The Experts below are selected from a list of 17655 Experts worldwide ranked by ideXlab platform
Peter M. Atkinson - One of the best experts on this subject based on the ideXlab platform.
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Quantifying the Effect of Registration Error on Spatio-Temporal Fusion
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020Co-Authors: Yijie Tang, Qunming Wang, Ka Zhang, Peter M. AtkinsonAbstract:It is challenging to acquire satellite sensor data with both fine spatial and fine temporal resolution, especially for monitoring at global scales. Among the widely used global monitoring satellite sensors, Landsat data have a coarse temporal resolution, but fine spatial resolution, while moderate resolution imaging spectroradiometer (MODIS) data have fine temporal resolution, but coarse spatial resolution. One solution to this problem is to blend the two types of data using spatio-temporal fusion, creating images with both fine temporal and fine spatial resolution. However, reliable geometric Registration of images acquired by different sensors is a prerequisite of spatio-temporal fusion. Due to the potentially large differences between the spatial resolutions of the images to be fused, the geometric Registration process always contains some degree of uncertainty. This article analyzes quantitatively the influence of geometric Registration Error on spatio-temporal fusion. The relationship between Registration Error and the accuracy of fusion was investigated under the influence of different temporal distances between images, different spatial patterns within the images and using different methods (i.e., spatial and temporal adaptive reflectance fusion model (STARFM), and Fit-FC; two typical spatio-temporal fusion methods). The results show that Registration Error has a significant impact on the accuracy of spatio-temporal fusion; as the Registration Error increased, the accuracy decreased monotonically. The effect of Registration Error in a heterogeneous region was greater than that in a homogeneous region. Moreover, the accuracy of fusion was not dependent on the temporal distance between images to be fused, but rather on their statistical correlation. Finally, the Fit-FC method was found to be more accurate than the STARFM method, under all Registration Error scenarios.
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Investigating the Influence of Registration Errors on the Patch-Based Spatio-Temporal Fusion Method
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020Co-Authors: Liguo Wang, Qunming Wang, Xiaoyi Wang, Peter M. AtkinsonAbstract:Spatio-temporal fusion is a common approach in remote sensing, used to create time-series image data with both fine spatial and temporal resolutions. However, geometric Registration Error, which is a common problem in remote sensing relative to the ground reference, is a particular problem for multiresolution remote sensing data, especially for images with very different spatial resolutions (e.g., Landsat and MODIS images). Registration Error can, thus, have a significant impact on the accuracy of spatio-temporal fusion. To the best of our knowledge, however, almost no effective solutions have been provided to-date to cope with this important issue. This article demonstrates the robustness to Registration Error of the existing SParse representation-based spatio-temporal reflectance fusion model (SPSTFM). Different to conventional methods that are performed on a per-pixel basis, SPSTFM utilizes image patches as the basic unit. We demonstrate theoretically that the effect of Registration Error on patch-based methods is smaller than for pixel-based methods. Experimental results show that SPSTFM is highly robust to Registration Error and is far more accurate under various Registration Errors relative to pixel-based methods. The advantage is shown to be greater for heterogeneous regions than for homogeneous regions, and is large for the fusion of normalized difference vegetation index data. SPSTFM, thus, offers the remote sensing community a crucial tool to overcome one of the longest standing challenges to the effective fusion of remote sensing image time-series.
Purang Abolmaesumi - One of the best experts on this subject based on the ideXlab platform.
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Estimation of Optimal Fiducial Target Registration Error in the Presence of Heteroscedastic Noise
IEEE transactions on medical imaging, 2010Co-Authors: Mehdi Hedjazi Moghari, Randy E Ellis, Purang AbolmaesumiAbstract:We study the effect of point dependent (heteroscedastic) and identically distributed anisotropic fiducial localization noise on fiducial target Registration Error (TRE). We derive an analytic expression, based on the concept of mechanism spatial stiffness, for predicting TRE. The accuracy of the predicted TRE is compared to simulated values where the optimal Registration transformation is computed using the heteroscedastic Errors in variables algorithm. The predicted values are shown to be contained by the 95% confidence intervals of the root mean square TRE obtained from the simulations.
