Pub Date : 2002-07-08DOI: 10.1109/ICIF.2002.1020919
S. Brumby, P. Pope, A. Galbraith, J. Szymanski
Hyperspectral imagery with moderate spatial resolution (/spl sim/30 m) presents an interesting challenge to feature extraction algorithm developers, as both spatial and spectral signatures may be required to identify the feature of interest. We describe a genetic programming software system, called GENIE, which augments the human scientist/analyst by evolving customized spatio-spectral feature extraction pipelines from training data provided via an intuitive, point-and-click interface. We describe recent work exploring geospatial feature extraction from hyperspectral imagery, and from a multi-instrument fused dataset. For hyperspectral imagery, we demonstrate our system on NASA Earth Observer 1 (EO-1) Hyperion imagery, applied to agricultural crop detection. We present an evolved pipeline, and discuss its operation. We also discuss work with multi-spectral imagery (DOE/NNSA Multispectral Thermal Imager) fused with USGS digital elevation model (DEM) data, with the application of detecting mixed conifer forest.
{"title":"Evolving feature extraction algorithms for hyperspectral and fused imagery","authors":"S. Brumby, P. Pope, A. Galbraith, J. Szymanski","doi":"10.1109/ICIF.2002.1020919","DOIUrl":"https://doi.org/10.1109/ICIF.2002.1020919","url":null,"abstract":"Hyperspectral imagery with moderate spatial resolution (/spl sim/30 m) presents an interesting challenge to feature extraction algorithm developers, as both spatial and spectral signatures may be required to identify the feature of interest. We describe a genetic programming software system, called GENIE, which augments the human scientist/analyst by evolving customized spatio-spectral feature extraction pipelines from training data provided via an intuitive, point-and-click interface. We describe recent work exploring geospatial feature extraction from hyperspectral imagery, and from a multi-instrument fused dataset. For hyperspectral imagery, we demonstrate our system on NASA Earth Observer 1 (EO-1) Hyperion imagery, applied to agricultural crop detection. We present an evolved pipeline, and discuss its operation. We also discuss work with multi-spectral imagery (DOE/NNSA Multispectral Thermal Imager) fused with USGS digital elevation model (DEM) data, with the application of detecting mixed conifer forest.","PeriodicalId":399150,"journal":{"name":"Proceedings of the Fifth International Conference on Information Fusion. FUSION 2002. (IEEE Cat.No.02EX5997)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132133259","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2002-07-08DOI: 10.1109/ICIF.2002.1020925
R. Niu, P. Varshney, K. Mehrotra, C. Mohan
For a multi-sensor tracking system, the effects of temporally staggered sensors are investigated and compared with synchronous sensors. To make fair comparisons, a new metric, the average estimation error variance, is defined. Many analytical results are derived for sensors with equal measurement noise variance. Temporally staggered sensors always result in a smaller average error variance than synchronous sensors. The corresponding optimal staggering pattern is such that the sensors are uniformly distributed over time. For sensors with different measurement noise variances, the optimal staggering pattern can be found numerically. Intuitive guidelines on selecting optimal staggering pattern have been presented for different target tracking scenarios.
