The Experts below are selected from a list of 30261 Experts worldwide ranked by ideXlab platform
Mark R Pickering - One of the best experts on this subject based on the ideXlab platform.
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a non rigid 3d multi modal registration algorithm using partial volume interpolation and the sum of Conditional Variance
Digital Image Computing: Techniques and Applications, 2014Co-Authors: Mst Nargis Aktar, Md Jahangir Alam, Mark R PickeringAbstract:Multi-modal medical image registration provides complementary information from the fusion of various medical imaging modalities. This paper presents a volume based multi-modal affine registration algorithm to register images acquired using different magnetic resonance imaging (MRI) modes. In the proposed algorithm, the sum-of-Conditional Variance (SCV) similarity measure is used. The SCV is considered to be a state-of-the- art similarity measure for registering multi-modal images. However, the main drawback of the SCV is that it uses only quantized information to calculate a joint histogram. To overcome this limitation, we propose to use partial volume interpolation (PVI) in the joint histogram calculation to improve the performance of the existing registration algorithm. To evaluate the performance of the registration algorithm, different similarity measures were compared in conjunction with gradient-based Gauss-Newton(GN) optimization to optimize the spatial transformation parameters. The experimental evaluation shows that the proposed approach provides a higher success rate and comparable accuracy to other methods that have been recently proposed for multi-modal medical image registration.
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robust 3d multi modal registration of mri volumes using the sum of Conditional Variance
Digital Image Computing: Techniques and Applications, 2013Co-Authors: Nargis Aktar, Jahangir Alam, Andrew Lambert, Mark R PickeringAbstract:Multi-modal registration is a fundamental step for many medical imaging procedures. In this paper, the sum of Conditional Variance (SCV) similarity measure is proposed for 3D multi-modal medical image registration. The SCV similarity measure is based on minimizing the sum of Conditional Variances that are calculated using the joint histogram of the two images to be registered. Standard Gauss-Newton optimization is used to automatically minimize this measure which allows fast computational time and high accuracy. Experimental results show that our proposed approach is robust, computationally efficient and also more accurate when compared with the standard mutual information (MI) based approach and also the recently proposed sum-of-squared-difference on entropy images (eSSD) approach.
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DICTA - Robust 3D Multi-Modal Registration of MRI Volumes Using the Sum of Conditional Variance
2013 International Conference on Digital Image Computing: Techniques and Applications (DICTA), 2013Co-Authors: Nargis Aktar, Jahangir Alam, Andrew Lambert, Mark R PickeringAbstract:Multi-modal registration is a fundamental step for many medical imaging procedures. In this paper, the sum of Conditional Variance (SCV) similarity measure is proposed for 3D multi-modal medical image registration. The SCV similarity measure is based on minimizing the sum of Conditional Variances that are calculated using the joint histogram of the two images to be registered. Standard Gauss-Newton optimization is used to automatically minimize this measure which allows fast computational time and high accuracy. Experimental results show that our proposed approach is robust, computationally efficient and also more accurate when compared with the standard mutual information (MI) based approach and also the recently proposed sum-of-squared-difference on entropy images (eSSD) approach.
R W Bilger - One of the best experts on this subject based on the ideXlab platform.
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Conditional Variance equation and its analysis
Symposium (International) on Combustion, 1998Co-Authors: N Swaminathan, R W BilgerAbstract:The Conditional moment closure method, found to perform well for reacting flow predictions, needs a second-order closure when there are extinction and ignition processes occurring in the flow. The secondorder contributions include the effect of the Conditional Variance of reactive species fluctuations. An evolution equation for the Conditional Variance is derived and is studied using a direct numerical simulation (DNS) database involving no global extinction or ignition. It is found that turbulent dissipative, diffusive, and chemically reactive processes balance the Conditional Variance production by scalar dissipation-scalar fluctuations correlation. The Damkohler number is found to play a role in the behavior of the abovementioned physical processes. The evolution of the Conditional Variance is observed to be a quasi-steady process although the bulk of the flow is decaying.
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measurement and prediction of the Conditional Variance in a turbulent reactive scalar mixing layer
Physics of Fluids, 1993Co-Authors: R W BilgerAbstract:Equations for the second‐order Conditional moments of reactive scalars are derived. For one‐step reaction with equal diffusivity these can be reduced to a single equation for the Conditional Variance, which is the same for all reactive species. The various terms in the equation have been modeled in light of the experimental data for a turbulent reactive‐scalar mixing layer. In comparison with conventional second‐order moment closure the Conditional means do not vary with the cross‐stream position, the turbulent flux in the cross‐stream direction is negligible while the source term needs special attention. Experimental results of the Conditional Variance at different points across the mixing layer and at various streamwise locations are presented. At each streamwise location the data in general collapse onto each other although the scatter is large. The modeled equations have been solved numerically and are compared with the experimental data. The prediction generally agrees with the data. Two models have been used for the Conditional conserved scalar dissipation. One model assumes that the Conditional scalar dissipation equals the unConditional one and the other is derived from a mapping closure. Both models give almost identical results for the Conditional means and only a small difference for the Conditional Variance.
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Measurement and prediction of the Conditional Variance in a turbulent reactive‐scalar mixing layer
Physics of Fluids A: Fluid Dynamics, 1993Co-Authors: R W BilgerAbstract:Equations for the second‐order Conditional moments of reactive scalars are derived. For one‐step reaction with equal diffusivity these can be reduced to a single equation for the Conditional Variance, which is the same for all reactive species. The various terms in the equation have been modeled in light of the experimental data for a turbulent reactive‐scalar mixing layer. In comparison with conventional second‐order moment closure the Conditional means do not vary with the cross‐stream position, the turbulent flux in the cross‐stream direction is negligible while the source term needs special attention. Experimental results of the Conditional Variance at different points across the mixing layer and at various streamwise locations are presented. At each streamwise location the data in general collapse onto each other although the scatter is large. The modeled equations have been solved numerically and are compared with the experimental data. The prediction generally agrees with the data. Two models have been used for the Conditional conserved scalar dissipation. One model assumes that the Conditional scalar dissipation equals the unConditional one and the other is derived from a mapping closure. Both models give almost identical results for the Conditional means and only a small difference for the Conditional Variance.
