@conference {12287,
	title = {Hierarchical routing with soft-state replicas in TerraDir},
	booktitle = {Parallel and Distributed Processing Symposium, 2004. Proceedings. 18th International},
	year = {2004},
	month = {2004/04//},
	pages = {48 - 48},
	abstract = {Summary form only given. Recent work on peer-to-peer systems has demonstrated the ability to deliver low latencies and good load balance when demand for data is relatively uniform. We describe an adaptive replication protocol that delivers low latencies, good load balance even when demand is heavily skewed. The protocol can withstand arbitrary and instantaneous changes in demand distribution. Our approach also addresses classical concerns related to topological constraints of asymmetrical namespaces, such as hierarchical bottlenecks in the context of hierarchical namespaces. The protocol replicates routing state in an ad-hoc manner based on profiled information, is lightweight, scalable, and requires no replica consistency guarantees.},
	keywords = {ad-hoc, adaptive, allocation;, asymmetrical, balancing;, bottlenecks;, consistency, constraints;, delivering;, demand, distribution;, guarantees;, hierarchical, latency, load, low, namespaces;, peer-to-peer, protocol;, protocols;, replica, replicas;, replication, resource, routing;, soft-state, systems;, TerraDir;, topological},
	doi = {10.1109/IPDPS.2004.1302967},
	author = {Silaghi,B. and Gopalakrishnan,Vijay and Bhattacharjee, Bobby and Kelcher,P.}
}
@conference {13216,
	title = {Iterative figure-ground discrimination},
	booktitle = {Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on},
	volume = {1},
	year = {2004},
	month = {2004/08//},
	pages = {67 - 70 Vol.1 - 67 - 70 Vol.1},
	abstract = {Figure-ground discrimination is an important problem in computer vision. Previous work usually assumes that the color distribution of the figure can be described by a low dimensional parametric model such as a mixture of Gaussians. However, such approach has difficulty selecting the number of mixture components and is sensitive to the initialization of the model parameters. In this paper, we employ non-parametric kernel estimation for color distributions of both the figure and background. We derive an iterative sampling-expectation (SE) algorithm for estimating the color, distribution and segmentation. There are several advantages of kernel-density estimation. First, it enables automatic selection of weights of different cues based on the bandwidth calculation from the image itself. Second, it does not require model parameter initialization and estimation. The experimental results on images of cluttered scenes demonstrate the effectiveness of the proposed algorithm.},
	keywords = {algorithm;, analysis;, Bandwidth, calculation;, Color, colour, Computer, density, dimensional, discrimination;, distribution;, distributions;, Estimation, estimation;, expectation, figure, Gaussian, ground, image, initialization;, iterative, Kernel, low, methods;, mixture;, model, model;, nonparametric, parameter, parametric, processes;, sampling, sampling;, segmentation, segmentation;, statistics;, theory;, vision;},
	doi = {10.1109/ICPR.2004.1334006},
	author = {Zhao, L. and Davis, Larry S.}
}
@conference {13625,
	title = {Product approximation by minimizing the upper bound of Bayes error rate for Bayesian combination of classifiers},
	booktitle = {Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on},
	volume = {1},
	year = {2004},
	month = {2004/08//},
	pages = {252 - 255 Vol.1 - 252 - 255 Vol.1},
	abstract = {In combining multiple classifiers using a Bayesian formalism, a high dimensional probability distribution is composed of a class and decisions of classifiers. In order to do product approximation of the probability distribution, the upper bound of Bayes error rate, bounded by the conditional entropy of a class and decisions, should be minimized. A second-order dependency-based product approximation is proposed in this paper by considering the second-order dependency between the class and decisions. The proposed method is evaluated by combining the classifiers recognizing unconstrained handwritten numerals.},
	keywords = {approximation;, Bayes, Bayesian, bound, character, classification;, classifiers;, conditional, distribution;, entropy;, error, formalism;, handwritten, methods;, minimisation;, multiple, numerals;, pattern, probability, probability;, Product, rate;, recognition;, statistics;, unconstrained, upper},
	doi = {10.1109/ICPR.2004.1334071},
	author = {Kang,Hee-Joong and David Doermann}
}
@conference {12729,
	title = {Activity recognition using the dynamics of the configuration of interacting objects},
	booktitle = {Computer Vision and Pattern Recognition, 2003. Proceedings. 2003 IEEE Computer Society Conference on},
	volume = {2},
	year = {2003},
	month = {2003/06//},
	pages = {II - 633-40 vol.2 - II - 633-40 vol.2},
	abstract = {Monitoring activities using video data is an important surveillance problem. A special scenario is to learn the pattern of normal activities and detect abnormal events from a very low resolution video where the moving objects are small enough to be modeled as point objects in a 2D plane. Instead of tracking each point separately, we propose to model an activity by the polygonal {\textquoteright}shape{\textquoteright} of the configuration of these point masses at any time t, and its deformation over time. We learn the mean shape and the dynamics of the shape change using hand-picked location data (no observation noise) and define an abnormality detection statistic for the simple case of a test sequence with negligible observation noise. For the more practical case where observation (point locations) noise is large and cannot be ignored, we use a particle filter to estimate the probability distribution of the shape given the noisy observations up to the current time. Abnormality detection in this case is formulated as a change detection problem. We propose a detection strategy that can detect both {\textquoteright}drastic{\textquoteright} and {\textquoteright}slow{\textquoteright} abnormalities. Our framework can be directly applied for object location data obtained using any type of sensors - visible, radar, infrared or acoustic.},
