325979
Recursive propagation of correspondences with applications to the creation of virtual video
This paper is concerned with the efficient temporal propagation of correspondences between frames of two video sequences, an integral component of many video processing tasks. The main contribution is a framework for the recursive propagation of these correspondences. The propagation consists of a time update step and a measurement update step. The time update depends only on the dynamics of the rotating source cameras, while the measurement update can be tailored to any member of a general class of image correspondence algorithms. Using these results, the correspondence between points of each frame pair can be propagated and updated in a fraction of the time required to estimate correspondences anew at every frame. We discuss an application of the recursive correspondence propagation framework to the creation of virtual video. Previous virtual view algorithms have been used to generate synthetic video of a static scene, in which objects seem frozen in time. In contrast, the algorithms described here allow the creation of "true" virtual video, in the sense that the synthetic video evolves dynamically along with the scene. While virtual video is our motivating application, the recursive correspondence propagation framework applies to any two-camera video application in which correspondence is difficult and prohibitively time-consuming to estimate by processing frame pairs independently.
7606261
Rates of convergence of nearest neighbor estimation under arbitrary sampling
Rates of convergence for nearest neighbor estimation are established in a general framework in terms of metric covering numbers of the underlying space. The first result is to find explicit finite sample upper bounds for the classical independent and identically distributed (i.i.d.) random sampling problem in a separable metric space setting. The convergence rate is a function of the covering numbers of the support of the distribution. For example, for bounded subsets of R/sup r/, the convergence rate is O(1/n/sup 2/r/). The main result is to extend the problem to allow samples drawn from a completely arbitrary random process in a separable metric space and to examine the performance in terms of the individual sample sequences. The authors show that for every sequence of samples the asymptotic time-average of nearest neighbor risks equals twice the time-average of the conditional Bayes risks of the sequence. Finite sample upper bounds under arbitrary sampling are again obtained in terms of the covering numbers of the underlying space. In particular, for bounded subsets of R/sup r/ the convergence rate of the time-averaged risk is O(1/n/sup 2/r/). The authors then establish a consistency result for k/sub n/-nearest neighbor estimation under arbitrary sampling and prove a convergence rate matching established rates for i.i.d. sampling. Finally, they show how their arbitrary sampling results lead to some classical i.i.d. sampling results and in fact extend them to stationary sampling. The framework and results are quite general while the proof techniques are surprisingly elementary. >
6063474
PAC learning with generalized samples and an application to stochastic geometry
In this paper, we introduce an extension of the standard PAC learning model which allows the use of generalized samples. We view a generalized sample as a pair consisting of a functional on the concept class together with the value obtained by the functional operating on the unknown concept. It appears that this model can be applied to a number of problems in signal processing and geometric reconstruction to provide sample size bounds under a PAC criterion. We consider a specific application of the model to a problem of curve reconstruction, and discuss some connections with a result from stochastic geometry.
1752947
Learning decision rules for pattern classification under a family of probability measures
In this paper, uniformly consistent estimation (learnability) of decision rules for pattern classification under a family of probability measures is investigated. In particular, it is shown that uniform boundedness of the metric entropy of the class of decision rules is both necessary and sufficient for learnability under each of two conditions: (i) the family of probability measures is totally bounded, with respect to the total variation metric, and (ii) the family of probability measures contains an interior point, when equipped with the same metric. In particular, this shows that insofar as uniform consistency is concerned, when the family of distributions contains a total variation neighborhood, nothing is gained by this knowledge about the distribution. Then two sufficient conditions for learnability are presented. Specifically, it is shown that learnability with respect to each of a finite collection of families of probability measures implies learnability with respect to their union; also, learnability with respect to each of a finite number of measures implies learnability with respect to the convex hull of the corresponding families of uniformly absolutely continuous probability measures.
9782780
A paradigm for class identification problems
The following problem arises in many applications involving classification, identification, and inference. There is a set of objects X, and a particular x /spl isin/ X is chosen (unknown to us). Based on information obtained about x in a sequential manner, one wishes to decide whether x belongs to one class of objects A/sub 0/ or a different class of objects A/sub 1/. The authors study a general paradigm applicable to a broad range of problems of this type, which they refer to as problems of class identification or discernibility. They consider various types of information sequences, and various success criteria including discernibility in the limit, discernibility with a stopping criterion, uniform discernibility, and discernibility in the Cesaro sense. They consider decision rules both with and without memory. Necessary and sufficient conditions for discernibility are provided for each case in terms of separability conditions on the sets A/sub 0/ and A/sub 1/. They then show that for any sets A/sub 0/ and A/sub 1/, various types of separability can be achieved by allowing failure on appropriate sets of small measure. Applications to problems in language identification, system identification, and discrete geometry are discussed. >
7846439
Universal lossless source coding with the Burrows Wheeler Transform
The Burrows Wheeler transform (1994) is a reversible sequence transformation used in a variety of practical lossless source-coding algorithms. In each, the BWT is followed by a lossless source code that attempts to exploit the natural ordering of the BWT coefficients. BWT-based compression schemes are widely touted as low-complexity algorithms giving lossless coding rates better than those of the Ziv-Lempel codes (commonly known as LZ'77 and LZ'78) and almost as good as those achieved by prediction by partial matching (PPM) algorithms. To date, the coding performance claims have been made primarily on the basis of experimental results. This work gives a theoretical evaluation of BWT-based coding. The main results of this theoretical evaluation include: (1) statistical characterizations of the BWT output on both finite strings and sequences of length n /spl rarr/ /spl infin/, (2) a variety of very simple new techniques for BWT-based lossless source coding, and (3) proofs of the universality and bounds on the rates of convergence of both new and existing BWT-based codes for finite-memory and stationary ergodic sources. The end result is a theoretical justification and validation of the experimentally derived conclusions: BWT-based lossless source codes achieve universal lossless coding performance that converges to the optimal coding performance more quickly than the rate of convergence observed in Ziv-Lempel style codes and, for some BWT-based codes, within a constant factor of the optimal rate of convergence for finite-memory sources.
