2006 9th International Conference on Information Fusion最新文献_第5页

Identification of Simple Product-Form Plumes Using Networks of Sensors With Random Errors 用随机误差传感器网络识别简单产品型羽流

2006 9th International Conference on Information Fusion Pub Date : 2006-07-10 DOI: 10.1109/ICIF.2006.301769

N. Rao

{"title":"Identification of Simple Product-Form Plumes Using Networks of Sensors With Random Errors","authors":"N. Rao","doi":"10.1109/ICIF.2006.301769","DOIUrl":"https://doi.org/10.1109/ICIF.2006.301769","url":null,"abstract":"We consider a class of simple, idealized plumes which are specified by a product of injection and distance decay terms. The plume propagates with a constant velocity, and its distance term decays exponentially with respect to distance in a planar region. If the intensity sensors are error-free, the difference triangulation method can identify the origin of plume both in time and space within a specified precision. In our case, the sensors are subject to random, correlated errors with unknown distributions in measuring the plume intensity. The sensors are available or in place to conduct controlled experiments and collect measurements. We present a training method that utilizes the plume equation together with controlled sensor measurements to identify the plume's origin with distribution-free probabilistic performance guarantees. The training consists of utilizing the measurements to compute a suitable precision value for the difference triangulation method to account for sensor distributions. We present a distribution-free relationship between the training sample size and the precision and probability with which plume's origin is identified","PeriodicalId":248061,"journal":{"name":"2006 9th International Conference on Information Fusion","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130120748","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}

引用次数: 17

Benefits of relating the Retail Domain and Information Fusion 关联零售领域和信息融合的好处

2006 9th International Conference on Information Fusion Pub Date : 2006-07-10 DOI: 10.1109/ICIF.2006.301644

Tuwe Löfström, Rikard König, U. Johansson, L. Niklasson, Mattias Strand, T. Ziemke

引用次数: 5

Signature-Aided Air-to-Ground Video Tracking 签名辅助空对地视频跟踪

2006 9th International Conference on Information Fusion Pub Date : 2006-07-10 DOI: 10.1109/ICIF.2006.301820

P. Arambel, Jeffrey Silver, Matthew E. Antone, T. Strat

引用次数: 9

A Feature Level Fusion in Similarity Matching to Content-Based Image Retrieval 基于内容的图像检索相似度匹配的特征级融合

2006 9th International Conference on Information Fusion Pub Date : 2006-07-10 DOI: 10.1109/ICIF.2006.301664

Md. Mahmudur Rahman, B. Desai, P. Bhattacharya

引用次数: 19

MLPDA and MLPMHT Applied to Some MSTWG Data MLPDA和MLPMHT在一些MSTWG数据中的应用

2006 9th International Conference on Information Fusion Pub Date : 2006-07-10 DOI: 10.1109/ICIF.2006.301739

P. Willett, S. Coraluppi

引用次数: 36

Optimization of Distributed Detection Systems under Neyman-Pearson Criterion Neyman-Pearson准则下的分布式检测系统优化

2006 9th International Conference on Information Fusion Pub Date : 2006-07-10 DOI: 10.1109/ICIF.2006.301690

M. Xiang

引用次数: 1

Robust face tracking using colour Dempster-Shafer fusion and particle filter 使用颜色Dempster-Shafer融合和粒子滤波的鲁棒人脸跟踪

2006 9th International Conference on Information Fusion Pub Date : 2006-07-10 DOI: 10.1109/ICIF.2006.301713

Francis Faux, F. Luthon

{"title":"Robust face tracking using colour Dempster-Shafer fusion and particle filter","authors":"Francis Faux, F. Luthon","doi":"10.1109/ICIF.2006.301713","DOIUrl":"https://doi.org/10.1109/ICIF.2006.301713","url":null,"abstract":"This paper describes a real time face detection and tracking system. The method consists in modelling the skin face by a pixel fusion process of three colour sources within the framework of the Demster-Shafer theory. The algorithm is composed of two phases. In a simple and fast initialising stage, the user selects successively in an image, a shadowy, an overexposed and a zone of mean intensity of the face. Then the fusion process models the face skin colour. Next, on the video sequence, a tracking phase uses the key idea that the face exterior edges are well approximated as an ellipse including the skin colour blob resulting from the fusion process. As ellipse detection gets easily disturbed in cluttered environments by edges caused by non-face objects, a simple and fast efficient least squares method for ellipse fitting is used. The ellipse parameters are taken into account by a stochastic algorithm using a particle filter in order to realise a robust face tracking in position, size and pose. The originality of the method consists in modelling the face skin by a pixel fusion process of three independant cognitive colour sources. Moreover, mass sets are determined from a priori models taking into account contextual variables specific to the face under study. Hence, the face specificity which is to present shadowy (neck) and overexposed zones (nose, front) is considered, so that sensitivity to lighting conditions decreases. Results of face skin modelling, fusion, ellipse fitting and tracking are illustrated and discussed in this paper. The limits of the method and future work are also commented in conclusion","PeriodicalId":248061,"journal":{"name":"2006 9th International Conference on Information Fusion","volume":"70 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133141204","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}

