@article {12440,
	title = {Example-Driven Manifold Priors for Image Deconvolution},
	journal = {Image Processing, IEEE Transactions on},
	volume = {20},
	year = {2011},
	month = {2011/11//},
	pages = {3086 - 3096},
	abstract = {Image restoration methods that exploit prior information about images to be estimated have been extensively studied, typically using the Bayesian framework. In this paper, we consider the role of prior knowledge of the object class in the form of a patch manifold to address the deconvolution problem. Specifically, we incorporate unlabeled image data of the object class, say natural images, in the form of a patch-manifold prior for the object class. The manifold prior is implicitly estimated from the given unlabeled data. We show how the patch-manifold prior effectively exploits the available sample class data for regularizing the deblurring problem. Furthermore, we derive a generalized cross-validation (GCV) function to automatically determine the regularization parameter at each iteration without explicitly knowing the noise variance. Extensive experiments show that this method performs better than many competitive image deconvolution methods.},
	keywords = {Bayesian, cross-validation, data;Bayes, deconvolution;image, determination;deblurring, estimation;, framework;GCV, function;automatic, function;image, image, image;patch-manifold, manifold, method;iteration, method;natural, methods;deconvolution;image, methods;natural, parameter, prior;generalized, prior;unlabeled, problem, regularization, regularization;example-driven, restoration, restoration;iterative, scenes;parameter},
	isbn = {1057-7149},
	doi = {10.1109/TIP.2011.2145386},
	author = {Ni,Jie and Turaga,P. and Patel, Vishal M. and Chellapa, Rama}
}
@conference {14830,
	title = {Localizing parts of faces using a consensus of exemplars},
	booktitle = {Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on},
	year = {2011},
	month = {2011/06//},
	pages = {545 - 552},
	abstract = {We present a novel approach to localizing parts in images of human faces. The approach combines the output of local detectors with a non-parametric set of global models for the part locations based on over one thousand hand-labeled exemplar images. By assuming that the global models generate the part locations as hidden variables, we derive a Bayesian objective function. This function is optimized using a consensus of models for these hidden variables. The resulting localizer handles a much wider range of expression, pose, lighting and occlusion than prior ones. We show excellent performance on a new dataset gathered from the internet and show that our localizer achieves state-of-the-art performance on the less challenging BioID dataset.},
	keywords = {Bayesian, faces;lighting;occlusion;pose;Bayes, function;exemplar, images;expression;face, localization;human, methods;face, objective, part, recognition;},
	doi = {10.1109/CVPR.2011.5995602},
	author = {Belhumeur,P. N. and Jacobs, David W. and Kriegman, D.J. and Kumar,N.}
}
@conference {12519,
	title = {Learning multi-modal densities on Discriminative Temporal Interaction Manifold for group activity recognition},
	booktitle = {Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on},
	year = {2009},
	month = {2009/06//},
	pages = {2450 - 2457},
	abstract = {While video-based activity analysis and recognition has received much attention, existing body of work mostly deals with single object/person case. Coordinated multi-object activities, or group activities, present in a variety of applications such as surveillance, sports, and biological monitoring records, etc., are the main focus of this paper. Unlike earlier attempts which model the complex spatial temporal constraints among multiple objects with a parametric Bayesian network, we propose a Discriminative Temporal Interaction Manifold (DTIM) framework as a data-driven strategy to characterize the group motion pattern without employing specific domain knowledge. In particular, we establish probability densities on the DTIM, whose element, the discriminative temporal interaction matrix, compactly describes the coordination and interaction among multiple objects in a group activity. For each class of group activity we learn a multi-modal density function on the DTIM. A Maximum a Posteriori (MAP) classifier on the manifold is then designed for recognizing new activities. Experiments on football play recognition demonstrate the effectiveness of the approach.},
	keywords = {a, activities;parametric, activity, analysis;belief, analysis;pattern, Bayesian, classification;image, classifier;multimodal, complex, constraints;data-driven, density, equipment;video, function;multiobject, interaction, manifold;discriminative, matrix;football, MOTION, network;video-based, networks;image, play, posteriori, processing;, recognition, recognition;group, recognition;maximum, signal, spatial, strategy;discriminative, temporal},
	doi = {10.1109/CVPR.2009.5206676},
	author = {Ruonan Li and Chellapa, Rama and Zhou,S. K}
}
@conference {12521,
	title = {Recognizing coordinated multi-object activities using a dynamic event ensemble model},