{"title":"Temporal fusion in multi-sensor target tracking systems","authors":"R. Niu, P. Varshney, K. Mehrotra, C. Mohan","doi":"10.1109/ICIF.2002.1020925","DOIUrl":"https://doi.org/10.1109/ICIF.2002.1020925","url":null,"abstract":"For a multi-sensor tracking system, the effects of temporally staggered sensors are investigated and compared with synchronous sensors. To make fair comparisons, a new metric, the average estimation error variance, is defined. Many analytical results are derived for sensors with equal measurement noise variance. Temporally staggered sensors always result in a smaller average error variance than synchronous sensors. The corresponding optimal staggering pattern is such that the sensors are uniformly distributed over time. For sensors with different measurement noise variances, the optimal staggering pattern can be found numerically. Intuitive guidelines on selecting optimal staggering pattern have been presented for different target tracking scenarios.","PeriodicalId":399150,"journal":{"name":"Proceedings of the Fifth International Conference on Information Fusion. FUSION 2002. (IEEE Cat.No.02EX5997)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134067579","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2002-07-08DOI: 10.1109/ICIF.2002.1021168
C. Ramesh, T. Ranjith
This paper introduces the concept of fusion symmetry (FS) as a measure of evaluating performance of fusion algorithms. The fusion symmetry measure quantifies the relative distance (in terms of mutual information) of the fused image with respect to input images. The smaller the FS the more symmetric is the fused image, i.e., it captures information from both the input images. The traditional criterion of maximizing the joint mutual information is also quantified and a definition called fusion factor is evolved. An algorithm for image fusion using a lifting wavelet filter is proposed. In this algorithm fusion is performed in the transformed domain. The performance of this algorithm is compared with that obtained using average, Laplacian pyramid based approaches and guidelines for selecting an appropriate image fusion algorithm for different sensor conditions are evolved.
{"title":"Fusion performance measures and a lifting wavelet transform based algorithm for image fusion","authors":"C. Ramesh, T. Ranjith","doi":"10.1109/ICIF.2002.1021168","DOIUrl":"https://doi.org/10.1109/ICIF.2002.1021168","url":null,"abstract":"This paper introduces the concept of fusion symmetry (FS) as a measure of evaluating performance of fusion algorithms. The fusion symmetry measure quantifies the relative distance (in terms of mutual information) of the fused image with respect to input images. The smaller the FS the more symmetric is the fused image, i.e., it captures information from both the input images. The traditional criterion of maximizing the joint mutual information is also quantified and a definition called fusion factor is evolved. An algorithm for image fusion using a lifting wavelet filter is proposed. In this algorithm fusion is performed in the transformed domain. The performance of this algorithm is compared with that obtained using average, Laplacian pyramid based approaches and guidelines for selecting an appropriate image fusion algorithm for different sensor conditions are evolved.","PeriodicalId":399150,"journal":{"name":"Proceedings of the Fifth International Conference on Information Fusion. FUSION 2002. (IEEE Cat.No.02EX5997)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121988138","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2002-07-08DOI: 10.1109/ICIF.2002.1021227
C. Christou
The present work explores a new method of integrated detection, localization, and tracking of multiple broadband signals directly from array data, without the requirement of distinct data association. The method is based on Maximum A-Posteriori probability concepts and combines Maximum Likelihood direction finding techniques with Kalman Filter theory. Implicit data association is given by a Nonlinear Programming scheme that simplifies the solution of a constrained optimization problem. Assuming Markov Motion and random Gaussian signals and noise, diverse kinematic scenarios for both synthetic and real data sets were investigated. Full data batch, semi-sequential and fully sequential variants were developed in element space, beamspace and windowed element space. The method was found to work well down to a signal-to-noise ratio of -10 dB, and for highly dynamic scenarios. An alternating projection method was used for contact state initialization and signal enumeration.
{"title":"An integrated method to detection, data association and tracking of multiple broadband signals","authors":"C. Christou","doi":"10.1109/ICIF.2002.1021227","DOIUrl":"https://doi.org/10.1109/ICIF.2002.1021227","url":null,"abstract":"The present work explores a new method of integrated detection, localization, and tracking of multiple broadband signals directly from array data, without the requirement of distinct data association. The method is based on Maximum A-Posteriori probability concepts and combines Maximum Likelihood direction finding techniques with Kalman Filter theory. Implicit data association is given by a Nonlinear Programming scheme that simplifies the solution of a constrained optimization problem. Assuming Markov Motion and random Gaussian signals and noise, diverse kinematic scenarios for both synthetic and real data sets were investigated. Full data batch, semi-sequential and fully sequential variants were developed in element space, beamspace and windowed element space. The method was found to work well down to a signal-to-noise ratio of -10 dB, and for highly dynamic scenarios. An alternating projection method was used for contact state initialization and signal enumeration.","PeriodicalId":399150,"journal":{"name":"Proceedings of the Fifth International Conference on Information Fusion. FUSION 2002. (IEEE Cat.No.02EX5997)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123743939","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2002-07-08DOI: 10.1109/ICIF.2002.1020966
Mieczyslaw M. Kokar, Jiao Wang
In this paper we investigate a scenario in which the fusion process (the algorithm) could be synthesized at run time. This goal can be achieved in two steps: first synthesize a formal specification of the fusion process and then generate code from the specification. In this paper we show the first of these steps.
