@conference {13074,
	title = {A large-scale benchmark dataset for event recognition in surveillance video},
	booktitle = {Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on},
	year = {2011},
	month = {2011/06//},
	pages = {3153 - 3160},
	abstract = {We introduce a new large-scale video dataset designed to assess the performance of diverse visual event recognition algorithms with a focus on continuous visual event recognition (CVER) in outdoor areas with wide coverage. Previous datasets for action recognition are unrealistic for real-world surveillance because they consist of short clips showing one action by one individual [15, 8]. Datasets have been developed for movies [11] and sports [12], but, these actions and scene conditions do not apply effectively to surveillance videos. Our dataset consists of many outdoor scenes with actions occurring naturally by non-actors in continuously captured videos of the real world. The dataset includes large numbers of instances for 23 event types distributed throughout 29 hours of video. This data is accompanied by detailed annotations which include both moving object tracks and event examples, which will provide solid basis for large-scale evaluation. Additionally, we propose different types of evaluation modes for visual recognition tasks and evaluation metrics along with our preliminary experimental results. We believe that this dataset will stimulate diverse aspects of computer vision research and help us to advance the CVER tasks in the years ahead.},
	keywords = {algorithm;evaluation, CVER, databases;, databases;video, dataset;moving, event, metrics;large-scale, object, recognition, recognition;diverse, recognition;video, scenes;surveillance, surveillance;visual, tasks;computer, tracks;outdoor, video, video;computer, vision;continuous, vision;image, visual},
	doi = {10.1109/CVPR.2011.5995586},
	author = {Oh,Sangmin and Hoogs, A. and Perera,A. and Cuntoor, N. and Chen,Chia-Chih and Lee,Jong Taek and Mukherjee,S. and Aggarwal, JK and Lee,Hyungtae and Davis, Larry S. and Swears,E. and Wang,Xioyang and Ji,Qiang and Reddy,K. and Shah,M. and Vondrick,C. and Pirsiavash,H. and Ramanan,D. and Yuen,J. and Torralba,A. and Song,Bi and Fong,A. and Roy-Chowdhury, A. and Desai,M.}
}
@conference {14850,
	title = {Trainable 3D recognition using stereo matching},
	booktitle = {Computer Vision Workshops (ICCV Workshops), 2011 IEEE International Conference on},
	year = {2011},
	month = {2011///},
	pages = {625 - 631},
	abstract = {Stereo matching has been used for face recognition in the presence of pose variation. In this approach, stereo matching is used to compare two 2-D images based on correspondences that reflect the effects of viewpoint variation and allow for occlusion. We show how to use stereo matching to derive image descriptors that can be used to train a classifier. This improves face recognition performance, producing the best published results on the CMU PIE dataset. We also demonstrate that classification based on stereo matching can be used for general object classification in the presence of pose variation. In preliminary experiments we show promising results on the 3D object class dataset, a standard, challenging 3D classification data set.},
	keywords = {2D, 3D, class, classification, classification;image, data, dataset;CMU, dataset;face, descriptor;occlusion;pose, estimation;solid, image, image;3D, matching;pose, matching;trainable, modelling;stereo, object, PIE, processing;, recognition;face, recognition;image, set;3D, variation;stereo},
	doi = {10.1109/ICCVW.2011.6130301},
	author = {Castillo,C. D and Jacobs, David W.}
}
@article {12524,
	title = {Moving Object Verification in Airborne Video Sequences},
	journal = {Circuits and Systems for Video Technology, IEEE Transactions on},
	volume = {19},
	year = {2009},
	month = {2009/01//},
	pages = {77 - 89},
	abstract = {This paper presents an end-to-end system for moving object verification in airborne video sequences. Using a sample selection module, the system first selects frames from a short sequence and stores them in an exemplar database. To handle appearance change due to potentially large aspect angle variations, a homography-based view synthesis method is then used to generate a novel view of each image in the exemplar database at the same pose as the testing object in each frame of a testing video segment. A rotationally invariant color matcher and a spatial-feature matcher based on distance transforms are combined using a weighted average rule to compare the novel view and the testing object. After looping over all testing frames, the set of match scores is passed to a temporal analysis module to examine the behavior of the testing object, and calculate a final likelihood. Very good verification performance is achieved over thousands of trials for both color and infrared video sequences using the proposed system.},
	keywords = {airborne, database;homography-based, databases;, matcher;color, matcher;image, matching;image, method;infrared, object, sequences;color, sequences;distance, sequences;moving, sequences;video, synthesis, system;exemplar, transforms;end-to-end, verification;spatial-feature, video, view},
	isbn = {1051-8215},
	doi = {10.1109/TCSVT.2008.2009243},
	author = {Yue,Zhanfeng and Guarino, D. and Chellapa, Rama}
}
@conference {14849,
	title = {Visibility constraints on features of 3D objects},
	booktitle = {Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on},
	year = {2009},
	month = {2009/06//},
	pages = {1231 - 1238},
	abstract = {To recognize three-dimensional objects it is important to model how their appearances can change due to changes in viewpoint. A key aspect of this involves understanding which object features can be simultaneously visible under different viewpoints. We address this problem in an image-based framework, in which we use a limited number of images of an object taken from unknown viewpoints to determine which subsets of features might be simultaneously visible in other views. This leads to the problem of determining whether a set of images, each containing a set of features, is consistent with a single 3D object. We assume that each feature is visible from a disk of viewpoints on the viewing sphere. In this case we show the problem is NP-hard in general, but can be solved efficiently when all views come from a circle on the viewing sphere. We also give iterative algorithms that can handle noisy data and converge to locally optimal solutions in the general case. Our techniques can also be used to recover viewpoint information from the set of features that are visible in different images. We show that these algorithms perform well both on synthetic data and images from the COIL dataset.},
	keywords = {3D, algorithms;synthetic, complexity;iterative, constraints;computational, data;synthetic, dataset;NP-hard;image-based, features;COIL, framework;iterative, images;three-dimensional, methods;object, object, recognition;, recognition;viewing, sphere;visibility},
	doi = {10.1109/CVPR.2009.5206726},
