The Experts below are selected from a list of 75366 Experts worldwide ranked by ideXlab platform
Aggelos K Katsaggelos - One of the best experts on this subject based on the ideXlab platform.
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EUSIPCO - Total variation blind deconvolution using a variational approach to Parameter, Image, and blur estimation
2007Co-Authors: S. Derin Babacan, Rafael Molina, Aggelos K KatsaggelosAbstract:In this paper we propose novel algorithms for total variation (TV) based blind deconvolution and Parameter estimation utilizing a variational framework. Within a hierarchical Bayesian formulation, the reconstructed Image, the blur and the unknown hyperParameters for the Image prior, the blur prior and the Image degradation noise are simultaneously estimated. We develop two algorithms resulting from this formulation which provide approximations to the posterior distributions of the latent variables. Different values can be drawn from these distributions as estimates to the latent variables and the uncertainty of these estimates can be measured. Experimental results are provided to demonstrate the performance of the algorithms.
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blind deconvolution using a variational approach to Parameter Image and blur estimation
IEEE Transactions on Image Processing, 2006Co-Authors: Rafael Molina, Javier Mateos, Aggelos K KatsaggelosAbstract:Following the hierarchical Bayesian framework for blind deconvolution problems, in this paper, we propose the use of simultaneous autoregressions as prior distributions for both the Image and blur, and gamma distributions for the unknown Parameters (hyperParameters) of the priors and the Image formation noise. We show how the gamma distributions on the unknown hyperParameters can be used to prevent the proposed blind deconvolution method from converging to undesirable Image and blur estimates and also how these distributions can be inferred in realistic situations. We apply variational methods to approximate the posterior probability of the unknown Image, blur, and hyperParameters and propose two different approximations of the posterior distribution. One of these approximations coincides with a classical blind deconvolution method. The proposed algorithms are tested experimentally and compared with existing blind deconvolution methods
Rafael Molina - One of the best experts on this subject based on the ideXlab platform.
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EUSIPCO - Total variation blind deconvolution using a variational approach to Parameter, Image, and blur estimation
2007Co-Authors: S. Derin Babacan, Rafael Molina, Aggelos K KatsaggelosAbstract:In this paper we propose novel algorithms for total variation (TV) based blind deconvolution and Parameter estimation utilizing a variational framework. Within a hierarchical Bayesian formulation, the reconstructed Image, the blur and the unknown hyperParameters for the Image prior, the blur prior and the Image degradation noise are simultaneously estimated. We develop two algorithms resulting from this formulation which provide approximations to the posterior distributions of the latent variables. Different values can be drawn from these distributions as estimates to the latent variables and the uncertainty of these estimates can be measured. Experimental results are provided to demonstrate the performance of the algorithms.
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blind deconvolution using a variational approach to Parameter Image and blur estimation
IEEE Transactions on Image Processing, 2006Co-Authors: Rafael Molina, Javier Mateos, Aggelos K KatsaggelosAbstract:Following the hierarchical Bayesian framework for blind deconvolution problems, in this paper, we propose the use of simultaneous autoregressions as prior distributions for both the Image and blur, and gamma distributions for the unknown Parameters (hyperParameters) of the priors and the Image formation noise. We show how the gamma distributions on the unknown hyperParameters can be used to prevent the proposed blind deconvolution method from converging to undesirable Image and blur estimates and also how these distributions can be inferred in realistic situations. We apply variational methods to approximate the posterior probability of the unknown Image, blur, and hyperParameters and propose two different approximations of the posterior distribution. One of these approximations coincides with a classical blind deconvolution method. The proposed algorithms are tested experimentally and compared with existing blind deconvolution methods
Javier Mateos - One of the best experts on this subject based on the ideXlab platform.
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blind deconvolution using a variational approach to Parameter Image and blur estimation
IEEE Transactions on Image Processing, 2006Co-Authors: Rafael Molina, Javier Mateos, Aggelos K KatsaggelosAbstract:Following the hierarchical Bayesian framework for blind deconvolution problems, in this paper, we propose the use of simultaneous autoregressions as prior distributions for both the Image and blur, and gamma distributions for the unknown Parameters (hyperParameters) of the priors and the Image formation noise. We show how the gamma distributions on the unknown hyperParameters can be used to prevent the proposed blind deconvolution method from converging to undesirable Image and blur estimates and also how these distributions can be inferred in realistic situations. We apply variational methods to approximate the posterior probability of the unknown Image, blur, and hyperParameters and propose two different approximations of the posterior distribution. One of these approximations coincides with a classical blind deconvolution method. The proposed algorithms are tested experimentally and compared with existing blind deconvolution methods
Qiang Chen - One of the best experts on this subject based on the ideXlab platform.
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Image recognition technology in rotating machinery fault diagnosis based on artificial immune
Smart Structures and Systems, 2010Co-Authors: Yanping Feng, Qiang ChenAbstract:By using Image recognition technology, this paper presents a new fault diagnosis method for rotating machinery with artificial immune algorithm. This method focuses on the vibration state Parameter Image. The main contribution of this paper is as follows: firstly, 3-D spectrum is created with raw vibrating signals. Secondly, feature information in the state Parameter Image of rotating machinery is extracted by using Wavelet Packet transformation. Finally, artificial immune algorithm is adopted to diagnose rotating machinery fault. On the modeling of 600MW turbine experimental bench, rotor`s normal rate, fault of unbalance, misalignment and bearing pedestal looseness are being examined. It`s demonstrated from the diagnosis example of rotating machinery that the proposed method can improve the accuracy rate and diagnosis system robust quality effectively.