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distribution of target Registration Error for anisotropic and inhomogeneous fiducial localization Error
IEEE Transactions on Medical Imaging, 2009Co-Authors: Mehdi Hedjazi Moghari, Purang AbolmaesumiAbstract:In point-based rigid-body Registration, target Registration Error (TRE) is an important measure of the accuracy of the performed Registration. The Registration's accuracy depends on the fiducial localization Error (FLE) which, in turn, is due to the measurement Errors in the points (fiducials) used to perform the Registration. FLE may have different characteristics and distributions at each point of the registering data sets, and along each orthogonal axis. Previously, the distribution of TRE was estimated based on the assumption that FLE has an independent, identical, and isotropic or anisotropic distribution for each point in the registering data sets. In this article, we present a general solution based on the maximum likelihood (ML) algorithm that estimates the distribution of TRE for the cases where FLE has an independent, identical or inhomogeneous, isotropic or anisotropic, distribution at each point in the registering data sets, and when an algorithm is available that is capable of calculating the optimum Registration to first order. Mathematically, we show that the proposed algorithm simplifies to the one proposed by Fitzpatrick and West when FLE has an independent, identical, and isotropic distribution in the registering data sets. Furthermore, we use numerical simulations to show that the proposed algorithm accurately estimates the distribution of TRE when FLE has an independent, inhomogeneous, and anisotropic distribution in the registering data sets.
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Distribution of Fiducial Registration Error in Rigid-Body Point-Based Registration
IEEE Transactions on Medical Imaging, 2009Co-Authors: Mehdi Hedjazi Moghari, Purang AbolmaesumiAbstract:Rigid-body point-based Registration is frequently used in computer assisted surgery to align corresponding points, or fiducials, in preoperative and intraoperative data. This alignment is mostly achieved by assuming the same homogeneous Error distribution for all the points; however, due to the properties of the medical instruments used in measuring the point coordinates, the Error distribution might be inhomogeneous and different for each point. In this paper, in an effort to understand the effect of Error distribution in the localized points on the performed Registration, we derive a closed-form solution relating the Error distribution of each point with the performed Registration accuracy. The solution uses maximum likelihood estimation to calculate the probability density function of Registration Error at each fiducial point. Extensive numerical simulations are performed to validated the proposed solution.
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a theoretical comparison of different target Registration Error estimators
Medical Image Computing and Computer-Assisted Intervention, 2008Co-Authors: Mehdi Hedjazi Moghari, Purang AbolmaesumiAbstract:Estimation of target Registration Error (TRE), a common measure of the Registration accuracy, is an important issue in computer assisted surgeries. Within the last decade, several new approaches have been developed to estimate either the mean squared value of TRE or the distribution of TRE under different noise conditions. In this paper, we theoretically demonstrate that all the proposed algorithms converge to a general Maximum Likelihood (ML) solution. Numerical simulations are performed to validate our derivations. Using experimentally measured fiducial localization Error, we provide an example of TRE prediction in the presence of anisotropic noise.
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maximum likelihood estimation of the distribution of target Registration Error
Medical Imaging 2008: Visualization Image-Guided Procedures and Modeling, 2008Co-Authors: Mehdi Hedjazi Moghari, Purang AbolmaesumiAbstract:Estimating the alignment accuracy is an important issue in rigid-body point-based Registration algorithms. The Registration accuracy depends on the level of the noise perturbing the registering data sets. The noise in the data sets arises from the fiducial (point) localization Error (FLE) that may have an identical or inhomogeneous, isotropic or anisotropic distribution at each point in each data set. Target Registration Error (TRE) has been defined in the literature, as an Error measure in terms of FLE, to compute the Registration accuracy at a point (target) which is not used in the Registration process. In this paper, we mathematically derive a general solution to approximate the distribution of TRE after Registration of two data sets in the presence of FLE having any type of distribution. The Maximum Likelihood (ML) algorithm is proposed to estimate the Registration parameters and their variances between two data sets. The variances are then used in a closed-form solution, previously presented by these authors, to derive the distribution of TRE at a target location. Based on numerical simulations, it is demonstrated that the derived distribution of TRE, in contrast to the existing methods in the literature, accurately follows the distribution generated by Monte Carlo simulation even when FLE has an inhomogeneous isotropic or anisotropic distribution.
Qunming Wang - One of the best experts on this subject based on the ideXlab platform.
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Quantifying the Effect of Registration Error on Spatio-Temporal Fusion
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020Co-Authors: Yijie Tang, Qunming Wang, Ka Zhang, Peter M. AtkinsonAbstract:It is challenging to acquire satellite sensor data with both fine spatial and fine temporal resolution, especially for monitoring at global scales. Among the widely used global monitoring satellite sensors, Landsat data have a coarse temporal resolution, but fine spatial resolution, while moderate resolution imaging spectroradiometer (MODIS) data have fine temporal resolution, but coarse spatial resolution. One solution to this problem is to blend the two types of data using spatio-temporal fusion, creating images with both fine temporal and fine spatial resolution. However, reliable geometric Registration of images acquired by different sensors is a prerequisite of spatio-temporal fusion. Due to the potentially large differences between the spatial resolutions of the images to be fused, the geometric Registration process always contains some degree of uncertainty. This article analyzes quantitatively the influence of geometric Registration Error on spatio-temporal fusion. The relationship between Registration Error and the accuracy of fusion was investigated under the influence of different temporal distances between images, different spatial patterns within the images and using different methods (i.e., spatial and temporal adaptive reflectance fusion model (STARFM), and Fit-FC; two typical spatio-temporal fusion methods). The results show that Registration Error has a significant impact on the accuracy of spatio-temporal fusion; as the Registration Error increased, the accuracy decreased monotonically. The effect of Registration Error in a heterogeneous region was greater than that in a homogeneous region. Moreover, the accuracy of fusion was not dependent on the temporal distance between images to be fused, but rather on their statistical correlation. Finally, the Fit-FC method was found to be more accurate than the STARFM method, under all Registration Error scenarios.
