@article {18242,
	title = {Modeling and Analysis of Correlated Binary Fingerprints for Content Identification},
	journal = {Information Forensics and Security, IEEE Transactions on},
	volume = {6},
	year = {2011},
	month = {2011/09//},
	pages = {1146 - 1159},
	abstract = {Multimedia identification via content fingerprints is used in many applications, such as content filtering on user-generated content websites, and automatic multimedia identification and tagging. A compact $\#$x201C;fingerprint $\#$x201D; is computed for each multimedia signal that captures robust and unique properties of the perceptual content, which is later used for identifying the multimedia. Several different multimedia fingerprinting schemes have been proposed in the literature and have been evaluated through experiments. To complement these experimental evaluations and provide guidelines for choosing system parameters and designing better schemes, this paper develops models for content fingerprinting and provides an analysis of the identification performance under these models. As a first step, bounds on the identification accuracy and the required fingerprint length for the simplest case when the fingerprint bits are modeled as i.i.d. are summarized. Markov Random Fields are then used to address more realistic settings of fingerprints with correlated components. The optimal likelihood ratio detector is derived and a statistical physics inspired approach for computing the probability of detection and probability of false alarm is described. The analysis shows that the commonly used Hamming distance detection criterion is susceptible to correlations among fingerprint bits, whereas the optimal log-likelihood ratio decision rule yields 5-20\% improvement in the accuracy over a range of correlations. Simulation results demonstrate the validity of the theoretical predictions.},
	keywords = {alarm, approach;user, binary, computing;, content, criterion;Markov, decision, DETECTION, detector;statistical, distance, fields;content, filtering;content, filtering;multimedia, fingerprinting, fingerprints;detection, generated, Hamming, identification;correlated, identification;optimal, inspired, likelihood, Physics, probability;false, probability;log, processes;Web, random, ratio, rule;multimedia, schemes;multimedia, sites;information, Websites;Markov},
	isbn = {1556-6013},
	doi = {10.1109/TIFS.2011.2152394},
	author = {Varna,A.L. and M. Wu}
}
@conference {18244,
	title = {Modeling content fingerprints using markov random fields},
	booktitle = {Information Forensics and Security, 2009. WIFS 2009. First IEEE International Workshop on},
	year = {2009},
	month = {2009/12//},
	pages = {111 - 115},
	abstract = {Content fingerprints are widely employed for identifying multimedia in various applications. A {\^A}{\textquestiondown}fingerprint{\^A}{\textquestiondown} of a video or audio is a short signature that captures unique characteristics of the signal and can be used to perform robust identification. Several fingerprinting techniques have been proposed in the literature and are often evaluated using benchmark databases. To complement these experimental evaluations, this paper develops a theoretical model for content fingerprints and evaluates the identification accuracy. Fingerprints and the noise are modeled as Markov random fields and the optimal decision rule for matching is derived. An algorithm to compute the probability of correct detection and the false alarm rate by estimating the density of states is described. Numerical results are provided for a model of a block based binary fingerprinting scheme and the influence of the fingerprint correlation and the noise on the detection accuracy is studied.},
	keywords = {based, binary, correlation;multimedia;noise;optimal, data;, decision, field;block, fingerprint;fingerprint, fingerprinting;content, Markov, of, processes;multimedia, random, rule;Markov, systems;probability;security},
	doi = {10.1109/WIFS.2009.5386471},
	author = {Varna,A.L. and M. Wu}
}
@conference {13186,
	title = {Fast multiple object tracking via a hierarchical particle filter},
	booktitle = {Computer Vision, 2005. ICCV 2005. Tenth IEEE International Conference on},
	volume = {1},
	year = {2005},
	month = {2005/10//},
	pages = {212 - 219 Vol. 1 - 212 - 219 Vol. 1},
	abstract = {A very efficient and robust visual object tracking algorithm based on the particle filter is presented. The method characterizes the tracked objects using color and edge orientation histogram features. While the use of more features and samples can improve the robustness, the computational load required by the particle filter increases. To accelerate the algorithm while retaining robustness we adopt several enhancements in the algorithm. The first is the use of integral images for efficiently computing the color features and edge orientation histograms, which allows a large amount of particles and a better description of the targets. Next, the observation likelihood based on multiple features is computed in a coarse-to-fine manner, which allows the computation to quickly focus on the more promising regions. Quasi-random sampling of the particles allows the filter to achieve a higher convergence rate. The resulting tracking algorithm maintains multiple hypotheses and offers robustness against clutter or short period occlusions. Experimental results demonstrate the efficiency and effectiveness of the algorithm for single and multiple object tracking.},
	keywords = {(numerical, algorithm;, analysis;, Color, colour, Computer, Convergence, detection;, edge, fast, filter;, Filtering, hierarchical, histogram;, image, images;, integral, likelihood;, methods);, methods;, multiple, numerical, object, observation, of, orientation, particle, processes;, quasirandom, random, sampling;, tracking, tracking;, vision;, visual},
	doi = {10.1109/ICCV.2005.95},
	author = {Yang,Changjiang and Duraiswami, Ramani and Davis, Larry S.}