	author = {Basri,R. and Felzenszwalb,P. F and Girshick,R. B and Jacobs, David W. and Klivans,C. J}
}
@conference {13117,
	title = {Multi-resolution Tracking in Space and Time},
	booktitle = {Computer Vision, Graphics Image Processing, 2008. ICVGIP {\textquoteright}08. Sixth Indian Conference on},
	year = {2008},
	month = {2008/12//},
	pages = {352 - 358},
	abstract = {This paper proposes efficient and robust methods for tracking a moving object at multiple spatial and temporal resolution levels. The efficiency comes from optimising the amounts of spatial and temporal data processed. The robustness results from multi-level coarse-to-fine state-space searching. Tracking across resolution levels incurs an accuracy-versus-speed trade-off. For example, tracking at higher resolutions incurs greater processing cost, while maintaining higher accuracy in estimating the position of the moving object. We propose a novel spatial multi-scale tracker that tracks at the optimal accuracy-versus-speed operating point. Next, we relax this requirement to propose a multi-resolution tracker that operates at a minimum acceptable performance level. Finally, we extend these ideas to a multi-resolution spatio-temporal tracker. We show results of extensive experimentation in support of the proposed approaches.},
	keywords = {analysis;image, coarse-to-fine, detection;, estimation;spatial, MOTION, moving, multiscale, object, resolution;object, searching;multiresolution, state-space, tracker;image, tracker;spatio-temporal, tracking;multilevel, tracking;position},
	doi = {10.1109/ICVGIP.2008.74},
	author = {Roy,S.D. and Tran,S.D. and Davis, Larry S. and Vikram,B.S.}
}
@conference {13137,
	title = {Robust Object Tracking with Regional Affine Invariant Features},
	booktitle = {Computer Vision, 2007. ICCV 2007. IEEE 11th International Conference on},
	year = {2007},
	month = {2007/10//},
	pages = {1 - 8},
	abstract = {We present a tracking algorithm based on motion analysis of regional affine invariant image features. The tracked object is represented with a probabilistic occupancy map. Using this map as support, regional features are detected and probabilistically matched across frames. The motion of pixels is then established based on the feature motion. The object occupancy map is in turn updated according to the pixel motion consistency. We describe experiments to measure the sensitivities of our approach to inaccuracy in initialization, and compare it with other approaches.},
	keywords = {affine, algorithm;feature, analysis;image, analysis;pixel, consistency;regional, detection;motion, detection;tracking;, extraction;image, feature, features;robust, invariant, matching;image, MOTION, object, resolution;object, tracking},
	doi = {10.1109/ICCV.2007.4408948},
	author = {Tran,Son and Davis, Larry S.}
}
@conference {13163,
	title = {Constraint Integration for Multiview Pose Estimation of Humans with Self-Occlusions},
	booktitle = {3D Data Processing, Visualization, and Transmission, Third International Symposium on},
	year = {2006},
	month = {2006/06//},
	pages = {900 - 907},
	abstract = {Detection of articulated objects such as humans is an important task in computer vision. We present a system that incorporates a variety of constraints in a unified multi- view framework to automatically detect humans in possibly crowded scenes. These constraints include the kinematic constraints, the occlusion of one part by another and the high correlation between the appearance of parts such as the two arms. The graphical structure (non-tree) obtained is optimized in a nonparametric belief propagation framework using prior based search.},
	keywords = {articulated, belief, constraint;multiview, detection;computer, detection;pose, estimation;, estimation;nonparametric, framework;self-occlusion;computer, graphics;computer, method;graphical, methods;object, object, pose, propagation, structure;kinematic, vision;correlation},
	doi = {10.1109/3DPVT.2006.45},
	author = {Gupta,A. and Mittal,A. and Davis, Larry S.}
}
@conference {12608,
	title = {Shape-Regulated Particle Filtering for Tracking Non-Rigid Objects},
	booktitle = {Image Processing, 2006 IEEE International Conference on},
	year = {2006},
	month = {2006/10//},
	pages = {2813 - 2816},
	abstract = {This paper presents an active contour based algorithm for tracking non-rigid objects in heavily cluttered scenes. We decompose the non-rigid contour tracking problem into three subproblems: 2D motion estimation, deformation detection, and shape regulation. First, we employ a particle filter to estimate the affine transform parameters between successive frames. Second, by using a dynamic object model, we generate a probabilistic map of deformation to reshape its contour. Finally, we project the updated model onto a trained shape subspace to constrain deformations to be within possible object appearances. Our experiments show that the proposed algorithm significantly improves the performance of the tracker},
	keywords = {(numerical, 2D, algorithm;affine, based, contour, detection;dynamic, detection;motion, detection;parameter, estimation;active, estimation;object, estimation;particle, Filtering, filtering;probabilistic, filters;, map;shape-regulation;affine, methods);tracking, model;nonrigid, MOTION, object, scenes;deformation, tracking;parameter, transform;cluttered, transforms;clutter;edge},
	doi = {10.1109/ICIP.2006.312993},
	author = {Jie Shao and Chellapa, Rama and Porikli, F.}
}
@conference {13188,
	title = {Detecting rotational symmetries},
	booktitle = {Computer Vision, 2005. ICCV 2005. Tenth IEEE International Conference on},
	volume = {2},
	year = {2005},
	month = {2005/10//},
	pages = {954 - 961 Vol. 2 - 954 - 961 Vol. 2},
	abstract = {We present an algorithm for detecting multiple rotational symmetries in natural images. Given an image, its gradient magnitude field is computed, and information from the gradients is spread using a diffusion process in the form of a gradient vector flow (GVF) field. We construct a graph whose nodes correspond to pixels in tire image, connecting points that are likely to be rotated versions of one another The n-cycles present in tire graph are made to vote for C<sub>n</sub> symmetries, their votes being weighted by the errors in transformation between GVF in the neighborhood of the voting points, and the irregularity of the n-sided polygons formed by the voters. The votes are accumulated at tire centroids of possible rotational symmetries, generating a confidence map for each order of symmetry. We tested the method with several natural images.},
	keywords = {axial, computational, detection;, field;, flow;, geometry;, gradient, graph, graph;, image, image;, magnitude, methods;, multiple, n-sided, object, polygons;, recognition;, rotational, symmetries;, symmetry;, theory;, tire, tyres;, vector},
	doi = {10.1109/ICCV.2005.71},
	author = {Shiv Naga Prasad,V. and Davis, Larry S.}
}
@conference {13194,
	title = {Extracting regions of symmetry},
