The Experts below are selected from a list of 34278 Experts worldwide ranked by ideXlab platform
Jean-francois Hochedez - One of the best experts on this subject based on the ideXlab platform.
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Segmentation of Extreme Ultraviolet Solar Images using a Multispectral Data Fusion Process
2007 IEEE International Fuzzy Systems Conference, 2007Co-Authors: Vincent Barra, Veronique Delouille, Jean-francois HochedezAbstract:Accurate means of quantifying the respective contributions of different structures to the solar irradiance is now a key issue in Solar Physics, with implications to Sun-Earth relationships and space weather study. In this paper, we propose a three-step Fusion scheme, that allows to aggregate (17.1 nm, 19.5 nm) Data stemming from the solar EIT instrument onboard the SoHO mission, and that is flexible enough to allow the integration of other type of information. The method is based on both a spatially constrained possibilistic clustering algorithm and a context dependent Fusion operator. It aggregates the complementary and redundant information coming from the input sources. The results obtained on a 9-year Dataset are consistent with those found in the solar physics literature. Unlike previous algorithms used in solar physics, our method has the ability to add further heterogeneous sources and sensors (e.g. human knowledge, images in other bandpasses, ratio of images) to the Process, in order to postpone the decision step (here the segmentation of structures of interest) until sufficient information is available.
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FUZZ-IEEE - Segmentation of Extreme Ultraviolet Solar Images using a Multispectral Data Fusion Process
2007 IEEE International Fuzzy Systems Conference, 2007Co-Authors: Vincent Barra, Veronique Delouille, Jean-francois HochedezAbstract:Accurate means of quantifying the respective contributions of different structures to the solar irradiance is now a key issue in Solar Physics, with implications to Sun-Earth relationships and space weather study. In this paper, we propose a three-step Fusion scheme, that allows to aggregate (17.1 nm, 19.5 nm) Data stemming from the solar EIT instrument onboard the SoHO mission, and that is flexible enough to allow the integration of other type of information. The method is based on both a spatially constrained possibilistic clustering algorithm and a context dependent Fusion operator. It aggregates the complementary and redundant information coming from the input sources. The results obtained on a 9-year Dataset are consistent with those found in the solar physics literature. Unlike previous algorithms used in solar physics, our method has the ability to add further heterogeneous sources and sensors (e.g. human knowledge, images in other bandpasses, ratio of images) to the Process, in order to postpone the decision step (here the segmentation of structures of interest) until sufficient information is available.
François Charpillet - One of the best experts on this subject based on the ideXlab platform.
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A new definition of qualified gain in a Data Fusion
2002Co-Authors: David Bellot, Anne Boyer, François CharpilletAbstract:A formal framework is proposed for defining Data Fusion Processes and particularly a notion of qualified gain in a Data Fusion Process is proposed: gain in representation, completeness, accuracy and certainty. These notions are applied to a medical monitoring and diagnosis problem where a dynamic Bayesian network (DBN) is used to modelize time series of observations and evolving states. The model aims at giving a daily diagnosis. Our experiments are under way by using Data of an already existing system collected on kidney disease patients. Results will be characterized using our notion of qualified gains.
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Vision Based Localisation for a Mobile Robot
2000Co-Authors: Franck Gechter, François CharpilletAbstract:We present a vision based localization system for a mobile robot that uses a representative set of images obtained during an initial exploration of the environment. This set of images makes it possible to represent the environment as a partially markov decision Process. The originality of this approach is the resulting Data Fusion Process that uses both image matching and decision made by the robot in order to estimate the set of plausible position of the robot and the associated probabilities. Image matching or recognition is achieved using principal components analysis.
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Markov Based Localization Device for a Mobile Robot
2000Co-Authors: Franck Gechter, François CharpilletAbstract:This paper presents a vision based localization system which can be used on a wide range of mobile vehicles. The principle of the algorithm developed is to merge the information given by a vision system with the Data extracted from the moves of the vehicle. The image Processing level is performed by using principal components analysis that allows low cost position estimation by using a representative set of images obtained during an initial exploration of the environment. This set of images makes it possible to represent the environment as a partially observable Markov decision Process. The originality of this approach is the resulting Data Fusion Process that uses both image matching and decision made by the robot in order to estimate the set of plausible positions of the robot and the associated probabilities. Furthermore, this stochastic localisation device shows better results compared with the classical methods such as static neighborhoods. The main characteristics of this localization device are its robustness, its accuracy and its low cost compared with usual localization methods.
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ICTAI - Vision based localisation for a mobile robot
Proceedings 12th IEEE Internationals Conference on Tools with Artificial Intelligence. ICTAI 2000, 1Co-Authors: Franck Gechter, François CharpilletAbstract:Presents a vision-based localisation system for a mobile robot that uses a representative set of images obtained during an initial exploration of the environment. This set of images makes it possible to represent the environment as a partially Markov decision Process. The originality of this approach is the resulting Data Fusion Process that uses both image matching and the decisions made by the robot in order to estimate the set of plausible positions of the robot and the associated probabilities. Image matching or recognition is achieved using principal components analysis.
Xuemei Xu - One of the best experts on this subject based on the ideXlab platform.