}
@conference {12686,
	title = {Robust Bayesian cameras motion estimation using random sampling},
	booktitle = {Image Processing, 2004. ICIP {\textquoteright}04. 2004 International Conference on},
	volume = {2},
	year = {2004},
	month = {2004/10//},
	pages = {1361 - 1364 Vol.2 - 1361 - 1364 Vol.2},
	abstract = {In this paper, we propose an algorithm for robust 3D motion estimation of wide baseline cameras from noisy feature correspondences. The posterior probability density function of the camera motion parameters is represented by weighted samples. The algorithm employs a hierarchy coarse-to-fine strategy. First, a coarse prior distribution of camera motion parameters is estimated using the random sample consensus scheme (RANSAC). Based on this estimate, a refined posterior distribution of camera motion parameters can then be obtained through importance sampling. Experimental results using both synthetic and real image sequences indicate the efficacy of the proposed algorithm.},
	keywords = {3D, baseline, Bayesian, CAMERAS, cameras;, coarse-to-fine, consensus, density, estimation;, feature, function;, hierarchy, image, images;, importance, matching;, MOTION, posterior, probability, probability;, processing;, random, RANSAC;, real, realistic, sample, sampling;, scheme;, sequences;, stereo, strategy;, synthetic, wide},
	doi = {10.1109/ICIP.2004.1419754},
	author = {Qian, G. and Chellapa, Rama and Qinfen Zheng}
}
@conference {13514,
	title = {Binarization of low quality text using a Markov random field model},
	booktitle = {Pattern Recognition, 2002. Proceedings. 16th International Conference on},
	volume = {3},
	year = {2002},
	month = {2002///},
	pages = {160 - 163 vol.3 - 160 - 163 vol.3},
	abstract = {Binarization techniques have been developed in the document analysis community for over 30 years and many algorithms have been used successfully. On the other hand, document analysis tasks are more and more frequently being applied to multimedia documents such as video sequences. Due to low resolution and lossy compression, the binarization of text included in the frames is a non-trivial task. Existing techniques work without a model of the spatial relationships in the image, which makes them less powerful. We introduce a new technique based on a Markov random field model of the document. The model parameters (clique potentials) are learned from training data and the binary image is estimated in a Bayesian framework. The performance is evaluated using commercial OCR software.},
	keywords = {analysis;, annealing;, Bayes, Bayesian, binarization;, computing;, distributions;, document, documents;, field;, Gibbs, image, low, Markov, method;, methods;, multimedia, optimization;, probability;, processes;, processing;, QUALITY, random, simulated, text},
	doi = {10.1109/ICPR.2002.1047819},
	author = {Wolf,C. and David Doermann}
}
@article {15022,
	title = {Scalable data parallel algorithms for texture synthesis using Gibbs random fields},
	journal = {Image Processing, IEEE Transactions on},
	volume = {4},
	year = {1995},
	month = {1995/10//},
	pages = {1456 - 1460},
	abstract = {This article introduces scalable data parallel algorithms for image processing. Focusing on Gibbs and Markov random field model representation for textures, we present parallel algorithms for texture synthesis, compression, and maximum likelihood parameter estimation, currently implemented on Thinking Machines CM-2 and CM-5. The use of fine-grained, data parallel processing techniques yields real-time algorithms for texture synthesis and compression that are substantially faster than the previously known sequential implementations. Although current implementations are on Connection Machines, the methodology presented enables machine-independent scalable algorithms for a number of problems in image processing and analysis},
	keywords = {algorithms;maximum, algorithms;parallel, algorithms;scalable, algorithms;texture, analysis;image, CM-2;Thinking, CM-5;fine-grained, compression;image, compression;texture, Connection, data, estimation;model, estimation;parallel, field;Thinking, fields;Markov, likelihood, machine, Machines;Gibbs, machines;random, Parallel, parameter, processes;, processes;data, processing;image, processing;machine-independent, random, representation;real-time, scalable, synthesis;Markov, texture;maximum},
	isbn = {1057-7149},
	doi = {10.1109/83.465111},
	author = {Bader, D.A. and JaJa, Joseph F. and Chellapa, Rama}
}
@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.}
}
@conference {14939,
	title = {An efficient parallel algorithm for channel routing},
	booktitle = {Computer Design: VLSI in Computers and Processors, 1990. ICCD {\textquoteright}90. Proceedings., 1990 IEEE International Conference on},
	year = {1990},
	month = {1990/09//},
	pages = {400 - 403},
	abstract = {The channel-routing of a set of multiterminal nets in the standard two-layer model is considered. The sequential algorithms based on the greedy strategy do not seem to be easily parallelizable. Proposed is an efficient parallel algorithm for routing channels with cyclic constraints. The algorithm runs in time <e1>O</e1>(<e1>n</e1><sup>2</sup>/<e1>p</e1>+log<sup>2</sup><e1>p</e1>), with <e1>p</e1> processors, on a shared memory parallel random access machine (PRAM) model where 1 les;<e1>p</e1> les;<e1>n</e1><sup>2</sup> radic; and <e1>n</e1> is the size of the input. An efficient adaptation of this algorithm on a Sequent Balance 21000 multiprocessor system is reported},
	keywords = {21000;channel, access, algorithm;sequential, algorithms;, algorithms;shared, balance, CAD;computational, complexity;parallel, Layout, machine;standard, memory, model;circuit, nets;parallel, Parallel, PRAM;Sequent, random, routing;multiprocessor, system;multiterminal, two-layer},
	doi = {10.1109/ICCD.1990.130261},
	author = {Krishnamurthy, S. and JaJa, Joseph F.}
}