{"title":"Using ontologies for recognition: an example","authors":"Mieczyslaw M. Kokar, Jiao Wang","doi":"10.1109/ICIF.2002.1020966","DOIUrl":"https://doi.org/10.1109/ICIF.2002.1020966","url":null,"abstract":"In this paper we investigate a scenario in which the fusion process (the algorithm) could be synthesized at run time. This goal can be achieved in two steps: first synthesize a formal specification of the fusion process and then generate code from the specification. In this paper we show the first of these steps.","PeriodicalId":399150,"journal":{"name":"Proceedings of the Fifth International Conference on Information Fusion. FUSION 2002. (IEEE Cat.No.02EX5997)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123859121","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2002-07-08DOI: 10.1109/ICIF.2002.1021197
K. Chang, Z. Tian, S. Mori, C. Chong
The purpose of this paper is to develop a quantifiable performance evaluation method for MAP (Maximum A Posterior) track fusion algorithm. The goal is to provide analytical fusion performance without extensive Monte Carlo simulations. The idea is to develop methodologies for steady state fusion performance. Several fusion algorithms such as simple convex combination, cross-covariance combination (CC), information matrix (IM), and MAP fusion have been studied and several performance evaluation methods have been proposed. But most of them are not based on the steady state of an actual dynamic system. This paper conducts similar analysis for MAP fusion algorithm. It has been shown that the MAP or Best-Linear Unbiased Estimate (BLUE) fusion formula provides the best linear minimum mean squared estimates (LMMSE) given local estimates under the linear Gaussian assumption in a static situation (i.e., single iteration). However, in a dynamic situation, recursive fusion iterations are needed and the impact on the performance is not obvious. This paper proposes a systematic analytical procedure to evaluate the performance of such algorithm under two different communication strategies. Specifically, hierarchical fusion with and without feedback is considered. Theoretical curves for the steady state performance of the fusion algorithm with various communication patterns are given. They provide performance bounds for different operating conditions.
本文的目的是开发一种可量化的MAP (Maximum a Posterior)航迹融合算法的性能评价方法。目标是在没有广泛的蒙特卡罗模拟的情况下提供分析聚变性能。这个想法是发展稳态聚变性能的方法。研究了简单凸组合、交叉协方差组合(CC)、信息矩阵(IM)和MAP融合等融合算法，并提出了几种性能评价方法。但它们中的大多数不是基于实际动态系统的稳态。本文对MAP融合算法进行了类似的分析。在静态情况下(即单次迭代)，MAP或best - linear Unbiased Estimate (BLUE)融合公式提供了在线性高斯假设下给定局部估计的最佳线性最小均方估计(LMMSE)。然而，在动态情况下，需要递归融合迭代，对性能的影响并不明显。本文提出了一个系统的分析过程来评估该算法在两种不同通信策略下的性能。具体来说，考虑了有反馈和无反馈的分层融合。给出了不同通信模式下融合算法稳态性能的理论曲线。它们为不同的操作条件提供性能界限。
{"title":"MAP track fusion performance evaluation","authors":"K. Chang, Z. Tian, S. Mori, C. Chong","doi":"10.1109/ICIF.2002.1021197","DOIUrl":"https://doi.org/10.1109/ICIF.2002.1021197","url":null,"abstract":"The purpose of this paper is to develop a quantifiable performance evaluation method for MAP (Maximum A Posterior) track fusion algorithm. The goal is to provide analytical fusion performance without extensive Monte Carlo simulations. The idea is to develop methodologies for steady state fusion performance. Several fusion algorithms such as simple convex combination, cross-covariance combination (CC), information matrix (IM), and MAP fusion have been studied and several performance evaluation methods have been proposed. But most of them are not based on the steady state of an actual dynamic system. This paper conducts similar analysis for MAP fusion algorithm. It has been shown that the MAP or Best-Linear Unbiased Estimate (BLUE) fusion formula provides the best linear minimum mean squared estimates (LMMSE) given local estimates under the linear Gaussian assumption in a static situation (i.e., single iteration). However, in a dynamic situation, recursive fusion iterations are needed and the impact on the performance is not obvious. This paper proposes a systematic analytical procedure to evaluate the performance of such algorithm under two different communication strategies. Specifically, hierarchical fusion with and without feedback is considered. Theoretical curves for the steady state performance of the fusion algorithm with various communication patterns are given. They provide performance bounds for different operating conditions.","PeriodicalId":399150,"journal":{"name":"Proceedings of the Fifth International Conference on Information Fusion. FUSION 2002. (IEEE Cat.No.02EX5997)","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124980632","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2002-07-08DOI: 10.1109/ICIF.2002.1020999
