The Experts below are selected from a list of 32904 Experts worldwide ranked by ideXlab platform
Stefano Tubaro - One of the best experts on this subject based on the ideXlab platform.
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Estimation and compensation of subpixel edge Localization Error
IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997Co-Authors: Federico Pedersini, Augusto Sarti, Stefano TubaroAbstract:We propose and analyze a method for improving the performance of subpixel edge Localization (EL) techniques through compensation of the systematic portion of the Localization Error. The method is based on the estimation of the EL characteristic through statistical analysis of a test image and is independent of the EL technique in use.
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Short Papers Estimation and Compensation of Subpixel Edge Localization Error
1997Co-Authors: Federico Pedersini, Augusto Sarti, Stefano TubaroAbstract:We propose and analyze a method for improving the performance of subpixel Edge Localization (EL) techniques through compensation of the systematic portion of the Localization Error. The method is based on the estimation of the EL characteristic through statistical analysis of a test image and is independent of the EL technique in use. Index Terms—Feature extraction, edge Localization, subpixel detection. ———————— ✦ ————————
Federico Pedersini - One of the best experts on this subject based on the ideXlab platform.
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Estimation and compensation of subpixel edge Localization Error
IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997Co-Authors: Federico Pedersini, Augusto Sarti, Stefano TubaroAbstract:We propose and analyze a method for improving the performance of subpixel edge Localization (EL) techniques through compensation of the systematic portion of the Localization Error. The method is based on the estimation of the EL characteristic through statistical analysis of a test image and is independent of the EL technique in use.
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Short Papers Estimation and Compensation of Subpixel Edge Localization Error
1997Co-Authors: Federico Pedersini, Augusto Sarti, Stefano TubaroAbstract:We propose and analyze a method for improving the performance of subpixel Edge Localization (EL) techniques through compensation of the systematic portion of the Localization Error. The method is based on the estimation of the EL characteristic through statistical analysis of a test image and is independent of the EL technique in use. Index Terms—Feature extraction, edge Localization, subpixel detection. ———————— ✦ ————————
Purang Abolmaesumi - One of the best experts on this subject based on the ideXlab platform.
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Understanding the Effect of Bias in Fiducial Localization Error on Point-Based Rigid-Body Registration
IEEE Transactions on Medical Imaging, 2010Co-Authors: Mehdi Hedjazi Moghari, Purang AbolmaesumiAbstract:Image registration is a single point of failure in the image-guided computer-assisted surgery. Registration is primarily used to align and fuse the data sets taken from patient's anatomy before and during surgeries. Point-based rigid-body registration is usually performed by identifying corresponding fiducials (either natural landmarks or implanted ones) in the data sets. Since the Localization of fiducials is imprecise and is generally perturbed by random noise, the performed registration is imperfect and has some Error. Previous work has extensively analyzed the behavior of this Error when the fiducial Localization Error has zero-mean over the entire set of fiducials. However, if noise has a nonzero-mean or a bias, no formulation yet exists to determine the effect of noise on the overall registration accuracy. In this work, we derive novel formulations that relate the bias in the localized fiducials to the accuracy of the performed registration. We analytically and numerically demonstrate that by eliminating the estimated bias from the measured fiducial locations, one can effectively increase the accuracy of the performed registration.
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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.
Mianxiong Dong - One of the best experts on this subject based on the ideXlab platform.
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PIMRC - Extreme Values of Trilateration Localization Error in Wireless Communication Systems
2020 IEEE 31st Annual International Symposium on Personal Indoor and Mobile Radio Communications, 2020Co-Authors: Muhammad Farooq-i-azam, Mianxiong DongAbstract:The analytical model of trilateration Localization Error expresses the Localization Error in terms of distance estimation Errors. Using the analytical model, we investigate the trilateration Localization Error, and derive the extreme values of its components and other novel results. We show that these extreme values occur when the distance estimation Errors have opposite signs. We further show that better Localization accuracy is achieved if all the distance estimation Errors are negative compared to when the same magnitudes of distance estimation Errors are positive. The results are applicable to all wireless communication systems and networks where trilateration and multilateration may be used for position estimation. This includes global navigation satellite systems, such as the global positioning system, wireless local area networks, wireless sensor networks, internet of things, and other miscellaneous applications. All the results are verified using simulation.
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GLOBECOM - An Analytical Model of Trilateration Localization Error
2019 IEEE Global Communications Conference (GLOBECOM), 2019Co-Authors: Muhammad Farooq-i-azam, Mianxiong DongAbstract:Trilateration and multilateration are important location estimation techniques used in a diverse range of networks and applications. The system of equations yielded by multilateration can be reduced to simpler linear equations which can be solved to arrive at a closed form analytic solution. Exploiting this solution technique, we develop a novel and unique analytical model for the Localization Error resulting from trilateration. The analytical model can be used for the analysis of the Localization Error in all applications wherever multilateration is used for position estimation including internet of things, wireless sensor networks and global navigation satellite system thereby increasing reliability and quality of Localization. As an example, we use the analytical model to corroborate the fact that Localization Error is a function of topology of reference positions in addition to distance estimation Errors. The analytical model is verified using simulation experiments.