	booktitle = {Image Processing, 2005. ICIP 2005. IEEE International Conference on},
	volume = {3},
	year = {2005},
	month = {2005/09//},
	pages = {III - 133-6 - III - 133-6},
	abstract = {This paper presents an approach for extending the normalized-cut (n-cut) segmentation algorithm to find symmetric regions present in natural images. We use an existing algorithm to quickly detect possible symmetries present in an image. The detected symmetries are then individually verified using the modified n-cut algorithm to eliminate spurious detections. The weights of the n-cut algorithm are modified so as to include both symmetric and spatial affinities. A global parameter is defined to model the tradeoff between spatial coherence and symmetry. Experimental results indicate that symmetric quality measure for a region segmented by our algorithm is a good indicator for the significance of the principal axis of symmetry.},
	keywords = {algorithm;, coherence;, DETECTION, detection;, elimination;, extraction;, feature, image, images;, natural, normalized-cut, object, region, segmentation, segmentation;, spatial, spurious, symmetry},
	doi = {10.1109/ICIP.2005.1530346},
	author = {Gupta,A. and Prasad,V. S.N and Davis, Larry S.}
}
@conference {13197,
	title = {Fast illumination-invariant background subtraction using two views: error analysis, sensor placement and applications},
	booktitle = {Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on},
	volume = {1},
	year = {2005},
	month = {2005/06//},
	pages = {1071 - 1078 vol. 1 - 1071 - 1078 vol. 1},
	abstract = {Background modeling and subtraction to detect new or moving objects in a scene is an important component of many intelligent video applications. Compared to a single camera, the use of multiple cameras leads to better handling of shadows, specularities and illumination changes due to the utilization of geometric information. Although the result of stereo matching can be used as the feature for detection, it has been shown that the detection process can be made much faster by a simple subtraction of the intensities observed at stereo-generated conjugate pairs in the two views. The methodology however, suffers from false and missed detections due to some geometric considerations. In this paper, we perform a detailed analysis of such errors. Then, we propose a sensor configuration that eliminates false detections. Algorithms are also proposed that effectively eliminate most detection errors due to missed detections, specular reflections and objects being geometrically close to the background. Experiments on several scenes illustrate the utility and enhanced performance of the proposed approach compared to existing techniques.},
	keywords = {analysis;, application;, background, cameras;, configuration;, DETECTION, detection;, error, error;, extraction;, false, feature, handling;, illumination-invariance;, image, intelligent, matching;, modeling;, object, placement;, processing;, sensor, sensors;, shadow, stereo, subtraction;, video},
	doi = {10.1109/CVPR.2005.155},
	author = {Lim,Ser-Nam and Mittal,A. and Davis, Larry S. and Paragios,N.}
}
@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 {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 {12665,
	title = {Moving Object Segmentation and Dynamic Scene Reconstruction Using Two Frames},
	booktitle = {Acoustics, Speech, and Signal Processing, 2005. Proceedings. (ICASSP {\textquoteright}05). IEEE International Conference on},
	volume = {2},
	year = {2005},
	month = {2005//18/23},
	pages = {705 - 708},
	keywords = {3D, analysis;, constraints;, dynamic, ego-motion, estimation;, flow, image, images;, independent, INTENSITY, least, mean, median, method;, methods;, model;, MOTION, motion;, moving, object, of, parallax, parallax;, parametric, processing;, reconstruction;, scene, segmentation;, signal, squares, squares;, static, structure;, subspace, surface, translational, two-frame, unconstrained, video},
	doi = {10.1109/ICASSP.2005.1415502},
	author = {Agrawala, Ashok K. and Chellapa, Rama}
}
@conference {14861,
	title = {Non-negative lighting and specular object recognition},
	booktitle = {Computer Vision, 2005. ICCV 2005. Tenth IEEE International Conference on},
	volume = {2},
	year = {2005},
	month = {2005/10//},
	pages = {1323 -1330 Vol. 2 - 1323 -1330 Vol. 2},
	abstract = {Recognition of specular objects is particularly difficult because their appearance is much more sensitive to lighting changes than that of Lambertian objects. We consider an approach in which we use a 3D model to deduce the lighting that best matches the model to the image. In this case, an important constraint is that incident lighting should be non-negative everywhere. In this paper, we propose a new method to enforce this constraint and explore its usefulness in specular object recognition, using the spherical harmonic representation of lighting. The method follows from a novel extension of Szego{\textquoteright}s eigenvalue distribution theorem to spherical harmonics, and uses semidefinite programming to perform a constrained optimization. The new method is faster as well as more accurate than previous methods. Experiments on both synthetic and real data indicate that the constraint can improve recognition of specular objects by better separating the correct and incorrect models},
	keywords = {and, distribution, eigenfunctions;image, eigenvalue, harmonic, Lambertian, lighting;nonnegative, lighting;semidefinite, matching;object, object, objects;Szego, optimization;incident, programming;specular, recognition;optimisation;, recognition;spherical, representation;eigenvalues, theorem;constrained},
	doi = {10.1109/ICCV.2005.168},
	author = {Shirdhonkar,S. and Jacobs, David W.}
}
@conference {13192,
	title = {Pedestrian classification from moving platforms using cyclic motion pattern},
	booktitle = {Image Processing, 2005. ICIP 2005. IEEE International Conference on},
	volume = {2},
	year = {2005},
	month = {2005/09//},
	pages = {II - 854-7 - II - 854-7},
	abstract = {This paper describes an efficient pedestrian detection system for videos acquired from moving platforms. Given a detected and tracked object as a sequence of images within a bounding box, we describe the periodic signature of its motion pattern using a twin-pendulum model. Then a principle gait angle is extracted in every frame providing gait phase information. By estimating the periodicity from the phase data using a digital phase locked loop (dPLL), we quantify the cyclic pattern of the object, which helps us to continuously classify it as a pedestrian. Past approaches have used shape detectors applied to a single image or classifiers based on human body pixel oscillations, but ours is the first to integrate a global cyclic motion model and periodicity analysis. Novel contributions of this paper include: i) development of a compact shape representation of cyclic motion as a signature for a pedestrian, ii) estimation of gait period via a feedback loop module, and iii) implementation of a fast online pedestrian classification system which operates on videos acquired from moving platforms.},