	keywords = {2D, abnormal, abnormality, abnormality;, acoustic, activity, analysis;, change;, Computer, configuration, configuration;, data;, DETECTION, detection;, distribution;, drastic, dynamics;, event;, filter;, hand-picked, image, infrared, interacting, learning;, location, low, mean, model;, monitoring;, MOTION, moving, noise;, noisy, object, object;, observation, observation;, particle, pattern, plane;, point, polygonal, probability, probability;, problem;, processing;, radar, recognition;, resolution, sensor;, sensors;, sequence;, SHAPE, shape;, signal, slow, statistic;, strategy;, Surveillance, surveillance;, target, test, tracking;, video, video;, visible, vision;},
	doi = {10.1109/CVPR.2003.1211526},
	author = {Vaswani, N. and RoyChowdhury, A. and Chellapa, Rama}
}
@conference {18169,
	title = {Anti-collusion of group-oriented fingerprinting},
	booktitle = {Multimedia and Expo, 2003. ICME {\textquoteright}03. Proceedings. 2003 International Conference on},
	volume = {2},
	year = {2003},
	month = {2003/07//},
	pages = {II - 217-20 vol.2 - II - 217-20 vol.2},
	abstract = {Digital fingerprinting of multimedia data involves embedding information in the content, and offers protection to the digital rights of the content by allowing illegitimate usage of the content to be identified by authorized parties. One potential threat to fingerprints is collusion, whereby a group of adversaries combine their individual copies in an attempt to remove the underlying fingerprints. Former studies indicate that collusion attacks based on a few dozen independent copies can confound a fingerprinting system that employs orthogonal modulation. However, since an adversary is more likely to collude with some users than other users, we propose a group-based fingerprinting scheme where users likely to collude with each other are assigned correlated fingerprints. We evaluate the performance of our group-based fingerprints by studying the collusion resistance of a fingerprinting system employing Gaussian distributed fingerprints. We compare the results to those of fingerprinting systems employing orthogonal modulation.},
	keywords = {anticollusion, attacks;, collusion, communication;, data;, digital, distributed, distribution;, fingerprinting;, fingerprints;, Gaussian, group-oriented, Internet;, method;, modulation;, multimedia, orthogonal, security;, Telecommunication, watermarking;},
	doi = {10.1109/ICME.2003.1221592},
	author = {Wang,Z.J. and M. Wu and Trappe,W. and Liu,K. J.R}
}
@conference {18258,
	title = {Nonlinear collusion attacks on independent fingerprints for multimedia},
	booktitle = {Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP {\textquoteright}03). 2003 IEEE International Conference on},
	volume = {5},
	year = {2003},
	month = {2003/04//},
	pages = {V - 664-7 vol.5 - V - 664-7 vol.5},
	abstract = {Digital fingerprinting is a technology for tracing the distribution of multimedia content and protecting them from unauthorized redistribution. Collusion attack is a cost effective attack against digital fingerprinting where several copies with the same content but different fingerprints are combined to remove the original fingerprints. In this paper, we investigate average and nonlinear collusion attacks of independent Gaussian fingerprints and study both their effectiveness and the perceptual quality. We also propose the bounded Gaussian fingerprints to improve the perceptual quality of the fingerprinted copies. We further discuss the tradeoff between the robustness against collusion attacks and the perceptual quality of a fingerprinting system.},
	keywords = {attacks;, average, bounded, collusion, computing;, content, copies;, digital, distribution;, fingerprinted, fingerprinting;, fingerprints;, Gaussian, independent, multimedia, nonlinear, perceptual, quality;, robustness;, watermarking;},
	doi = {10.1109/ICASSP.2003.1200058},
	author = {Zhao,Hong and M. Wu and Wang,Z.J. and Liu,K. J.R}
}
@conference {13248,
	title = {Probabilistic tracking in joint feature-spatial spaces},
	booktitle = {Computer Vision and Pattern Recognition, 2003. Proceedings. 2003 IEEE Computer Society Conference on},
	volume = {1},
	year = {2003},
	month = {2003/06//},
	pages = {I-781 - I-788 vol.1 - I-781 - I-788 vol.1},
	abstract = {In this paper, we present a probabilistic framework for tracking regions based on their appearance. We exploit the feature-spatial distribution of a region representing an object as a probabilistic constraint to track that region over time. The tracking is achieved by maximizing a similarity-based objective function over transformation space given a nonparametric representation of the joint feature-spatial distribution. Such a representation imposes a probabilistic constraint on the region feature distribution coupled with the region structure, which yields an appearance tracker that is robust to small local deformations and partial occlusion. We present the approach for the general form of joint feature-spatial distributions and apply it to tracking with different types of image features including row intensity, color and image gradient.},