-
Investigating the Influence of Registration Errors on the Patch-Based Spatio-Temporal Fusion Method
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020Co-Authors: Liguo Wang, Qunming Wang, Xiaoyi Wang, Peter M. AtkinsonAbstract:Spatio-temporal fusion is a common approach in remote sensing, used to create time-series image data with both fine spatial and temporal resolutions. However, geometric Registration Error, which is a common problem in remote sensing relative to the ground reference, is a particular problem for multiresolution remote sensing data, especially for images with very different spatial resolutions (e.g., Landsat and MODIS images). Registration Error can, thus, have a significant impact on the accuracy of spatio-temporal fusion. To the best of our knowledge, however, almost no effective solutions have been provided to-date to cope with this important issue. This article demonstrates the robustness to Registration Error of the existing SParse representation-based spatio-temporal reflectance fusion model (SPSTFM). Different to conventional methods that are performed on a per-pixel basis, SPSTFM utilizes image patches as the basic unit. We demonstrate theoretically that the effect of Registration Error on patch-based methods is smaller than for pixel-based methods. Experimental results show that SPSTFM is highly robust to Registration Error and is far more accurate under various Registration Errors relative to pixel-based methods. The advantage is shown to be greater for heterogeneous regions than for homogeneous regions, and is large for the fusion of normalized difference vegetation index data. SPSTFM, thus, offers the remote sensing community a crucial tool to overcome one of the longest standing challenges to the effective fusion of remote sensing image time-series.
Mehdi Hedjazi Moghari - One of the best experts on this subject based on the ideXlab platform.
-
Estimation of Optimal Fiducial Target Registration Error in the Presence of Heteroscedastic Noise
IEEE transactions on medical imaging, 2010Co-Authors: Mehdi Hedjazi Moghari, Randy E Ellis, Purang AbolmaesumiAbstract:We study the effect of point dependent (heteroscedastic) and identically distributed anisotropic fiducial localization noise on fiducial target Registration Error (TRE). We derive an analytic expression, based on the concept of mechanism spatial stiffness, for predicting TRE. The accuracy of the predicted TRE is compared to simulated values where the optimal Registration transformation is computed using the heteroscedastic Errors in variables algorithm. The predicted values are shown to be contained by the 95% confidence intervals of the root mean square TRE obtained from the simulations.
-
distribution of target Registration Error for anisotropic and inhomogeneous fiducial localization Error
IEEE Transactions on Medical Imaging, 2009Co-Authors: Mehdi Hedjazi Moghari, Purang AbolmaesumiAbstract:In point-based rigid-body Registration, target Registration Error (TRE) is an important measure of the accuracy of the performed Registration. The Registration's accuracy depends on the fiducial localization Error (FLE) which, in turn, is due to the measurement Errors in the points (fiducials) used to perform the Registration. FLE may have different characteristics and distributions at each point of the registering data sets, and along each orthogonal axis. Previously, the distribution of TRE was estimated based on the assumption that FLE has an independent, identical, and isotropic or anisotropic distribution for each point in the registering data sets. In this article, we present a general solution based on the maximum likelihood (ML) algorithm that estimates the distribution of TRE for the cases where FLE has an independent, identical or inhomogeneous, isotropic or anisotropic, distribution at each point in the registering data sets, and when an algorithm is available that is capable of calculating the optimum Registration to first order. Mathematically, we show that the proposed algorithm simplifies to the one proposed by Fitzpatrick and West when FLE has an independent, identical, and isotropic distribution in the registering data sets. Furthermore, we use numerical simulations to show that the proposed algorithm accurately estimates the distribution of TRE when FLE has an independent, inhomogeneous, and anisotropic distribution in the registering data sets.