	keywords = {analysis;, angle;, body, classification;, compact, cyclic, DETECTION, detection;, digital, Feedback, Gait, human, image, information;, locked, loop, loop;, loops;, module;, MOTION, object, oscillations;, pattern;, pedestrian, phase, Pixel, principle, representation;, sequence;, sequences;, SHAPE, system;},
	doi = {10.1109/ICIP.2005.1530190},
	author = {Yang Ran and Qinfen Zheng and Weiss, I. and Davis, Larry S. and Abd-Almageed, Wael and Liang Zhao}
}
@conference {13193,
	title = {Robust observations for object tracking},
	booktitle = {Image Processing, 2005. ICIP 2005. IEEE International Conference on},
	volume = {2},
	year = {2005},
	month = {2005/09//},
	pages = {II - 442-5 - II - 442-5},
	abstract = {It is a difficult task to find an observation model that will perform well for long-term visual tracking. In this paper, we propose an adaptive observation enhancement technique based on likelihood images, which are derived from multiple visual features. The most discriminative likelihood image is extracted by principal component analysis (PCA) and incrementally updated frame by frame to reduce temporal tracking error. In the particle filter framework, the feasibility of each sample is computed using this most discriminative likelihood image before the observation process. Integral image is employed for efficient computation of the feasibility of each sample. We illustrate how our enhancement technique contributes to more robust observations through demonstrations.},
	keywords = {(numerical, adaptive, analysis;, component, enhancement;, filter, Filtering, framework;, image, images;, likelihood, methods);, object, observation, particle, PCA;, principal, tracking;},
	doi = {10.1109/ICIP.2005.1530087},
	author = {Han,Bohyung and Davis, Larry S.}
}
@conference {12652,
	title = {Tracking objects in video using motion and appearance models},
	booktitle = {Image Processing, 2005. ICIP 2005. IEEE International Conference on},
	volume = {2},
	year = {2005},
	month = {2005/09//},
	pages = {II - 394-7 - II - 394-7},
	abstract = {This paper proposes a visual tracking algorithm that combines motion and appearance in a statistical framework. It is assumed that image observations are generated simultaneously from a background model and a target appearance model. This is different from conventional appearance-based tracking, that does not use motion information. The proposed algorithm attempts to maximize the likelihood ratio of the tracked region, derived from appearance and background models. Incorporation of motion in appearance based tracking provides robust tracking, even when the target violates the appearance model. We show that the proposed algorithm performs well in tracking targets efficiently over long time intervals.},
	keywords = {algorithm;, analysis;, appearance, background, estimation;, image, likelihood, maximum, model;, models;, MOTION, object, processing;, signal, target, tracking, tracking;, video, visual},
	doi = {10.1109/ICIP.2005.1530075},
	author = {Sankaranarayanan,A. C and Chellapa, Rama and Qinfen Zheng}
}
@conference {12703,
	title = {Appearance-based tracking and recognition using the 3D trilinear tensor},
	booktitle = {Acoustics, Speech, and Signal Processing, 2004. Proceedings. (ICASSP {\textquoteright}04). IEEE International Conference on},
	volume = {3},
	year = {2004},
	month = {2004/05//},
	pages = {iii - 613-16 vol.3 - iii - 613-16 vol.3},
	abstract = {The paper presents an appearance-based adaptive algorithm for simultaneous tracking and recognition by generalizing the transformation model to 3D perspective transformation. A trilinear tensor operator is used to represent the 3D geometrical structure. The tensor is estimated by predicting the corresponding points using the existing affine-transformation based algorithm. The estimated tensor is used to synthesize novel views to update the appearance templates. Some experimental results using airborne video are presented.},
	keywords = {3D, adaptive, affine-transformation, airborne, algorithm;, appearance, appearance-based, based, estimation;, geometrical, image, mathematical, novel, object, operator;, operators;, perspective, prediction;, processing;, recognition;, representation;, signal, structure, synthesis;, template, tensor, tensor;, tensors;, tracking;, transformation;, trilinear, updating;, video, video-based, video;, view},
	doi = {10.1109/ICASSP.2004.1326619},
	author = {Jie Shao and Zhou,S. K and Chellapa, Rama}
}
@conference {12693,
	title = {Classification probability analysis of principal component space analysis},
	booktitle = {Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on},
	volume = {1},
	year = {2004},
	month = {2004/08//},
	pages = {240 - 243 Vol.1 - 240 - 243 Vol.1},
	abstract = {In a previous paper, we have presented a new linear classification algorithm, principal component space analysis (PCNSA) which is designed for problems like object recognition where different classes have unequal and non-white noise covariance matrices. PCNSA first obtains a principal components space (PCA space) for the entire data and in this PCA space, it finds for each class "i", an M<sub>i</sub> dimensional subspace along which the class{\textquoteright}s intra-class variance is the smallest. We call this subspace an approximate space (ANS) since the lowest variance is usually "much smaller" than the highest. A query is classified into class "i" if its distance from the class{\textquoteright}s mean in the class{\textquoteright}s ANS is a minimum. In this paper, we discuss the PCNSA algorithm more precisely and derive tight upper bounds on its classification error probability. We use these expressions to compare classification performance of PCNSA with that of subspace linear discriminant analysis (SLDA).},
	keywords = {algorithm;, analysis;, approximate, classification, classification;, component, covariance, discriminant, error, intraclass, linear, matrices;, matrix;, noise;, nonwhite, object, pattern, PCA;, principal, probability;, recognition;, space, space;, statistics;, subspace, variance;},
	doi = {10.1109/ICPR.2004.1334068},
	author = {Vaswani, N. and Chellapa, Rama}
}
@conference {13212,
	title = {A fine-structure image/video quality measure using local statistics},
	booktitle = {Image Processing, 2004. ICIP {\textquoteright}04. 2004 International Conference on},
	volume = {5},
	year = {2004},
	month = {2004/10//},
	pages = {3535 - 3538 Vol. 5 - 3535 - 3538 Vol. 5},
	abstract = {An objective no-reference measure is presented to assess line-structure image/video quality. It was designed to measure image/video quality for video surveillance applications, especially for background modeling and foreground object detection. The proposed measure using local statistics reflects image degradation well in terms of noise and blur. The experimental results on a background subtraction algorithm validate the usefulness of the proposed measure, by showing its correlation with the algorithm{\textquoteright}s performance.},