2979288
Transformations for write-all-with-collision model,
Dependable properties such as self-stabilization are crucial requirements in sensor networks. One way to achieve these properties is to utilize the vast literature on distributed systems where such self-stabilizing algorithms have been designed. Since these existing algorithms are designed in read/write model (or variations thereof), they cannot be directly applied in sensor networks. For this reason, we consider a new atomicity model, write all with collision (WAC), that captures the computations of sensor networks and focus on transformations from read/write model to WAC model and vice versa. We show that the transformation from WAC model to read/write model is stabilization preserving, and the transformation from read/write model to WAC model is stabilization preserving for timed systems. In the transformation from read/write model to WAC model, if the system is untimed (asynchronous) and processes are deterministic then under reasonable assumptions, we show that (1) the resulting program in WAC model can allow at most one process to execute, and (2) the resulting program in WAC model cannot be stabilizing.
14320923
Scalable Clustering for Large High-Dimensional Data Based on Data Summarization
Clustering large data sets with high dimensionality is a challenging data-mining task. This paper presents a framework to perform such a task efficiently. It is based on the notion of data space reduction, which finds high density areas, or dense cells, in the given feature space. The dense cells store summarized information of the data. A designated partitioning or hierarchical clustering algorithm can be used as the second step to find clusters based on the data summaries. Using Kmeans as an example, this paper presents GARDEN-Kmeans, which performs data space reduction using Gamma Region DENsity partition, and utilizes Kmeans to cluster the summarized information. The experimental study shows that GARDEN-Kmeans executes several orders of magnitude faster than basic Kmeans and the recursive bisection Kmeans algorithm of CLUTO, while producing comparable clustering quality
14751317
Reducing the cost of the critical path in secure multicast for dynamic groups
In this paper we focus on the problem of secure multicast in dynamic groups. In this problem, a group of users communicate using a shared key. Due to the dynamic nature of these groups, to preserve secrecy, it is necessary to change the group key whenever the group membership changes. While the group key, is being changed, the group communication needs to be interrupted until the rekeying is complete. This interruption is especially necessary if the rekeying is done because a user has left (or is removed). We split the rekeying cost into two parts: the cost of the critical path-where each user receives the new group key, and the cost of the non-critical path-where each user receives any other keys that it needs to obtain. We present two algorithms that show the trade off between the cost of the critical path and the cost of the non-critical path. We also compare our algorithms to previous algorithms and show that our algorithms reduce the cost of the critical path while keeping the total cost manageable.
38081004
A high-speed 32-bit parallel correlator for spread spectrum communication
This paper describes a high speed 32-bit pipelined digital parallel correlator implemented in a Lattice field programmable gate array (FPGA). The parallel correlator is of use in CDMA and spread spectrum transceivers for the continuous calculation of correlation between an incoming data stream with a PN sequence. The maximum frequency of operation of the FPGA based correlator is 87 MHz providing a throughput of one 32-bit correlation every 11.5 ns resulting in considerable improvement over commercially available correlators in terms of speed as well as number of bits. The high speed of operation of the correlator coupled with its ability to handle up to 32 chips per data bit alleviates the problem of low information bit rates due to signal encoding in spread spectrum communication systems. A CMOS integrated circuit implementation of the parallel correlator is presented.
1315797
Correctness of Component-Based Adaptation
Long running applications often need to adapt due to changing requirements or changing environment. Typically, such adaptation is performed by dynamically adding or removing components. In these types of adaptation, components are often added to or removed from multiple processes in the system. While techniques for such adaptations have been extensively discussed in the literature, there is a lack of systematic methods to ensure the correctness of dynamic adaptation. To redress this deficiency, in this paper, we propose a new method, based on the concept of proof lattice, for verifying correctness of dynamic adaptation in a distributed application. We use transitional-invariant lattice to verify correctness of adaptation. As an illustration of this method, we show how correctness of dynamic adaptation is obtained in the context of a message communication application.