Eric Marchand - One of the best experts on this subject based on the ideXlab platform.
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Visual Servoing using the Sum of Conditional Variance
2012Co-Authors: Bertrand Delabarre, Eric MarchandAbstract:In this paper we propose a new way to achieve direct visual servoing. The novelty is the use of the sum of Conditional Variance to realize the optimization process of a positioning task. This measure, which has previously been used successfully in the case of visual tracking, has been shown to be invariant to non-linear illumination variations and inexpensive to compute. Compared to other direct approaches of visual servoing, it is a good compromise between techniques using the illumination of pixels which are computationally inexpensive but non robust to illumination variations and other approaches using the mutual information which are more complicated to compute but offer more robustness towards the variations of the scene. This method results in a direct visual servoing task easy and fast to compute and robust towards non-linear illumination variations. This paper describes a visual servoing task based on the sum of Conditional Variance performed using a Levenberg-Marquardt optimization process. The results are then demonstrated through experimental validations and compared to both photometric-based and entropy-based techniques.
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IROS - Visual servoing using the sum of Conditional Variance
2012 IEEE RSJ International Conference on Intelligent Robots and Systems, 2012Co-Authors: Bertrand Delabarre, Eric MarchandAbstract:In this paper we propose a new way to achieve direct visual servoing. The novelty is the use of the sum of Conditional Variance to realize the optimization process of a positioning task. This measure, which has previously been used successfully in the case of visual tracking, has been shown to be invariant to non-linear illumination variations and inexpensive to compute. Compared to other direct approaches of visual servoing, it is a good compromise between techniques using the illumination of pixels which are computationally inexpensive but non robust to illumination variations and other approaches using the mutual information which are more complicated to compute but offer more robustness towards the variations of the scene. This method results in a direct visual servoing task easy and fast to compute and robust towards non-linear illumination variations. This paper describes a visual servoing task based on the sum of Conditional Variance performed using a Levenberg-Marquardt optimization process. The results are then demonstrated through experimental validations and compared to both photometric-based and entropy-based techniques.
Fateh Chebana - One of the best experts on this subject based on the ideXlab platform.
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a simultaneous test for Conditional mean and Conditional Variance functions in time series models with martingale difference innovations
Statistical Methodology, 2011Co-Authors: Naâmane Laib, Fateh ChebanaAbstract:Abstract We consider here, in contrast to classical time series models where innovations are assumed to be independent and identically distributed (iid), a class of nonlinear semi-parametric models in which the innovations are stationary ergodic Conditionally martingale differences. We establish the local asymptotic normality associated with these models. From this result, an efficient simultaneous locally asymptotic test is derived for testing the Conditional mean and the Conditional Variance functions without a specified error law. The main result shows that the test statistic built by substituting consistent estimated residuals and parameters for theoretical ones is asymptotically normal. Its asymptotic power is also obtained under local alternatives. The performances of the proposed test are illustrated by means of some simulations.
Gregory D Hager - One of the best experts on this subject based on the ideXlab platform.
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visual tracking using the sum of Conditional Variance
Intelligent Robots and Systems, 2011Co-Authors: Rogerio Richa, Raphael Sznitman, Russell H Taylor, Gregory D HagerAbstract:The goal of this paper is to introduce a direct visual tracking method based on an image similarity measure called the sum of Conditional Variance (SCV). The SCV was originally proposed in the medical imaging domain for registering multi-modal images. In the context of visual tracking, the SCV is invariant to non-linear illumination variations, multi-modal and computationally inexpensive. Compared to information theoretic tracking methods, it requires less iterations to converge and has a significantly larger convergence radius. The novelty in this paper is a generalization of the efficient second-order minimization formulation for tracking using the SCV, allowing us to combine the efficient second-order approximation of the Hessian with a similarity metric invariant to non-linear illumination variations. The result is a visual tracking method that copes with non-linear illumination variations without requiring the estimation of photometric correction parameters at every iteration. We demonstrate the superior performance of the proposed method through comparative studies and tracking experiments under challenging illumination conditions and rapid motions.
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IROS - Visual tracking using the sum of Conditional Variance
2011 IEEE RSJ International Conference on Intelligent Robots and Systems, 2011Co-Authors: Rogerio Richa, Raphael Sznitman, Russell H Taylor, Gregory D HagerAbstract:The goal of this paper is to introduce a direct visual tracking method based on an image similarity measure called the sum of Conditional Variance (SCV). The SCV was originally proposed in the medical imaging domain for registering multi-modal images. In the context of visual tracking, the SCV is invariant to non-linear illumination variations, multi-modal and computationally inexpensive. Compared to information theoretic tracking methods, it requires less iterations to converge and has a significantly larger convergence radius. The novelty in this paper is a generalization of the efficient second-order minimization formulation for tracking using the SCV, allowing us to combine the efficient second-order approximation of the Hessian with a similarity metric invariant to non-linear illumination variations. The result is a visual tracking method that copes with non-linear illumination variations without requiring the estimation of photometric correction parameters at every iteration. We demonstrate the superior performance of the proposed method through comparative studies and tracking experiments under challenging illumination conditions and rapid motions.