	keywords = {algorithm;, background, degradation;, detection;, foreground, image, line-structure, local, measure;, modeling;, no-reference, object, objective, processing;, QUALITY, signal, statistics;, subtraction, surveillance;, video},
	doi = {10.1109/ICIP.2004.1421879},
	author = {Kim,Kyungnam and Davis, Larry S.}
}
@conference {13214,
	title = {Object tracking by adaptive feature extraction},
	booktitle = {Image Processing, 2004. ICIP {\textquoteright}04. 2004 International Conference on},
	volume = {3},
	year = {2004},
	month = {2004/10//},
	pages = {1501 - 1504 Vol. 3 - 1501 - 1504 Vol. 3},
	abstract = {Tracking objects in the high-dimensional feature space is not only computationally expensive but also functionally inefficient. Selecting a low-dimensional discriminative feature set is a critical step to improve tracker performance. A good feature set for tracking can differ from frame to frame due to the changes in the background against the tracked object, and due to an on-line algorithm that adaptively determines a advantageous distinctive feature set. In this paper, multiple heterogeneous features are assembled, and likelihood images are constructed for various subspaces of the combined feature space. Then, the most discriminative feature is extracted by principal component analysis (PCA) based on those likelihood images. This idea is applied to the mean-shift tracking algorithm [D. Comaniciu et al., June 2000], and we demonstrate its effectiveness through various experiments.},
	keywords = {adaptive, algorithm;, analysis;, colour, component, extraction;, feature, feature;, heterogeneous, image, image;, likelihood, Mean-shift, object, online, principal, tracking, tracking;},
	doi = {10.1109/ICIP.2004.1421349},
	author = {Han,Bohyung and Davis, Larry S.}
}
@conference {12695,
	title = {Probabilistic identity characterization for face recognition},
	booktitle = {Computer Vision and Pattern Recognition, 2004. CVPR 2004. Proceedings of the 2004 IEEE Computer Society Conference on},
	volume = {2},
	year = {2004},
	month = {2004/07/02/june},
	pages = {II-805 - II-812 Vol.2 - II-805 - II-812 Vol.2},
	abstract = {We present a general framework for characterizing the object identity in a single image or a group of images with each image containing a transformed version of the object, with applications to face recognition. In terms of the transformation, the group is made of either many still images or frames of a video sequence. The object identity is either discrete- or continuous-valued. This probabilistic framework integrates all the evidence of the set and handles the localization problem, illumination and pose variations through subspace identity encoding. Issues and challenges arising in this framework are addressed and efficient computational schemes are presented. Good face recognition results using the PIE database are reported.},
	keywords = {characterization;, database, database;, encoding;, Face, identity, identity;, image, localization, management, object, PIE, probabilistic, problem;, recognition;, sequence;, sequences;, subspace, systems;, video},
	doi = {10.1109/CVPR.2004.1315247},
	author = {Zhou,S. K and Chellapa, Rama}
}
@conference {12705,
	title = {Vehicle detection and tracking using acoustic and video sensors},
	booktitle = {Acoustics, Speech, and Signal Processing, 2004. Proceedings. (ICASSP {\textquoteright}04). IEEE International Conference on},
	volume = {3},
	year = {2004},
	month = {2004/05//},
	pages = {iii - 793-6 vol.3 - iii - 793-6 vol.3},
	abstract = {Multimodal sensing has attracted much attention in solving a wide range of problems, including target detection, tracking, classification, activity understanding, speech recognition, etc. In surveillance applications, different types of sensors, such as video and acoustic sensors, provide distinct observations of ongoing activities. We present a fusion framework using both video and acoustic sensors for vehicle detection and tracking. In the detection phase, a rough estimate of target direction-of-arrival (DOA) is first obtained using acoustic data through beam-forming techniques. This initial DOA estimate designates the approximate target location in video. Given the initial target position, the DOA is refined by moving target detection using the video data. Markov chain Monte Carlo techniques are then used for joint audio-visual tracking. A novel fusion approach has been proposed for tracking, based on different characteristics of audio and visual trackers. Experimental results using both synthetic and real data are presented. Improved tracking performance has been observed by fusing the empirical posterior probability density functions obtained using both types of sensors.},
	keywords = {acoustic, applications;, audio, audio-visual, beam-forming, Carlo, chain, density, detection;, direction-of-arrival, DOA, empirical, estimation;, framework;, functions;, fusion, fusion;, joint, Markov, methods;, Monte, moving, multimodal, object, optical, posterior, probability, probability;, processes;, processing;, sensing;, sensor, sensors;, signal, Surveillance, surveillance;, systems;, target, techniques;, tracking;, vehicle, video},
	doi = {10.1109/ICASSP.2004.1326664},
	author = {Chellapa, Rama and Qian,Gang and Qinfen Zheng}
}
@conference {12697,
	title = {View independent human body pose estimation from a single perspective image},
	booktitle = {Computer Vision and Pattern Recognition, 2004. CVPR 2004. Proceedings of the 2004 IEEE Computer Society Conference on},
	volume = {2},
	year = {2004},
	month = {2004/07/02/june},
	pages = {II-16 - II-22 Vol.2 - II-16 - II-22 Vol.2},
	abstract = {Recovering the 3D coordinates of various joints of the human body from an image is a critical first step for several model-based human tracking and optical motion capture systems. Unlike previous approaches that have used a restrictive camera model or assumed a calibrated camera, our work deals with the general case of a perspective uncalibrated camera and is thus well suited for archived video. The input to the system is an image of the human body and correspondences of several body landmarks, while the output is the set of 3D coordinates of the landmarks in a body-centric coordinate system. Using ideas from 3D model based invariants, we set up a polynomial system of equations in the unknown head pitch, yaw and roll angles. If we are able to make the often-valid assumption that the torso twist is small, there are finite numbers of solutions to the head-orientation that can be computed readily. Once the head orientation is computed, the epipolar geometry of the camera is recovered, leading to solutions to the 3D joint positions. Results are presented on synthetic and real images.},
	keywords = {3D, analysis;, biomechanics;, body, body-centric, camera;, capture, coordinate, coordinates;, detection;, epipolar, equation, estimation;, geometry;, human, image, image;, images;, model-based, models;, MOTION, object, optical, perspective, physiological, polynomial, polynomials;, pose, real, single, synthetic, system;, systems;, torso, tracking;, twist;, uncalibrated},
