A probabilistic algorithm is presented for finding a basis of the root space of a linearized polynomial L(x) = Sigma(t)(i=0) L(i)x(qi) over F(q)n. The expected time complexity of the presented algorithm is O (nt) oper...
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A probabilistic algorithm is presented for finding a basis of the root space of a linearized polynomial L(x) = Sigma(t)(i=0) L(i)x(qi) over F(q)n. The expected time complexity of the presented algorithm is O (nt) operations in F(q)n.
In this paper, we are going to answer the following question: assuming that we have estimates for the epipolar geometry and its uncertainty between two views, how probable is it that a new, independent point pair will...
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In this paper, we are going to answer the following question: assuming that we have estimates for the epipolar geometry and its uncertainty between two views, how probable is it that a new, independent point pair will satisfy the true epipolar geometry and be, in this sense, a feasible candidate correspondence pair? If we knew the true fundamental matrix, the answer would be trivial but in reality we do not know it because of estimation errors. So, as an independent point in the first view is given, we will show we may compute the point-probability-density function, termed as the epipolar pdf, for the epipolar line points in the second view that describes the current level of knowledge of the epipolar geometry between the views. This point-point-probability-density relation is a probabilistic form of the epipolar constraint that also approaches the true point-line relation as the number of training correspondences tends to infinity. In this paper, we will also show that the eigenvectors of the epipolar line covariance matrix have certain interpretations on the image plane, of which one is the previously observed, narrowest point of the epipolar envelope. The results of this paper are important since the uncertainty of the epipolar constraint can be now taken into account in a sound way in applications. (c) 2006 Elsevier B.V. All rights reserved.
In the near future, several radio access technologies will coexist in Beyond 3G mobile networks ( B3G), and they will be eventually transformed into one seamless global communication infrastructure. Self-managing syst...
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In the near future, several radio access technologies will coexist in Beyond 3G mobile networks ( B3G), and they will be eventually transformed into one seamless global communication infrastructure. Self-managing systems (i.e., those that self-configure, self-protect, self-heal, and self-optimize) are the solution to tackle the high complexity inherent to these networks. In this context, this paper proposes a system for autodiagnosis in the Radio Access Network ( RAN) of wireless systems. The malfunction of the RAN may be due not only to a hardware fault but also ( and more difficult to identify) to a bad configuration. The proposed system is based on the analysis of Key Performance Indicators ( KPIs) in order to isolate the cause of the network malfunction. In this paper, two alternative probabilistic systems are compared, which differ on how KPIs are modeled ( continuous or discrete variables). Experimental results are examined in order to support the theoretical concepts, based on data from a live network. The drawbacks and benefits of both systems are studied, and some conclusions on the scenarios under which each model should be used are presented.
This paper proposes a method for detecting object classes that exhibit variable shape structure in heavily cluttered images. The term "variable shape structure" is used to characterize object classes in whic...
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This paper proposes a method for detecting object classes that exhibit variable shape structure in heavily cluttered images. The term "variable shape structure" is used to characterize object classes in which some shape parts can be repeated an arbitrary number of times, some parts can be optional, and some parts can have several alternative appearances. Hidden State Shape Models (HSSMs), a generalization of Hidden Markov Models (HMMs), are introduced to model object classes of variable shape structure using a probabilistic framework. A polynomial inference algorithm automatically determines object location, orientation, scale, and structure by finding the globally optimal registration of model states with the image features, even in the presence of clutter. Experiments with real images demonstrate that the proposed method can localize objects of variable shape structure with high accuracy. For the task of hand shape localization and structure identification, the proposed method is significantly more accurate than previously proposed methods based on chamfer-distance matching. Furthermore, by integrating simple temporal constraints, the proposed method gains speed-ups of more than an order of magnitude and produces highly accurate results in experiments on nonrigid hand motion tracking.
The goal of online event analysis is to detect events and track their associated documents in real time from a continuous stream of documents generated by multiple information sources. Unlike traditional text categori...
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The goal of online event analysis is to detect events and track their associated documents in real time from a continuous stream of documents generated by multiple information sources. Unlike traditional text categorization methods, event analysis approaches consider the temporal relations among documents. However, such methods suffer from the threshold-dependency problem, so they only perform well for a narrow range of thresholds. In addition, if the contents of a document stream change, the optimal threshold (that is, the threshold that yields the best performance) often changes as well. In this paper, we propose a threshold-resilient online algorithm, called the Incremental probabilistic Latent Semantic Indexing (IPLSI) algorithm, which alleviates the threshold-dependency problem and simultaneously maintains the continuity of the latent semantics to better capture the story line development of events. The IPLSI algorithm is theoretically sound and empirically efficient and effective for event analysis. The results of the performance evaluation performed on the Topic Detection and Tracking (TDT)-4 corpus show that the algorithm reduces the cost of event analysis by as much as 15 percent similar to 20 percent and increases the acceptable threshold range by 200 percent to 300 percent over the baseline.
We present a novel probabilistic latent variable model to perform linear dimensionality reduction on data sets which contain clusters. We prove that the maximum likelihood solution of the model is an unsupervised gene...
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We present a novel probabilistic latent variable model to perform linear dimensionality reduction on data sets which contain clusters. We prove that the maximum likelihood solution of the model is an unsupervised generalization of linear discriminant analysis. This provides a completely new approach to one of the most established and widely used classification algorithms. The performance of the model is then demonstrated on a number of real and artificial data sets.