Shunsuke Kamijo - One of the best experts on this subject based on the ideXlab platform.
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evaluating the capability of openstreetmap for estimating vehicle Localization Error
International Conference on Intelligent Transportation Systems, 2019Co-Authors: Kelvin Wong, Ehsan Javanmardi, Mahdi Javanmardi, Shunsuke KamijoAbstract:Accurate Localization is an important part of successful autonomous driving. Recent studies suggest that when using map-based Localization methods, the representation and layout of real-world phenomena within the prebuilt map is a source of Error. To date, the investigations have been limited to 3D point clouds and normal distribution (ND) maps. This paper explores the potential of using OpenStreetMap (OSM) as a proxy to estimate vehicle Localization Error. Specifically, the experiment uses random forest regression to estimate mean 3D Localization Error from map matching using LiDAR scans and ND maps. Six map evaluation factors were defined for 2D geographic information in a vector format. Initial results for a 1.2 km path in Shinjuku, Tokyo, show that vehicle Localization Error can be estimated with 56.3% model prediction accuracy with two existing OSM data layers only. When OSM data quality issues (inconsistency and completeness) were addressed, the model prediction accuracy was improved to 73.1%.
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Estimating Autonomous Vehicle Localization Error Using 2D Geographic Information
ISPRS International Journal of Geo-Information, 2019Co-Authors: Kelvin Wong, Ehsan Javanmardi, Javanmardi, Shunsuke KamijoAbstract:Accurately and precisely knowing the location of the vehicle is a critical requirement for safe and successful autonomous driving. Recent studies suggest that Error for map-based Localization methods are tightly coupled with the surrounding environment. Considering this relationship, it is therefore possible to estimate Localization Error by quantifying the representation and layout of real-world phenomena. To date, existing work on estimating Localization Error have been limited to using self-collected 3D point cloud maps. This paper investigates the use of pre-existing 2D geographic information datasets as a proxy to estimate autonomous vehicle Localization Error. Seven map evaluation factors were defined for 2D geographic information in a vector format, and random forest regression was used to estimate Localization Error for five experiment paths in Shinjuku, Tokyo. In the best model, the results show that it is possible to estimate autonomous vehicle Localization Error with 69.8% of predictions within 2.5 cm and 87.4% within 5 cm.
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ITSC - Evaluating the Capability of OpenStreetMap for Estimating Vehicle Localization Error
2019 IEEE Intelligent Transportation Systems Conference (ITSC), 2019Co-Authors: Kelvin Wong, Ehsan Javanmardi, Javanmardi, Shunsuke KamijoAbstract:Accurate Localization is an important part of successful autonomous driving. Recent studies suggest that when using map-based Localization methods, the representation and layout of real-world phenomena within the prebuilt map is a source of Error. To date, the investigations have been limited to 3D point clouds and normal distribution (ND) maps. This paper explores the potential of using OpenStreetMap (OSM) as a proxy to estimate vehicle Localization Error. Specifically, the experiment uses random forest regression to estimate mean 3D Localization Error from map matching using LiDAR scans and ND maps. Six map evaluation factors were defined for 2D geographic information in a vector format. Initial results for a 1.2 km path in Shinjuku, Tokyo, show that vehicle Localization Error can be estimated with 56.3% model prediction accuracy with two existing OSM data layers only. When OSM data quality issues (inconsistency and completeness) were addressed, the model prediction accuracy was improved to 73.1%.
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adaptive resolution refinement of ndt map based on Localization Error modeled by map factors
International Conference on Intelligent Transportation Systems, 2018Co-Authors: Ehsan Javanmardi, Mahdi Javanmardi, Shunsuke KamijoAbstract:One of the prominent methods for accurate self-Localization for autonomous vehicles is map-matching with light detection and ranging (LiDAR) based on Normal distribution transform (NDT). In NDT, map space is divided into the grids, and for each grid, normal distribution (ND) of the points are calculated, and LiDAR scan is matched to these NDs. Bigger grid sizes (lower resolution) are more favorable because it can abstract more points in each grid and reduce map size. However, if the resolution is low, many details of the environment are ignored, and the Localization accuracy degrades. This information loss and Localization Error is different from place to place on the map and can be evaluated beforehand for each resolution. In this work, ten map factors are used to evaluate the Localization ability of the map in a specific position for each resolution. Using the evaluation result, for each position of the map, a lower resolution that can preserve the required Localization accuracy are determined. In this method, NDT map is generated by adaptively selecting the resolution for each position of the map. Experimental results in Shinjuku, Tokyo, show that by using this strategy, map size can be reduced by up to 32% of the original size while the mean Localization Error remains less than 0.141m.