	doi = {10.1109/CVPR.2004.1315139},
	author = {Parameswaran, V. and Chellapa, Rama}
}
@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 {12721,
	title = {An appearance based approach for human and object tracking},
	booktitle = {Image Processing, 2003. ICIP 2003. Proceedings. 2003 International Conference on},
	volume = {2},
	year = {2003},
	month = {2003/09//},
	pages = {II - 85-8 vol.3 - II - 85-8 vol.3},
	abstract = {A system for tracking humans and detecting human-object interactions in indoor environments is described. A combination of correlogram and histogram information is used to model object and human color distributions. Humans and objects are detected using a background subtraction algorithm. The models are built on the fly and used to track them on a frame by frame basis. The system is able to detect when people merge into groups and segment them during occlusion. Identities are preserved during the sequence, even if a person enters and leaves the scene. The system is also able to detect when a person deposits or removes an object from the scene. In the first case the models are used to track the object retroactively in time. In the second case the objects are tracked for the rest of the sequence. Experimental results using indoor video sequences are presented.},
	keywords = {algorithm;, analysis;, background, basis;, by, Color, colour, correlogram, detection;, distributions;, frame, histogram, human, image, information;, object, processing;, segmentation;, sequences;, signal, subtraction, tracking;, video},
	doi = {10.1109/ICIP.2003.1246622},
	author = {Capellades,M. B and David Doermann and DeMenthon,D. and Chellapa, Rama}
}
@article {12301,
	title = {Deno: a decentralized, peer-to-peer object-replication system for weakly connected environments},
	journal = {Computers, IEEE Transactions on},
	volume = {52},
	year = {2003},
	month = {2003/07//},
	pages = {943 - 959},
	abstract = {This paper presents the design, implementation, and evaluation of the replication framework of Deno, a decentralized, peer-to-peer object-replication system targeted for weakly connected environments. Deno uses weighted voting for availability and pair-wise, epidemic information flow for flexibility. This combination allows the protocols to operate with less than full connectivity, to easily adapt to changes in group membership, and to make few assumptions about the underlying network topology. We present two versions of Deno{\textquoteright}s protocol that differ in the consistency levels they support. We also propose security extensions to handle a class of malicious actions that involve misrepresentation of protocol information. Deno has been implemented and runs on top of Linux and Win32 platforms. We use the Deno prototype to characterize the performance of the Deno protocols and extensions. Our study reveals several interesting results that provide fundamental insight into the benefits of decentralization and the mechanics of epidemic protocols.},
	keywords = {actions;, connected, consistency, data, data;, databases;, decentralized, Deno;, distributed, environments;, epidemic, group, levels;, Linux;, malicious, management;, membership;, network, object, object-replication, of, operating, peer-to-peer, protocols;, replicated, replication;, Security, security;, synchronisation;, system;, systems;, topology;, Unix;, voting;, weakly, weighted, Win32;},
	isbn = {0018-9340},
	doi = {10.1109/TC.2003.1214342},
	author = {Cetintemel,U. and Keleher,P. J and Bhattacharjee, Bobby and Franklin,M.J.}
}
@conference {13243,
	title = {Human body pose estimation using silhouette shape analysis},
	booktitle = {Proceedings. IEEE Conference on Advanced Video and Signal Based Surveillance, 2003.},
	year = {2003},
	month = {2003/07//},
	pages = {263 - 270},
	abstract = {We describe a system for human body pose estimation from multiple views that is fast and completely automatic. The algorithm works in the presence of multiple people by decoupling the problems of pose estimation of different people. The pose is estimated based on a likelihood function that integrates information from multiple views and thus obtains a globally optimal solution. Other characteristics that make our method more general than previous work include: (1) no manual initialization; (2) no specification of the dimensions of the 3D structure; (3) no reliance on some learned poses or patterns of activity; (4) insensitivity to edges and clutter in the background and within the foreground. The algorithm has applications in surveillance and promising results have been obtained.},
	keywords = {3D, analysis;, body, classification;, clutter;, detection;, estimation;, extraction;, feature, function;, human, image, likelihood, multiple, object, parameter, parameters;, Pixel, pose, probability;, segmentation;, segmentations;, SHAPE, silhouette, structure;, surveillance;, views;},
	doi = {10.1109/AVSS.2003.1217930},
	author = {Mittal,A. and Liang Zhao and Davis, Larry S.}
}
@conference {13244,
	title = {Image-based pan-tilt camera control in a multi-camera surveillance environment},
	booktitle = {Multimedia and Expo, 2003. ICME {\textquoteright}03. Proceedings. 2003 International Conference on},
	volume = {1},
	year = {2003},
	month = {2003/07//},
	pages = {I - 645-8 vol.1 - I - 645-8 vol.1},
	abstract = {In automated surveillance systems with multiple cameras, the system must be able to position the cameras accurately. Each camera must be able to pan-tilt such that an object detected in the scene is in a vantage position in the camera{\textquoteright}s image plane and subsequently capture images of that object. Typically, camera calibration is required. We propose an approach that uses only image-based information. Each camera is assigned a pan-tilt zero-position. Position of an object detected in one camera is related to the other cameras by homographies between the zero-positions while different pan-tilt positions of the same camera are related in the form of projective rotations. We then derive that the trajectories in the image plane corresponding to these projective rotations are approximately circular for pan and linear for tilt. The camera control technique is subsequently tested in a working prototype.},
	keywords = {automated, camera, cameras;, control;, detection;, environment;, image, image-based, information;, multicamera, object, pan-tilt, position;, processing;, sensors;, Surveillance, surveillance;, systems;, vantage, zero-position;, zero-positions;},
	doi = {10.1109/ICME.2003.1221000},
	author = {Lim,Ser-Nam and Elgammal,A. and Davis, Larry S.}
}
@article {14886,
	title = {Lambertian reflectance and linear subspaces},
	journal = {Pattern Analysis and Machine Intelligence, IEEE Transactions on},
	volume = {25},
	year = {2003},
	month = {2003/02//},
	pages = {218 - 233},
