choi, R.g. BaraniukAbstract:Wavelet-domain hidden Markov models have proven to be useful tools for statistical signal and Image processing. The hidden Markov tree (HMT) model captures the key features of the joint statistics of the wavelet coefficients of real-world data. One potential drawback to the HMT framework is the need for computationally expensive iterative training (using the EM algorithm, for example). In this paper, we propose two reduced-parameter HMT models that capture the general structure of a broad class of grayscale Images. The Image HMT (iHMT) model leverages the fact that for a large class of Images the structure of the HMT is self-similar across scale. This allows us to reduce the complexity of the iHMT to just nine easily trained parameters (independent of the size of the Image and the number of wavelet scales). In the universal HMT (uHMT) we take a Bayesian approach and fix these nine parameters. The uHMT requires no training of any kind. While simple, we show using a series of Image estimation/denoising experiments that these two new models retain nearly all of the key structures modeled by the full HMT. Based on these new models, we develop a shift-invariant wavelet denoising scheme that outperforms all algorithms in the current literature.
Hassan Ahmed - One of the best experts on this subject based on the ideXlab platform.
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Multi-model AAM framework for face Image Modeling
2013 18th International Conference on Digital Signal Processing (DSP), 2013Co-Authors: Muhammad Aurangzeb Khan, Costas Xydeas, Hassan AhmedAbstract:Active Appearance Modeling (AAM) offers acceptable face synthesis performance when applied to person-specific Modeling applications. The aim of the work presented in this paper is to enable AAM to model and synthesize more accurately previously unseen face Images. Thus a clustering process based on shape similarities is incorporated in the system and applied prior to conventional AAM Modeling, to yield Multi-Model AAM. In this approach the wide appearance spectrum possible face Images is decomposed into a number of cluster each containing similar shape faces. This allows AAM Modeling per cluster to be applied and therefore the generation of several AAM models which capture more accurately variability between possible input faces. Experimental results show that, when dealing with previously unseen faces, models generated through this Multi-Model AAM framework can be significantly more effective in terms of both shape and texture, than the conventional single model AAM approach.
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Multi-Component/Multi-Model AAM framework for face Image Modeling
2013 IEEE International Conference on Acoustics Speech and Signal Processing, 2013Co-Authors: Muhammad Aurangzeb Khan, Costas Xydeas, Hassan AhmedAbstract:An Image face Modeling framework is proposed that aims to enhance the face Modeling capability of the well known Active Appearance Model (AAM). AAM has been used successfully in person-specific related applications but it poses significant limitations when employed in generic face Modeling. Thus this work is focused on the development of new face models which are generic in nature and which accurately fit unseen Image faces, both in terms of shape and texture. For this purpose, Images are decomposed into face related components which are subsequently clustered on the basis of shape similarities. Experimental results show that models generated through this novel framework can be significantly more effective than conventional AAM, in terms of both shape and texture.
Xiaobo Lu - One of the best experts on this subject based on the ideXlab platform.
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A generalized DAMRF Image Modeling for superresolution of license plates
IEEE Transactions on Intelligent Transportation Systems, 2012Co-Authors: Weili Zeng, Xiaobo LuAbstract:In this paper, we propose a novel super-resolution Image reconstruction algorithm to handle license plate texts in real traffic videos. A generalized discontinuity adaptive Markov random field (DAMRF) model is proposed based on the recently reported bilateral filtering, which is not only edge preservation but also robust to noise. Moreover, instead of looking for fixed value for the regularization parameter, a method for automatically estimating it is applied to the proposed model based on the input Images. We use graduated non-convexity (GNC) optimization procedures to minimize the cost function. Results on synthetic and several real traffic sequences are presented, showing the effectiveness of the proposed method and demonstrating its superiority to the conventional DAMRF super-resolution method.
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A Generalized DAMRF Image Modeling for Superresolution of License Plates
IEEE Transactions on Intelligent Transportation Systems, 2012Co-Authors: Weili Zeng, Xiaobo LuAbstract:In this paper, we propose a novel superresolution (SR) reconstruction algorithm to handle license plate texts in real traffic videos. To make license plate numbers more legible, a generalized discontinuity-adaptive Markov random field (DAMRF) model is proposed based on the recently reported bilateral filtering, which not only preserves edges but is robust to noise as well. Moreover, instead of looking for a fixed value for the regularization parameter, a method for automatically estimating it is applied to the proposed model based on the input Images. Information needed to determine the regularization parameter is updated at each iteration step, which is based on the available reconstructed Image. Finally, we use the graduated nonconvexity optimization procedure to minimize the cost function. Results on synthetic and real traffic sequences are presented, which show the effectiveness of the proposed method and demonstrate its superiority to the conventional DAMRF SR method.