Qiao Xiangdong, W. Baoshu
Track-to-track fusion is an important part of multisensor multitarget tracking. Much research has been done in this area. An adaptive approach for track fusion in multisensor environment proposed by C. Beugnon et al. is investigated in this paper. The algorithm chooses the method for calculating the global estimate according to a decision logic, which is based on comparison between distance metric and threshold. Unfortunately, we found that the algorithm, in deriving distance metric, is established under an implicit assumption that sensor level tracks are uncorrelated with global tracks. However, even without process noise the global track and sensor-level track are cross-correlated because they are based on common data. Based on this, a modified adaptive track fusion approach is developed in this paper. The crosscorrelation between sensor-level and global tracks is taken into account in the modified approach. The modified approach still reserves the flexible ability to react to the change of sensor system and it also provides a natural link between track association and fusion. Simulation result illustrates that the modified approach is more robust to the change of system environment.
{"title":"A modified adaptive track fusion approach","authors":"Qiao Xiangdong, W. Baoshu","doi":"10.1109/ICIF.2002.1020999","DOIUrl":"https://doi.org/10.1109/ICIF.2002.1020999","url":null,"abstract":"Track-to-track fusion is an important part of multisensor multitarget tracking. Much research has been done in this area. An adaptive approach for track fusion in multisensor environment proposed by C. Beugnon et al. is investigated in this paper. The algorithm chooses the method for calculating the global estimate according to a decision logic, which is based on comparison between distance metric and threshold. Unfortunately, we found that the algorithm, in deriving distance metric, is established under an implicit assumption that sensor level tracks are uncorrelated with global tracks. However, even without process noise the global track and sensor-level track are cross-correlated because they are based on common data. Based on this, a modified adaptive track fusion approach is developed in this paper. The crosscorrelation between sensor-level and global tracks is taken into account in the modified approach. The modified approach still reserves the flexible ability to react to the change of sensor system and it also provides a natural link between track association and fusion. Simulation result illustrates that the modified approach is more robust to the change of system environment.","PeriodicalId":399150,"journal":{"name":"Proceedings of the Fifth International Conference on Information Fusion. FUSION 2002. (IEEE Cat.No.02EX5997)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127093022","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2002-07-08DOI: 10.1109/ICIF.2002.1020936
H. Schultz, A. Hanson, E. Riseman, F. Stolle, Zhigang Zhu, Woo Dong-min
This paper describes a robust method for recovering an optimal DEM and its variance from multiple, randomly orientated views of a surface. The method generates a set of DEM tiles in a common coordinate system from multiple overlapping images, and then employs the concept of self-consistency to detect and remove errors from the tiles. The clean tiles are averaged together to form a low noise composite DEM. The method is tested on real and photo realistic simulated data. Results show that the method is capable of producing a virtually error free composite DEM.