	abstract = {We prove that the set of all Lambertian reflectance functions (the mapping from surface normals to intensities) obtained with arbitrary distant light sources lies close to a 9D linear subspace. This implies that, in general, the set of images of a convex Lambertian object obtained under a wide variety of lighting conditions can be approximated accurately by a low-dimensional linear subspace, explaining prior empirical results. We also provide a simple analytic characterization of this linear space. We obtain these results by representing lighting using spherical harmonics and describing the effects of Lambertian materials as the analog of a convolution. These results allow us to construct algorithms for object recognition based on linear methods as well as algorithms that use convex optimization to enforce nonnegative lighting functions. We also show a simple way to enforce nonnegative lighting when the images of an object lie near a 4D linear space. We apply these algorithms to perform face recognition by finding the 3D model that best matches a 2D query image.},
	keywords = {2D, 4D, 9D, analog;, analytic, characterization;, convex, convolution, distant, functions;, harmonics;, image, image;, intensities;, Lambertian, light, lighting, linear, methods;, nonnegative, normals;, object, optimization;, programming;, query, recognition;, reflectance;, reflectivity;, set;, sources;, space;, spherical, subspace;, subspaces;, surface},
	isbn = {0162-8828},
	doi = {10.1109/TPAMI.2003.1177153},
	author = {Basri,R. and Jacobs, David W.}
}
@conference {13246,
	title = {Modelling pedestrian shapes for outlier detection: a neural net based approach},
	booktitle = {Intelligent Vehicles Symposium, 2003. Proceedings. IEEE},
	year = {2003},
	month = {2003/06//},
	pages = {428 - 433},
	abstract = {In this paper we present an example-based approach to learn a given class of complex shapes, and recognize instances of that shape with outliers. The system consists of a two-layer custom-designed neural network. We apply this approach to the recognition of pedestrians carrying objects from a single camera. The system is able to capture and model an ample range of pedestrian shapes at varying poses and camera orientations, and achieves a 90\% correct recognition rate.},
	keywords = {(artificial, complex, Computer, computing;, custom, design;, detection;, engineering, intelligence);, layer, learning, method;, modelling;, net;, nets;, neural, object, outlier, pedestrian, pedestrians, rate;, recognition, recognition;, SHAPE, shapes;, traffic, two, vision;},
	doi = {10.1109/IVS.2003.1212949},
	author = {Nanda,H. and Benabdelkedar,C. and Davis, Larry S.}
}
@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 {12718,
	title = {Rank constrained recognition under unknown illuminations},
	booktitle = {Analysis and Modeling of Faces and Gestures, 2003. AMFG 2003. IEEE International Workshop on},
	year = {2003},
	month = {2003/10//},
	pages = {11 - 18},
	abstract = {Recognition under illumination variations is a challenging problem. The key is to successfully separate the illumination source from the observed appearance. Once separated, what remains is invariant to illuminant and appropriate for recognition. Most current efforts employ a Lambertian reflectance model with varying albedo field ignoring both attached and cast shadows, but restrict themselves by using object-specific samples, which undesirably deprives them of recognizing new objects not in the training samples. Using rank constraints on the albedo and the surface normal, we accomplish illumination separation in a more general setting, e.g., with class-specific samples via a factorization approach. In addition, we handle shadows (both attached and cast ones) by treating them as missing values, and resolve the ambiguities in the factorization method by enforcing integrability. As far as recognition is concerned, a bootstrap set which is just a collection of two-dimensional image observations can be utilized to avoid the explicit requirement that three-dimensional information be available. Our approaches produce good recognition results as shown in our experiments using the PIE database.},
	keywords = {albedo, approach;, constrained, database;, databases;, decomposition;, factorization, field;, illumination, image;, information;, Lambertian, lighting;, model;, object, object-specific, PIE, rank, recognition;, reflectance, samples;, singular, three-dimensional, two-dimensional, value, variations;, visual},
	doi = {10.1109/AMFG.2003.1240818},
	author = {Zhou, S. and Chellapa, Rama}
}
@article {17809,
	title = {Temporal probabilistic object bases},
	journal = {Knowledge and Data Engineering, IEEE Transactions on},
	volume = {15},
	year = {2003},
	month = {2003/08//july},
	pages = {921 - 939},
	abstract = {There are numerous applications where we have to deal with temporal uncertainty associated with objects. The ability to automatically store and manipulate time, probabilities, and objects is important. We propose a data model and algebra for temporal probabilistic object bases (TPOBs), which allows us to specify the probability with which an event occurs at a given time point. In explicit TPOB-instances, the sets of time points along with their probability intervals are explicitly enumerated. In implicit TPOB-instances, sets of time points are expressed by constraints and their probability intervals by probability distribution functions. Thus, implicit object base instances are succinct representations of explicit ones; they allow for an efficient implementation of algebraic operations, while their explicit counterparts make defining algebraic operations easy. We extend the relational algebra to both explicit and implicit instances and prove that the operations on implicit instances correctly implement their counterpart on explicit instances.},
	keywords = {algebra;, algebraic, bases;, constraints;, data, database, database;, databases;, distribution, explicit, functions;, handling;, implicit, instances;, integrity;, intervals;, management;, model;, models;, object, object-oriented, operations;, probabilistic, probability, probability;, relational, temporal, theory;, Uncertainty, uncertainty;},
	isbn = {1041-4347},
	doi = {10.1109/TKDE.2003.1209009},
	author = {Biazzo,V. and Giugno,R. and Lukasiewicz,T. and V.S. Subrahmanian}
}
@article {12752,
	title = {A generic approach to simultaneous tracking and verification in video},
	journal = {Image Processing, IEEE Transactions on},
	volume = {11},
	year = {2002},
	month = {2002/05//},
	pages = {530 - 544},