引用次数: 17

Efficient Control of Information Flow for Distributed Multisensor Fusion Using Markov Decision Processes 基于马尔可夫决策过程的分布式多传感器融合信息流有效控制

2006 9th International Conference on Information Fusion Pub Date : 2006-07-10 DOI: 10.1109/ICIF.2006.301722

D. Akselrod, A. Sinha, C. V. Goldman, T. Kirubarajan

{"title":"Efficient Control of Information Flow for Distributed Multisensor Fusion Using Markov Decision Processes","authors":"D. Akselrod, A. Sinha, C. V. Goldman, T. Kirubarajan","doi":"10.1109/ICIF.2006.301722","DOIUrl":"https://doi.org/10.1109/ICIF.2006.301722","url":null,"abstract":"Network-centric multisensor-multitarget tracking has numerous advantages over single-sensor or single-platform tracking. In this paper, we present a solution to one of the main problems of network-centric tracking, namely, decentralized information sharing among the platforms participating in the distributed data fusion. This paper presents a decision mechanism that provides each platform with the required data for the distributed data fusion process while reducing redundancy in the information flow in the overall system. We consider a distributed data fusion system consisting of platforms that are decentralized, heterogeneous, and potentially unreliable. The proposed approach, which is based on Markov decision processes and decentralized lookup substrate, will control the information exchange process based, among the other parameters, on tracking performance metrics of individual platforms, thereby enhancing the whole distributed system's reliability as well as that of each participating platform. Simulation examples demonstrate the operation and the performance results of the system","PeriodicalId":248061,"journal":{"name":"2006 9th International Conference on Information Fusion","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133786213","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}

引用次数: 6

Reverse-Time Tracking to Enhance Passive Sonar 逆时跟踪增强被动声纳

2006 9th International Conference on Information Fusion Pub Date : 2006-07-10 DOI: 10.1109/ICIF.2006.301720

G. Mellema

引用次数: 5

Markov Regularization of Mixture of Latent variable Models for Multi-component Image Unsupervised Joint Reduction/Segmentatin 多分量图像无监督联合约简/分割中潜在变量混合模型的马尔可夫正则化

2006 9th International Conference on Information Fusion Pub Date : 2006-07-10 DOI: 10.1109/ICIF.2006.301667

F. Flitti, C. Collet

{"title":"Markov Regularization of Mixture of Latent variable Models for Multi-component Image Unsupervised Joint Reduction/Segmentatin","authors":"F. Flitti, C. Collet","doi":"10.1109/ICIF.2006.301667","DOIUrl":"https://doi.org/10.1109/ICIF.2006.301667","url":null,"abstract":"This paper is concerned with multi-component image segmentation which plays an important role in many imagery applications. Unfortunately, we are faced with the Hughes phenomenon when the number of components increases, and a space dimensionality reduction is often carried out as a preprocessing step before segmentation. An interesting solution is the mixtures of latent variable models which recover clusters in the observation structure and establish a local linear mapping on a reduced dimension space for each cluster. Thus, a globally nonlinear model is obtained to reduce dimensionality. Furthermore, a likelihood to each local model is often available which allows a well formulation of the mixture model and a maximum likelihood based decision for the clustering task. However for D-component images classification, such clustering, based only on the distance between observations in the D-dimensional space is not adapted since it neglects the observation spatial locations in the image. We propose to use a Markov a priori associated with such models to regularize D-dimensional pixel classification. Thus segmentation and reduction are performed simultaneously. In this paper, we focus on the probabilistic principal component analysis (PPCA) as latent model, and the hidden Markov quad-tree (HMT) as a Markov a priori","PeriodicalId":248061,"journal":{"name":"2006 9th International Conference on Information Fusion","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123060396","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}

引用次数: 0