	booktitle = {Acoustics, Speech and Signal Processing, 2009. ICASSP 2009. IEEE International Conference on},
	year = {2009},
	month = {2009/04//},
	pages = {3541 - 3544},
	abstract = {While video-based activity analysis and recognition has received broad attention, existing body of work mostly deals with single object/person case. Modeling involving multiple objects and recognition of coordinated group activities, present in a variety of applications such as surveillance, sports, biological records, and so on, is the main focus of this paper. Unlike earlier attempts which model the complex spatial temporal constraints among different activities of multiple objects with a parametric Bayesian network, we propose a dynamic dasiaevent ensemblepsila framework as a data-driven strategy to characterize the group motion pattern without employing any specific domain knowledge. In particular, we exploit the Riemannian geometric property of the set of ensemble description functions and develop a compact representation for group activities on the ensemble manifold. An appropriate classifier on the manifold is then designed for recognizing new activities. Experiments on football play recognition demonstrate the effectiveness of the framework.},
	keywords = {activities, activity, analysis;Bayes, Bayesian, classification;video, description, ensemble, event, framework;dynamic, functions;ensemble, geometric, manifold;football, methods;geometry;image, model;ensemble, multiobject, network;video-based, play, processing;, property;classifier;coordinated, recognition;data-driven, recognition;parametric, Riemannian, signal, strategy;dynamic},
	doi = {10.1109/ICASSP.2009.4960390},
	author = {Ruonan Li and Chellapa, Rama}
}
@conference {13121,
	title = {Action recognition using ballistic dynamics},
	booktitle = {Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on},
	year = {2008},
	month = {2008/06//},
	pages = {1 - 8},
	abstract = {We present a Bayesian framework for action recognition through ballistic dynamics. Psycho-kinesiological studies indicate that ballistic movements form the natural units for human movement planning. The framework leads to an efficient and robust algorithm for temporally segmenting videos into atomic movements. Individual movements are annotated with person-centric morphological labels called ballistic verbs. This is tested on a dataset of interactive movements, achieving high recognition rates. The approach is also applied on a gesture recognition task, improving a previously reported recognition rate from 84\% to 92\%. Consideration of ballistic dynamics enhances the performance of the popular Motion History Image feature. We also illustrate the approachpsilas general utility on real-world videos. Experiments indicate that the method is robust to view, style and appearance variations.},
	keywords = {analysis;image, Bayesian, dynamics;gesture, feature;person-centric, framework;action, History, image, labels;psycho-kinesiological, morphological, MOTION, Movement, movements;motion, planning;interactive, processing;, recognition, recognition;ballistic, recognition;image, segmentation;video, signal, studies;image, task;human},
	doi = {10.1109/CVPR.2008.4587806},
	author = {Vitaladevuni,S.N. and Kellokumpu,V. and Davis, Larry S.}
}
@conference {13119,
	title = {Decentralized discovery of camera network topology},
	booktitle = {Distributed Smart Cameras, 2008. ICDSC 2008. Second ACM/IEEE International Conference on},
	year = {2008},
	month = {2008/09//},
	pages = {1 - 10},
	abstract = {One of the primary uses of camera networks is the observation and tracking of objects within some domain. Substantial research has gone into tracking objects within single and multiple views. However, few such approaches scale to large numbers of sensors, and those that do require an understanding of the network topology. Camera network topology models camera adjacency in the context of tracking: when an object/entity leaves one camera, which cameras could it appear at next? This paper presents a decentralized approach for estimating a camera networkpsilas topology based on sequential Bayesian estimation using a modified multinomial distribution. Central to this method is an information-theoretic appearance model for observation weighting. The distributed nature of the approach utilizes all of the sensors as processing agents in collectively recovering the network topology. Experimental results are presented using camera networks varying in size from 10-100 nodes.},
	keywords = {adjacency;camera, Bayesian, camera, discovery;modified, distribution;sequential, estimation;Bayes, methods;distributed, multinomial, network, sensors;, sensors;image, topology;decentralized},
	doi = {10.1109/ICDSC.2008.4635696},
	author = {Farrell,R. and Davis, Larry S.}
}
@conference {13139,
	title = {Hierarchical Part-Template Matching for Human Detection and Segmentation},
	booktitle = {Computer Vision, 2007. ICCV 2007. IEEE 11th International Conference on},
	year = {2007},
	month = {2007/10//},
	pages = {1 - 8},