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Application of Linear Predictive Coding and Data Fusion Process for Target Tracking by Doppler Through-Wall Radar
IEEE Transactions on Microwave Theory and Techniques, 2019Co-Authors: Yipeng Ding, Xiali Yu, Juan Zhang, Xuemei XuAbstract:The issue of ambiguous frequency is a major drawback of the Doppler through-wall radar in target tracking applications. To solve the issue, an additional receiver, which can acquire target information from a different detection perspective, is added to the radar system, and the Data Fusion Process is proposed to identify and track the targets in real time. The linear prediction coding technique is combined with the Data Fusion Process to predict the target initial state for higher positioning accuracy. Compared with the traditional tracking approach, the proposed algorithm can not only improve the target positioning accuracy, suppress the issue of ambiguous frequency, but also help to identify stationary and tangentially moving targets, which is very essential in target tracking applications. A series of experiments are conducted to illustrate an outranking performance of the proposed algorithm in comparison with the traditional approach.
Vincent Barra - One of the best experts on this subject based on the ideXlab platform.
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Segmentation of Extreme Ultraviolet Solar Images using a Multispectral Data Fusion Process
2007 IEEE International Fuzzy Systems Conference, 2007Co-Authors: Vincent Barra, Veronique Delouille, Jean-francois HochedezAbstract:Accurate means of quantifying the respective contributions of different structures to the solar irradiance is now a key issue in Solar Physics, with implications to Sun-Earth relationships and space weather study. In this paper, we propose a three-step Fusion scheme, that allows to aggregate (17.1 nm, 19.5 nm) Data stemming from the solar EIT instrument onboard the SoHO mission, and that is flexible enough to allow the integration of other type of information. The method is based on both a spatially constrained possibilistic clustering algorithm and a context dependent Fusion operator. It aggregates the complementary and redundant information coming from the input sources. The results obtained on a 9-year Dataset are consistent with those found in the solar physics literature. Unlike previous algorithms used in solar physics, our method has the ability to add further heterogeneous sources and sensors (e.g. human knowledge, images in other bandpasses, ratio of images) to the Process, in order to postpone the decision step (here the segmentation of structures of interest) until sufficient information is available.
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FUZZ-IEEE - Segmentation of Extreme Ultraviolet Solar Images using a Multispectral Data Fusion Process
2007 IEEE International Fuzzy Systems Conference, 2007Co-Authors: Vincent Barra, Veronique Delouille, Jean-francois HochedezAbstract:Accurate means of quantifying the respective contributions of different structures to the solar irradiance is now a key issue in Solar Physics, with implications to Sun-Earth relationships and space weather study. In this paper, we propose a three-step Fusion scheme, that allows to aggregate (17.1 nm, 19.5 nm) Data stemming from the solar EIT instrument onboard the SoHO mission, and that is flexible enough to allow the integration of other type of information. The method is based on both a spatially constrained possibilistic clustering algorithm and a context dependent Fusion operator. It aggregates the complementary and redundant information coming from the input sources. The results obtained on a 9-year Dataset are consistent with those found in the solar physics literature. Unlike previous algorithms used in solar physics, our method has the ability to add further heterogeneous sources and sensors (e.g. human knowledge, images in other bandpasses, ratio of images) to the Process, in order to postpone the decision step (here the segmentation of structures of interest) until sufficient information is available.
Jean-françois Sérignat - One of the best experts on this subject based on the ideXlab platform.
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Information extraction from sound for medical telemonitoring
IEEE Transactions on Information Technology in Biomedicine, 2006Co-Authors: Dan Istrate, Eric Castelli, Michel Vacher, Laurent Besacier, Jean-françois SérignatAbstract:Today, the growth of the aging population in Europe needs an increasing number of health care professionals and facilities for aged persons. Medical telemonitoring at home (and, more generally, telemedicine) improves the patient's comfort and reduces hospitalization costs. Using sound surveillance as an alternative solution to video telemonitoring, this paper deals with the detection and classification of alarming sounds in a noisy environment. The proposed sound analysis system can detect distress or everyday sounds everywhere in the monitored apartment, and is connected to classical medical telemonitoring sensors through a Data Fusion Process. The sound analysis system is divided in two stages: sound detection and classification. The first analysis stage (sound detection) must extract significant sounds from a continuous signal flow. A new detection algorithm based on discrete wavelet transform is proposed in this paper, which leads to accurate results when applied to nonstationary signals (such as impulsive sounds). The algorithm presented in this paper was evaluated in a noisy environment and is favorably compared to the state of the art algorithms in the field. The second stage of the system is sound classification, which uses a statistical approach to identify unknown sounds. A statistical study was done to find out the most discriminant acoustical parameters in the input of the classification module. New wavelet based parameters, better adapted to noise, are proposed in this paper. The telemonitoring system validation is presented through various real and simulated test sets. The global sound based system leads to a 3% missed alarm rate and could be fused with other medical sensors to improve performance.
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Multichannel smart sound sensor for perceptive spaces
2004Co-Authors: Dan Istrate, Jean-françois Sérignat, Michel Vacher, Eric CastelliAbstract:Sound based perceptive spaces are usually encountered for friendly man-machine interfaces, but sound informa-tion extraction for perceptive spaces is a complex task be-cause of environmental noise and of multichannel Process-ing need. A multichannel smart sound sensor capable to detect and identify sound events in noisy conditions is pre-sented in this paper. The multichannel sound Processing al-lows us to localize the sound in the perceptive space and to select appropriate signal for identification procedure. This sensor is real time implemented on PC. The event detec-tion module is carried out for each channel in real time. The classification module is launched in a parallel task on the channel chosen by Data Fusion Process. The aim of this Process is to select the channel with the biggest signal to noise ratio when a multiple detection occurs. The valida-tion of smart sensor is made on a test set and is presented with the proposed methodology of evaluation for a medical telemonitoring application. The obtained results are allow-ing us to develop perceptive space applications.