	abstract = {A generic approach to simultaneous tracking and verification in video data is presented. The approach is based on posterior density estimation using sequential Monte Carlo methods. Visual tracking, which is in essence a temporal correspondence problem, is solved through probability density propagation, with the density being defined over a proper state space characterizing the object configuration. Verification is realized through hypothesis testing using the estimated posterior density. In its most basic form, verification can be performed as follows. Given a measurement vector Z and two hypotheses H<sub>1</sub> and H0, we first estimate posterior probabilities P(H<sub>0</sub>|Z) and P(H<sub>1</sub>|Z), and then choose the one with the larger posterior probability as the true hypothesis. Several applications of the approach are illustrated by experiments devised to evaluate its performance. The idea is first tested on synthetic data, and then experiments with real video sequences are presented, illustrating vehicle tracking and verification, human (face) tracking and verification, facial feature tracking, and image sequence stabilization.},
	keywords = {approach;, Carlo, configuration;, correspondence, data;, density, density;, estimated, estimation;, evaluation;, extraction;, Face, facial, feature, generic, human, hypothesis, image, measurement, methods;, Monte, object, performance, posterior, probability, probability;, problem;, processing;, propagation;, recognition;, road, sequence, sequences;, sequential, signal, space;, stabilization;, state, synthetic, temporal, testing;, tracking;, vector;, vehicle, vehicles;, verification;, video, visual},
	isbn = {1057-7149},
	doi = {10.1109/TIP.2002.1006400},
	author = {Li,Baoxin and Chellapa, Rama}
}
@conference {13623,
	title = {Performance evaluation of object detection algorithms},
	booktitle = {Pattern Recognition, 2002. Proceedings. 16th International Conference on},
	volume = {3},
	year = {2002},
	month = {2002///},
	pages = {965 - 969 vol.3 - 965 - 969 vol.3},
	abstract = {The continuous development of object detection algorithms is ushering in the need for evaluation tools to quantify algorithm performance. In this paper a set of seven metrics are proposed for quantifying different aspects of a detection algorithm{\textquoteright}s performance. The strengths and weaknesses of these metrics are described. They are implemented in the Video Performance Evaluation Resource (ViPER) system and will be used to evaluate algorithms for detecting text, faces, moving people and vehicles. Results for running two previous text-detection algorithms on a common data set are presented.},
	keywords = {algorithms;, common, data, DETECTION, detection;, Evaluation, evaluation;, image, object, performance, recognition;, resource, set;, system;, text-detection, video},
	doi = {10.1109/ICPR.2002.1048198},
	author = {Mariano,V.Y. and Min,Junghye and Park,Jin-Hyeong and Kasturi,R. and Mihalcik,D. and Huiping Li and David Doermann and Drayer,T.}
}
@conference {14843,
	title = {Comparing images under variable illumination},
	booktitle = {Computer Vision and Pattern Recognition, 1998. Proceedings. 1998 IEEE Computer Society Conference on},
	year = {1998},
	month = {1998/06//},
	pages = {610 - 617},
	abstract = {We consider the problem of determining whether two images come from different objects or the same object in the same pose, but under different illumination conditions. We show that this problem cannot be solved using hard constraints: even using a Lambertian reflectance model, there is always an object and a pair of lighting conditions consistent with any two images. Nevertheless, we show that for point sources and objects with Lambertian reflectance, the ratio of two images from the same object is simpler than the ratio of images from different objects. We also show that the ratio of the two images provides two of the three distinct values in the Hessian matrix of the object{\textquoteright}s surface. Using these observations, we develop a simple measure for matching images under variable illumination, comparing its performance to other existing methods on a database of 450 images of 10 individuals},
	keywords = {conditions;matching, illumination;visual, images;point, Lambertian, matching;object, model;database;lighting, object, recognition;, recognition;image, reflectance, sources;pose;variable},
	doi = {10.1109/CVPR.1998.698668},
	author = {Jacobs, David W. and Belhumeur,P. N. and Basri,R.}
}
@conference {15524,
	title = {Semantic query optimization for object databases},
	booktitle = {Data Engineering, 1997. Proceedings. 13th International Conference on},
	year = {1997},
	month = {1997/04//},
	pages = {444 - 453},
	abstract = {Presents a technique for semantic query optimization (SQO) for object databases. We use the ODMG-93 (Object Data Management Group) standard ODL (Object Database Language) and OQL (Object Query Language) languages. The ODL object schema and the OQL object query are translated into a DATALOG representation. Semantic knowledge about the object model and the particular application is expressed as integrity constraints. This is an extension of the ODMG-93 standard. SQO is performed in the DATALOG representation, and an equivalent logic query and (subsequently) an equivalent OQL object query are obtained. SQO is based on the residue technique of Chakravarthy et al. (1990). We show that our technique generalizes previous research on SQO for object databases. It can be applied to queries with structure constructors and method application. It utilizes integrity constraints about keys, methods and knowledge of access support relations, to produce equivalent queries, which may have more efficient evaluation plans},
	keywords = {application;object, constraints;keys;method, constructors;DATALOG;data, databases;object-oriented, databases;query, Datalog, Evaluation, integrity;data, languages;query, Logic, object, optimization;structure, plans;residue, processing;software, query, query;access, query;integrity, relations;equivalent, representation;ODL, schema;ODMG-93, standard;OQL, standards;, structures;object-oriented, Support, technique;semantic},
	doi = {10.1109/ICDE.1997.581983},
	author = {Grant,J. and Gryz,J. and Minker, Jack and Raschid, Louiqa}
}
@conference {14839,
	title = {Error propagation in full 3D-from-2D object recognition},
	booktitle = {Computer Vision and Pattern Recognition, 1994. Proceedings CVPR {\textquoteright}94., 1994 IEEE Computer Society Conference on},
	year = {1994},
	month = {1994/06//},
	pages = {892 - 898},
	abstract = {Robust recognition systems require a careful understanding of the effects of error in sensed features. Error in these image features results in uncertainty in the possible image location of each additional model feature. We present an accurate, analytic approximation for this uncertainty when model poses are based on matching three image and model points. This result applies to objects that are fully three-dimensional, where past results considered only two-dimensional objects. Further, we introduce a linear programming algorithm to compute this uncertainty when poses are based on any number of initial matches},
	keywords = {3D-from-2D, algorithm;, error, extraction;, feature, features;, handling;, image, initial, linear, matches;, object, Programming, programming;, propagation;, recognition, recognition;, robust, sequences;, systems;, Uncertainty, uncertainty;},
	doi = {10.1109/CVPR.1994.323920},
	author = {Alter,T. D. and Jacobs, David W.}
}