-
Distribution of Fiducial Registration Error in Rigid-Body Point-Based Registration
IEEE Transactions on Medical Imaging, 2009Co-Authors: Mehdi Hedjazi Moghari, Purang AbolmaesumiAbstract:Rigid-body point-based Registration is frequently used in computer assisted surgery to align corresponding points, or fiducials, in preoperative and intraoperative data. This alignment is mostly achieved by assuming the same homogeneous Error distribution for all the points; however, due to the properties of the medical instruments used in measuring the point coordinates, the Error distribution might be inhomogeneous and different for each point. In this paper, in an effort to understand the effect of Error distribution in the localized points on the performed Registration, we derive a closed-form solution relating the Error distribution of each point with the performed Registration accuracy. The solution uses maximum likelihood estimation to calculate the probability density function of Registration Error at each fiducial point. Extensive numerical simulations are performed to validated the proposed solution.
-
a theoretical comparison of different target Registration Error estimators
Medical Image Computing and Computer-Assisted Intervention, 2008Co-Authors: Mehdi Hedjazi Moghari, Purang AbolmaesumiAbstract:Estimation of target Registration Error (TRE), a common measure of the Registration accuracy, is an important issue in computer assisted surgeries. Within the last decade, several new approaches have been developed to estimate either the mean squared value of TRE or the distribution of TRE under different noise conditions. In this paper, we theoretically demonstrate that all the proposed algorithms converge to a general Maximum Likelihood (ML) solution. Numerical simulations are performed to validate our derivations. Using experimentally measured fiducial localization Error, we provide an example of TRE prediction in the presence of anisotropic noise.
-
maximum likelihood estimation of the distribution of target Registration Error
Medical Imaging 2008: Visualization Image-Guided Procedures and Modeling, 2008Co-Authors: Mehdi Hedjazi Moghari, Purang AbolmaesumiAbstract:Estimating the alignment accuracy is an important issue in rigid-body point-based Registration algorithms. The Registration accuracy depends on the level of the noise perturbing the registering data sets. The noise in the data sets arises from the fiducial (point) localization Error (FLE) that may have an identical or inhomogeneous, isotropic or anisotropic distribution at each point in each data set. Target Registration Error (TRE) has been defined in the literature, as an Error measure in terms of FLE, to compute the Registration accuracy at a point (target) which is not used in the Registration process. In this paper, we mathematically derive a general solution to approximate the distribution of TRE after Registration of two data sets in the presence of FLE having any type of distribution. The Maximum Likelihood (ML) algorithm is proposed to estimate the Registration parameters and their variances between two data sets. The variances are then used in a closed-form solution, previously presented by these authors, to derive the distribution of TRE at a target location. Based on numerical simulations, it is demonstrated that the derived distribution of TRE, in contrast to the existing methods in the literature, accurately follows the distribution generated by Monte Carlo simulation even when FLE has an inhomogeneous isotropic or anisotropic distribution.
Mark A Livingston - One of the best experts on this subject based on the ideXlab platform.
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the effect of Registration Error on tracking distant augmented objects
International Symposium on Mixed and Augmented Reality, 2008Co-Authors: Mark A LivingstonAbstract:We conducted a user study of the effect of Registration Error on performance of tracking distant objects in augmented reality. Categorizing Error by types that are often used as specifications, we hoped to derive some insight into the ability of users to tolerate noise, latency, and orientation Error. We used measurements from actual systems to derive the parameter settings. We expected all three Errors to influence userspsila ability to perform the task correctly and the precision with which they performed the task. We found that high latency had a negative impact on both performance and response time. While noise consistently interacted with the other variables, and orientation Error increased user Error, the differences between ldquohighrdquo and ldquolowrdquo amounts were smaller than we expected. Results of userspsila subjective rankings of these three categories of Error were surprisingly mixed. Users believed noise was the most detrimental, though statistical analysis of performance refuted this belief. We interpret the results and draw insights for system design.
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ISMAR - The effect of Registration Error on tracking distant augmented objects
2008 7th IEEE ACM International Symposium on Mixed and Augmented Reality, 2008Co-Authors: Mark A LivingstonAbstract:We conducted a user study of the effect of Registration Error on performance of tracking distant objects in augmented reality. Categorizing Error by types that are often used as specifications, we hoped to derive some insight into the ability of users to tolerate noise, latency, and orientation Error. We used measurements from actual systems to derive the parameter settings. We expected all three Errors to influence userspsila ability to perform the task correctly and the precision with which they performed the task. We found that high latency had a negative impact on both performance and response time. While noise consistently interacted with the other variables, and orientation Error increased user Error, the differences between ldquohighrdquo and ldquolowrdquo amounts were smaller than we expected. Results of userspsila subjective rankings of these three categories of Error were surprisingly mixed. Users believed noise was the most detrimental, though statistical analysis of performance refuted this belief. We interpret the results and draw insights for system design.