	abstract = {Local part-based human detectors are capable of handling partial occlusions efficiently and modeling shape articulations flexibly, while global shape template-based human detectors are capable of detecting and segmenting human shapes simultaneously. We describe a Bayesian approach to human detection and segmentation combining local part-based and global template-based schemes. The approach relies on the key ideas of matching a part-template tree to images hierarchically to generate a reliable set of detection hypotheses and optimizing it under a Bayesian MAP framework through global likelihood re-evaluation and fine occlusion analysis. In addition to detection, our approach is able to obtain human shapes and poses simultaneously. We applied the approach to human detection and segmentation in crowded scenes with and without background subtraction. Experimental results show that our approach achieves good performance on images and video sequences with severe occlusion.},
	keywords = {analysis;global, approach;background, articulations;video, Bayesian, detection;human, detectors;hierarchical, detectors;partial, framework;Bayesian, human, likelihood, MAP, matching;human, matching;image, methods;image, occlusion, occlusions;shape, part-based, part-template, re-evaluation;global, segmentation;image, segmentation;local, sequences;, sequences;Bayes, SHAPE, subtraction;fine, template-based},
	doi = {10.1109/ICCV.2007.4408975},
	author = {Zhe Lin and Davis, Larry S. and David Doermann and DeMenthon,D.}
}
@conference {12594,
	title = {Joint Acoustic-Video Fingerprinting of Vehicles, Part II},
	booktitle = {Acoustics, Speech and Signal Processing, 2007. ICASSP 2007. IEEE International Conference on},
	volume = {2},
	year = {2007},
	month = {2007/04//},
	pages = {II-749 -II-752 - II-749 -II-752},
	abstract = {In this second paper, we first show how to estimate the wheelbase length of a vehicle using line metrology in video. We then address the vehicle fingerprinting problem using vehicle silhouettes and color invariants. We combine the acoustic metrology and classification results discussed in Part I with the video results to improve estimation performance and robustness. The acoustic video fusion is achieved in a Bayesian framework by assuming conditional independence of the observations of each modality. For the metrology density functions, Laplacian approximations are used for computational efficiency. Experimental results are given using field data},
	keywords = {acoustic-video, analysis;video, approximations;acoustic, Bayesian, colour, density, efficiency;joint, estimation;Bayes, fingerprinting;metrology, framework;Laplacian, functions;performance, fusion;color, identification;image, invariants;computational, methods;acoustic, metrology;acoustic, processing;, processing;fingerprint, signal, video},
	doi = {10.1109/ICASSP.2007.366344},
	author = {Cevher, V. and Guo, F. and Sankaranarayanan,A. C and Chellapa, Rama}
}
@conference {13142,
	title = {Learning Higher-order Transition Models in Medium-scale Camera Networks},
	booktitle = {Computer Vision, 2007. ICCV 2007. IEEE 11th International Conference on},
	year = {2007},
	month = {2007/10//},
	pages = {1 - 8},
	abstract = {We present a Bayesian framework for learning higher- order transition models in video surveillance networks. Such higher-order models describe object movement between cameras in the network and have a greater predictive power for multi-camera tracking than camera adjacency alone. These models also provide inherent resilience to camera failure, filling in gaps left by single or even multiple non-adjacent camera failures. Our approach to estimating higher-order transition models relies on the accurate assignment of camera observations to the underlying trajectories of objects moving through the network. We addresses this data association problem by gathering the observations and evaluating alternative partitions of the observation set into individual object trajectories. Searching the complete partition space is intractable, so an incremental approach is taken, iteratively adding observations and pruning unlikely partitions. Partition likelihood is determined by the evaluation of a probabilistic graphical model. When the algorithm has considered all observations, the most likely (MAP) partition is taken as the true object trajectories. From these recovered trajectories, the higher-order statistics we seek can be derived and employed for tracking. The partitioning algorithm we present is parallel in nature and can be readily extended to distributed computation in medium-scale smart camera networks.},
	keywords = {(artificial, approach;medium-scale, association, Bayesian, camera, cameras;video, framework;data, fusion;iterative, graphical, intelligence);optical, learning;incremental, likelihood;multicamera, likely, methods;higher, methods;learning, model, model;video, movement;probabilistic, network;Bayes, network;most, order, partition, problem;higher-order, statistics;higher-order, statistics;image, Surveillance, surveillance;, tracking;object, tracking;probability;video, transition},
	doi = {10.1109/ICCV.2007.4409203},
	author = {Farrell,R. and David Doermann and Davis, Larry S.}
}
@conference {13148,