A multiple faces tracking system was presented based on Relevance Vector Machine (RVM) and Boosting learning. In this system, a face detector based on Boosting learning is used to detect faces at the first frame, and ...
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A multiple faces tracking system was presented based on Relevance Vector Machine (RVM) and Boosting learning. In this system, a face detector based on Boosting learning is used to detect faces at the first frame, and the face motion model and color model are created. The face motion model consists of a set of RVMs that learn the relationship between the motion of the face and its appearance, and the face color model is the 2D histogram of the face region in CrCb color space. In the tracking process different tracking methods (RVM tracking, local search, giving up tracking) are used according to different states of faces, and the states are changed according to the tracking results. When the full image search condition is satisfied, a full image search is started in order to find new coming faces and former occluded faces. In the full image search and local search, the similarity matrix is introduced to help matching faces efficiently. Experimental results demonstrate that this system can (a) automatically find new coming faces;(b) recover from occlusion, for example, if the faces are occluded by others and reappear or leave the scene and return;(c) run with a high computation efficiency, run at about 20 frames/s. (C) 2008 Elsevier Inc. All rights reserved.
This thesis is devoted to the analysis of a class ofiterative probabilistic algorithms in regular graphs, calledlocally greedy algorithms, which will provide bounds forgraph functions in regular graphs with large girt...
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This thesis is devoted to the analysis of a class ofiterative probabilistic algorithms in regular graphs, calledlocally greedy algorithms, which will provide bounds forgraph functions in regular graphs with large girth. This class isuseful because, by conveniently setting the parameters associatedwith it, we may derive algorithms for some well-known graphproblems, such as algorithms to find a large independent set, alarge induced forest, or even a small dominating set in an inputgraph G. The name ``locally greedy" comes from the fact that, inan algorithm of this class, the probability associated with therandom selection of a vertex v is determined by the currentstate of the vertices within some fixed distance of *** r > 2 and an r-regular graph G, we determine theexpected performance of a locally greedy algorithm in G,depending on the girth g of the input and on the degree r ofits vertices. When the girth of the graph is sufficiently large,this analysis leads to new lower bounds on the independence numberof G and on the maximum number of vertices in an induced forestin G, which, in both cases, improve the bounds previously *** also implies bounds on the same functions in graphs with largegirth and maximum degree r and in random regular graphs. As amatter of fact, the asymptotic lower bounds on the cardinality ofa maximum induced forest in a random regular graph improve earlierbounds, while, for independent sets, our bounds coincide withasymptotic lower bounds first obtained by Wormald. Our resultprovides an alternative proof of these bounds which avoids sharpconcentration *** main contribution of this work lies in the method presentedrather than in these particular new bounds. This method allows us,in some sense, to directly analyse prioritised algorithms inregular graphs, so that the class of locally greedy algorithms, orslight modifications thereof, may be applied to a wider range ofproblems in regular graphs with large girth
The ad hoc network localization problem deals with estimating the geographical location of all nodes in an ad hoc network, focusing on those nodes that do not have a direct way (for example, GPS) to determine their ow...
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The ad hoc network localization problem deals with estimating the geographical location of all nodes in an ad hoc network, focusing on those nodes that do not have a direct way (for example, GPS) to determine their own location. Proposed solutions to the ad hoc localization problem (AHLP) assume that nodes are capable of measuring received signal strength indication (RSSI) and/or are able to do coarse (sectoring) or fine signal angle-of-arrival (AoA) measurements. Existing algorithms exploit different aspects of such sensory data to provide either better localization accuracy or higher localization coverage. However, there is a need for a framework that could benefit from the interactions of nodes with mixed types of sensors. In this paper, we study the behavior of RSSI and AoA sensory data in the context of AHLP by using both geometric analysis and computer simulations. We show which type of sensor is better suited for which type of network scenario. We study how nodes using either, both, or none of these sensors could coexist in the same localization framework. We then provide a general particle-filtering framework, the first of its kind, that allows heterogeneity in the types of sensory data to solve the localization problem. We show that, when compared to localization scenarios where only one type of sensor is used, our framework provides significantly better localization results. Furthermore, our framework provides not only a location estimate for each nonanchor, but also an implicit confidence measure as to how accurate this estimate is. This confidence measure enables nodes to further improve on their location estimates using a local, iterative one-hop simple message exchange without having to rely on synchronized multiphase operations like in traditional multilateration methods.
In this paper, we investigate the use of brain activity for person authentication. It has been shown in previous studies that the brain-wave pattern of every individual is unique and that the electroencephalogram ( EE...
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In this paper, we investigate the use of brain activity for person authentication. It has been shown in previous studies that the brain-wave pattern of every individual is unique and that the electroencephalogram ( EEG) can be used for biometric identification. EEG-based biometry is an emerging research topic and we believe that it may open new research directions and applications in the future. However, very little work has been done in this area and was focusing mainly on person identification but not on person authentication. Person authentication aims to accept or to reject a person claiming an identity, i.e., comparing a biometric data to one template, while the goal of person identification is to match the biometric data against all the records in a database. We propose the use of a statistical framework based on Gaussian Mixture Models and Maximum A Posteriori model adaptation, successfully applied to speaker and face authentication, which can deal with only one training session. We perform intensive experimental simulations using several strict train/test protocols to show the potential of our method. We also show that there are some mental tasks that are more appropriate for person authentication than others.
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