{"title":"A self-consistency technique for fusing 3D information","authors":"H. Schultz, A. Hanson, E. Riseman, F. Stolle, Zhigang Zhu, Woo Dong-min","doi":"10.1109/ICIF.2002.1020936","DOIUrl":"https://doi.org/10.1109/ICIF.2002.1020936","url":null,"abstract":"This paper describes a robust method for recovering an optimal DEM and its variance from multiple, randomly orientated views of a surface. The method generates a set of DEM tiles in a common coordinate system from multiple overlapping images, and then employs the concept of self-consistency to detect and remove errors from the tiles. The clean tiles are averaged together to form a low noise composite DEM. The method is tested on real and photo realistic simulated data. Results show that the method is capable of producing a virtually error free composite DEM.","PeriodicalId":399150,"journal":{"name":"Proceedings of the Fifth International Conference on Information Fusion. FUSION 2002. (IEEE Cat.No.02EX5997)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129840007","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2002-07-08DOI: 10.1109/ICIF.2002.1020927
A. Newman, J. Emory, W.H. Bennett
Surveillance of ground targets from airborne systems is increasingly of interest in tactical operations. Collection management synchronization can provide improved tactical data products for multi-platform operations. Synchronization of collection management is considered for coalition operations involving multiple airborne GMTI radar systems. An emerging synchronization collection management planning capability is evaluated for application to surveillance using GMTI radar systems. Several examples are provided which illustrate the capability and benefits of synchronized collection management.
{"title":"Synchronized GMTI radar collection management in a coalition environment","authors":"A. Newman, J. Emory, W.H. Bennett","doi":"10.1109/ICIF.2002.1020927","DOIUrl":"https://doi.org/10.1109/ICIF.2002.1020927","url":null,"abstract":"Surveillance of ground targets from airborne systems is increasingly of interest in tactical operations. Collection management synchronization can provide improved tactical data products for multi-platform operations. Synchronization of collection management is considered for coalition operations involving multiple airborne GMTI radar systems. An emerging synchronization collection management planning capability is evaluated for application to surveillance using GMTI radar systems. Several examples are provided which illustrate the capability and benefits of synchronized collection management.","PeriodicalId":399150,"journal":{"name":"Proceedings of the Fifth International Conference on Information Fusion. FUSION 2002. (IEEE Cat.No.02EX5997)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121447341","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2002-07-08DOI: 10.1109/ICIF.2002.1020944
L. Sokol
The modern day challenges posed by terrorism and crime mean that we must make better use of the data we collect. We need to create a process that can transform different types of explicit and structured data into actionable knowledge. We need to be able to create unified infrastructure that will support a single query across all of the data sources. This unified infrastructure would allow us to integrate different collection stove pipes, including: text, structured data, images, faxes, audio, and video. To create this unified structure, we transform the collection stovepipes into sets of derived data that are integrated with structured data. Knowledge discovery tools are used over the entire set of collected data. This paper will present some of the components that we have been using to create knowledge infrastructures as well as the types of analysis required by our clients.
{"title":"Creating knowledge from heterogeneous data stove pipes","authors":"L. Sokol","doi":"10.1109/ICIF.2002.1020944","DOIUrl":"https://doi.org/10.1109/ICIF.2002.1020944","url":null,"abstract":"The modern day challenges posed by terrorism and crime mean that we must make better use of the data we collect. We need to create a process that can transform different types of explicit and structured data into actionable knowledge. We need to be able to create unified infrastructure that will support a single query across all of the data sources. This unified infrastructure would allow us to integrate different collection stove pipes, including: text, structured data, images, faxes, audio, and video. To create this unified structure, we transform the collection stovepipes into sets of derived data that are integrated with structured data. Knowledge discovery tools are used over the entire set of collected data. This paper will present some of the components that we have been using to create knowledge infrastructures as well as the types of analysis required by our clients.","PeriodicalId":399150,"journal":{"name":"Proceedings of the Fifth International Conference on Information Fusion. FUSION 2002. (IEEE Cat.No.02EX5997)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121369604","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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