	title = {Objects in Action: An Approach for Combining Action Understanding and Object Perception},
	booktitle = {Computer Vision and Pattern Recognition, 2007. CVPR {\textquoteright}07. IEEE Conference on},
	year = {2007},
	month = {2007/06//},
	pages = {1 - 8},
	abstract = {Analysis of videos of human-object interactions involves understanding human movements, locating and recognizing objects and observing the effects of human movements on those objects. While each of these can be conducted independently, recognition improves when interactions between these elements are considered. Motivated by psychological studies of human perception, we present a Bayesian approach which unifies the inference processes involved in object classification and localization, action understanding and perception of object reaction. Traditional approaches for object classification and action understanding have relied on shape features and movement analysis respectively. By placing object classification and localization in a video interpretation framework, we can localize and classify objects which are either hard to localize due to clutter or hard to recognize due to lack of discriminative features. Similarly, by applying context on human movements from the objects on which these movements impinge and the effects of these movements, we can segment and recognize actions which are either too subtle to perceive or too hard to recognize using motion features alone.},
	keywords = {analysis;image, approach;action, Bayesian, classification;image, classification;object, framework;Bayes, interactions;inference, interpretation, localization;object, methods;gesture, MOTION, movements;human, perception;, perception;human-object, perception;object, process;object, processing;visual, recognition;image, recognition;video, segmentation;action, segmentation;object, signal, understanding;human},
	doi = {10.1109/CVPR.2007.383331},
	author = {Gupta,A. and Davis, Larry S.}
}
@conference {13140,
	title = {Probabilistic Fusion Tracking Using Mixture Kernel-Based Bayesian Filtering},
	booktitle = {Computer Vision, 2007. ICCV 2007. IEEE 11th International Conference on},
	year = {2007},
	month = {2007/10//},
	pages = {1 - 8},
	abstract = {Even though sensor fusion techniques based on particle filters have been applied to object tracking, their implementations have been limited to combining measurements from multiple sensors by the simple product of individual likelihoods. Therefore, the number of observations is increased as many times as the number of sensors, and the combined observation may become unreliable through blind integration of sensor observations - especially if some sensors are too noisy and non-discriminative. We describe a methodology to model interactions between multiple sensors and to estimate the current state by using a mixture of Bayesian filters - one filter for each sensor, where each filter makes a different level of contribution to estimate the combined posterior in a reliable manner. In this framework, an adaptive particle arrangement system is constructed in which each particle is allocated to only one of the sensors for observation and a different number of samples is assigned to each sensor using prior distribution and partial observations. We apply this technique to visual tracking in logical and physical sensor fusion frameworks, and demonstrate its effectiveness through tracking results.},
	keywords = {(numerical, adaptive, arrangement, Bayesian, Filtering, filtering;multiple, filters;probabilistic, fusion, fusion;tracking;, integration;mixture, kernel-based, methods);sensor, methods;array, particle, processing;particle, sensors;object, signal, system;blind, techniques;visual, tracking;Bayes, tracking;particle, tracking;sensor},
	doi = {10.1109/ICCV.2007.4408938},
	author = {Han,Bohyung and Joo,Seong-Wook and Davis, Larry S.}
}
@conference {14862,
	title = {A Study of Face Recognition as People Age},
	booktitle = {Computer Vision, 2007. ICCV 2007. IEEE 11th International Conference on},
	year = {2007},
	month = {2007/10//},
	pages = {1 - 8},
	abstract = {In this paper we study face recognition across ages within a real passport photo verification task. First, we propose using the gradient orientation pyramid for this task. Discarding the gradient magnitude and utilizing hierarchical techniques, we found that the new descriptor yields a robust and discriminative representation. With the proposed descriptor, we model face verification as a two-class problem and use a support vector machine as a classifier. The approach is applied to two passport data sets containing more than 1,800 image pairs from each person with large age differences. Although simple, our approach outperforms previously tested Bayesian technique and other descriptors, including the intensity difference and gradient with magnitude. In addition, it works as well as two commercial systems. Second, for the first time, we empirically study how age differences affect recognition performance. Our experiments show that, although the aging process adds difficulty to the recognition task, it does not surpass illumination or expression as a confounding factor.},