	keywords = {analysis;, appearance, appearance;, color;, colour, Computer, constraint;, deformation;, detection;, distribution;, edge, estimation;, extraction;, feature, feature-spatial, feature;, function, gradient;, image, intensity;, joint, likelihood, local, maximization;, maximum, nonparametric, object, objective, occlusion;, optical, partial, probabilistic, probability;, region, representation;, row, similarity-based, small, space;, structure;, target, tracker;, tracking;, transformation, vision;},
	doi = {10.1109/CVPR.2003.1211432},
	author = {Elgammal,A. and Duraiswami, Ramani and Davis, Larry S.}
}
@conference {15096,
	title = {Toward secure key distribution in truly ad-hoc networks},
	booktitle = {Applications and the Internet Workshops, 2003. Proceedings. 2003 Symposium on},
	year = {2003},
	month = {2003/01//},
	pages = {342 - 346},
	abstract = {Ad-hoc networks - and in particular wireless mobile ad-hoc networks $have unique characteristics and constraints that make traditional cryptographic mechanisms and assumptions inappropriate. In particular it may not be warranted to assume pre-existing shared secrets between members of the network or the presence of a common PKI. Thus, the issue of key distribution in ad-hoc networks represents an important problem. Unfortunately, this issue has been largely ignored; as an example, most protocols for secure ad-hoc routing assume that key distribution has already taken place. Traditional key distribution schemes either do not apply in an ad-hoc scenario or are not efficient enough for small, resource-constrained devices. We propose to combine efficient techniques from identity-based (ID-based) and threshold cryptography to provide a mechanism that enables flexible and efficient key distribution while respecting the constraints of ad-hoc networks. We also discuss the available mechanisms and their suitability for the proposed task.},
	keywords = {ad, ad-hoc, cryptography;, distribution;, hoc, identity-based, key, mobile, network, networks;, routing;, secure, security;, Telecommunication, threshold, wireless},
	doi = {10.1109/SAINTW.2003.1210183},
	author = {Khalili,A. and Katz, Jonathan and Arbaugh, William A.}
}
@conference {12766,
	title = {Probabilistic recognition of human faces from video},
	booktitle = {Image Processing. 2002. Proceedings. 2002 International Conference on},
	volume = {1},
	year = {2002},
	month = {2002///},
	pages = {I-41 - I-44 vol.1 - I-41 - I-44 vol.1},
	abstract = {Most present face recognition approaches recognize faces based on still images. We present a novel approach to recognize faces in video. In that scenario, the face gallery may consist of still images or may be derived from a videos. For evidence integration we use classical Bayesian propagation over time and compute the posterior distribution using sequential importance sampling. The probabilistic approach allows us to handle uncertainties in a systematic manner. Experimental results using videos collected by NIST/USF and CMU illustrate the effectiveness of this approach in both still-to-video and video-to-video scenarios with appropriate model choices.},
	keywords = {Bayes, Bayesian, CMU;, distribution;, Face, faces;, gallery;, handling;, human, image, images;, importance, likelihood;, methods;, NIST/USF;, observation, posterior, probabilistic, probability;, processing;, propagation;, recognition;, sampling;, sequential, signal, still, Still-to-video, Uncertainty, video, Video-to-video},
	doi = {10.1109/ICIP.2002.1037954},
	author = {Chellapa, Rama and Kruger, V. and Zhou,Shaohua}
}
@conference {14855,
	title = {Stochastic completion fields: a neural model of illusory contour shape and salience},
	booktitle = {Computer Vision, 1995. Proceedings., Fifth International Conference on},
	year = {1995},
	month = {1995/06//},
	pages = {408 - 415},
	abstract = {We describe an algorithm and representation level theory of illusory contour shape and salience. Unlike previous theories, our model is derived from a single assumption-namely, that the prior probability distribution of boundary completion shape can be modeled by a random walk in a lattice whose points are positions and orientations in the image plane (i.e. the space which one can reasonably assume is represented by neurons of the mammalian visual cortex). Our model does not employ numerical relaxation or other explicit minimization, but instead relies on the fact that the probability that a particle following a random walk will pass through a given position and orientation on a path joining two boundary fragments can be computed directly as the product of two vector-field convolutions. We show that for the random walk we define, the maximum likelihood paths are curves of least energy, that is, on average, random walks follow paths commonly assumed to model the shape of illusory contours. A computer model is demonstrated on numerous illusory contour stimuli from the literature},
	keywords = {boundary, completion, computational, Computer, contour, contours;, convolutions;, cortex;, curves, detection;, distribution;, edge, energy;, estimation;, fields;, fragments;, geometry;, illusory, image, lattice;, least, likelihood, mammalian, maximum, model;, nets;, neural, of, paths;, plane;, probability, probability;, random, recognition;, shape;, stimuli;, Stochastic, vector-field, visual, walk;},
	doi = {10.1109/ICCV.1995.466910},
	author = {Williams,L. R and Jacobs, David W.}
}