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Image recognition technology in rotating machinery faultdiagnosis based on artificial immune
Smart Structures and Systems, 2010Co-Authors: Dachang Zhu, Yanping Feng, Qiang Chen, Jinbao CaiAbstract:By using Image recognition technology, this paper presents a new fault diagnosis method for rotating machinery with artificial immune algorithm. This method focuses on the vibration state Parameter Image. The main contribution of this paper is as follows: firstly, 3-D spectrum is created with raw vibrating signals. Secondly, feature information in the state Parameter Image of rotating machinery is extracted by using Wavelet Packet transformation. Finally, artificial immune algorithm is adopted to diagnose rotating machinery fault. On the modeling of 600MW turbine experimental bench, rotor`s normal rate, fault of unbalance, misalignment and bearing pedestal looseness are being examined. It`s demonstrated from the diagnosis example of rotating machinery that the proposed method can improve the accuracy rate and diagnosis system robust quality effectively.
Frederic L. Lizzi - One of the best experts on this subject based on the ideXlab platform.
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Noninvasive in vivo detection of prognostic indicators for high-risk uveal melanoma: ultrasound Parameter imaging.
Ophthalmology, 2004Co-Authors: D. Jackson Coleman, Frederic L. Lizzi, Ronald H. Silverman, Mark J. Rondeau, H. Culver Boldt, Harriet O. Lloyd, Thomas A. Weingeist, Xue Chen, Sumalee Vangveeravong, Robert FolbergAbstract:Abstract Purpose Primary malignant melanoma of the choroid and ciliary body has traditionally been treated without histologic staging, using purely clinical indicators. The presence of extravascular matrix patterns (EMP) in histologic sections of uveal melanoma has been shown to be an independent indicator of metastatic risk. These patterns are of a dimension and physical composition that are likely to be detected with ultrasound backscatter analysis. Our aim was to determine whether ultrasound Parameter imaging could detect the presence of EMP at a diagnostically significant level for treatment staging and for planning investigational studies of therapeutic modalities. Design Prospective, masked ultrasound–pathologic correlative study. Participants One hundred seventeen patients diagnosed with previously untreated choroidal melanoma were scanned within 2 weeks before enucleation. Methods Tumors were evaluated histologically and divided into high-risk and low-risk groups on the basis of the presence of 2% or more histologic cross-sectional area composed of EMP patterns. Digital ultrasound data were processed to generate Parameter Images representing the size and concentration of ultrasound scatterers. Histologic and ultrasound Images and data were correlated, and linear and nonlinear statistical methods were used to create multivariate models for noninvasive differentiation of high-risk and low-risk tumors. Main outcome measures Presence or absence of high-risk EMP and associated ultrasound Parameter classification models. Results Of the 117 tumors, 69 were classified as low risk, and 48 were classified as high-risk with histologic analysis. A classification that used ultrasound Parameter Image features with linear discriminant analysis could correctly identify 79.5% of cases retrospectively and 75.2% of cases by use of cross-validation, an estimate of prospective classification ability. By use of a more powerful classification technique (support vector machine), 93.1% of cases were correctly classified retrospectively. With a cross-validation procedure, 80.10% of cases were correctly classified. Conclusions Ultrasound can be used noninvasively to classify tumors into high-risk and low-risk groups by detecting the presence of EMP patterns. By the use of previous studies that compared the histologic presence of EMP patterns with patient survival, estimates of hazard rates associated with ultrasound risk groups can be made. The noninvasive ultrasound classification is potentially useful as a prognostic variable and as a tool for stratification of patient populations for tumor treatment evaluation.
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Statistical framework for ultrasonic spectral Parameter imaging
Ultrasound in medicine & biology, 1997Co-Authors: Frederic L. Lizzi, Michael Astor, Ernest J. Feleppa, Mary Shao, Andrew KaliszAbstract:This study examines the statistics of ultrasonic spectral Parameter Images that are being used to evaluate tissue microstructure in several organs. The Parameters are derived from sliding-window spectrum analysis of radiofrequency echo signals. Calibrated spectra are expressed in dB and analyzed with linear regression procedures to compute spectral slope, intercept and midband fit, which is directly related to integrated backscatter. Local values of each Parameter are quantitatively depicted in gray-scale cross-sectional Images to determine tissue type, response to therapy and physical scatterer properties. In this report, we treat the statistics of each type of Parameter Image for statistically homogeneous scatterers. Probability density functions are derived for each Parameter, and theoretical results are compared with corresponding histograms clinically measured in homogeneous tissue segments in the liver and prostate. Excellent agreement was found between theoretical density functions and data histograms for homogeneous tissue segments. Departures from theory are observed in heterogeneous tissue segments. The results demonstrate how the statistics of each spectral Parameter and integrated backscatter are related to system and analysis Parameters. These results are now being used to guide the design of system and analysis Parameters, to improve assays of tissue heterogeneity and to evaluate the precision of estimating features associated with effective scatterer sizes and concentrations.