	keywords = {Bayesian, difference;passport, machine;face, machines;, magnitude;gradient, orientation, photo, pyramid;hierarchical, recognition;face, recognition;support, representation;face, task;support, technique;discriminative, techniques;intensity, vector, verification, verification;gradient},
	doi = {10.1109/ICCV.2007.4409069},
	author = {Ling,Haibin and Soatto,S. and Ramanathan,N. and Jacobs, David W.}
}
@article {12603,
	title = {Face Verification Across Age Progression},
	journal = {Image Processing, IEEE Transactions on},
	volume = {15},
	year = {2006},
	month = {2006/11//},
	pages = {3349 - 3361},
	abstract = {Human faces undergo considerable amounts of variations with aging. While face recognition systems have been proven to be sensitive to factors such as illumination and pose, their sensitivity to facial aging effects is yet to be studied. How does age progression affect the similarity between a pair of face images of an individual? What is the confidence associated with establishing the identity between a pair of age separated face images? In this paper, we develop a Bayesian age difference classifier that classifies face images of individuals based on age differences and performs face verification across age progression. Further, we study the similarity of faces across age progression. Since age separated face images invariably differ in illumination and pose, we propose preprocessing methods for minimizing such variations. Experimental results using a database comprising of pairs of face images that were retrieved from the passports of 465 individuals are presented. The verification system for faces separated by as many as nine years, attains an equal error rate of 8.5\%},
	keywords = {age, Aging, Bayesian, classification;, classification;face, classifier;age, difference, effects;preprocessing, Face, image, images;error, methods;Bayes, methods;error, progression;age, rate;face, recognition, recognition;image, separated, statistics;face, systems;face, verification;facial},
	isbn = {1057-7149},
	doi = {10.1109/TIP.2006.881993},
	author = {Ramanathan,N. and Chellapa, Rama}
}
@conference {13198,
	title = {Kernel-based Bayesian filtering for object tracking},
	booktitle = {Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on},
	volume = {1},
	year = {2005},
	month = {2005/06//},
	pages = {227 - 234 vol. 1 - 227 - 234 vol. 1},
	abstract = {Particle filtering provides a general framework for propagating probability density functions in nonlinear and non-Gaussian systems. However, the algorithm is based on a Monte Carlo approach and sampling is a problematic issue, especially for high dimensional problems. This paper presents a new kernel-based Bayesian filtering framework, which adopts an analytic approach to better approximate and propagate density functions. In this framework, the techniques of density interpolation and density approximation are introduced to represent the likelihood and the posterior densities by Gaussian mixtures, where all parameters such as the number of mixands, their weight, mean, and covariance are automatically determined. The proposed analytic approach is shown to perform sampling more efficiently in high dimensional space. We apply our algorithm to real-time tracking problems, and demonstrate its performance on real video sequences as well as synthetic examples.},
	keywords = {approach;, approximation;, Bayes, Bayesian, Carlo, density, detection;, filtering;, functions;, Gaussian, interpolation;, kernel-based, methods;, mixtures;, Monte, nonGaussian, nonlinear, object, particle, probability, probability;, processes;, recognition;, sampling, sampling;, sequences;, system;, tracking;, video},
	doi = {10.1109/CVPR.2005.199},
	author = {Han,Bohyung and Zhu,Ying and Comaniciu, D. 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 {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 {12758,
	title = {Bayesian structure from motion using inertial information},
	booktitle = {Image Processing. 2002. Proceedings. 2002 International Conference on},
	volume = {3},
	year = {2002},
	month = {2002///},
	pages = {III-425 - III-428 vol.3 - III-425 - III-428 vol.3},
	abstract = {A novel approach to Bayesian structure from motion (SfM) using inertial information and sequential importance sampling (SIS) is presented. The inertial information is obtained from camera-mounted inertial sensors and is used in the Bayesian SfM approach as prior knowledge of the camera motion in the sampling algorithm. Experimental results using both synthetic and real images show that, when inertial information is used, more accurate results can be obtained or the same estimation accuracy can be obtained at a lower cost.},
	keywords = {3D, analysis;, Bayes, Bayesian, camera, estimation;, image, images;, importance, inertial, information;, methods;, MOTION, motion;, parameter, processing;, real, reconstruction;, sampling;, scene, sensors;, sequence;, sequences;, sequential, signal, structure-from-motion;, synthetic, systems;, video},
	doi = {10.1109/ICIP.2002.1038996},
	author = {Qian,Gang 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}
}
@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}
}
