@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 {13213,
	title = {Background modeling and subtraction by codebook construction},
	booktitle = {Image Processing, 2004. ICIP {\textquoteright}04. 2004 International Conference on},
	volume = {5},
	year = {2004},
	month = {2004/10//},
	pages = {3061 - 3064 Vol. 5 - 3061 - 3064 Vol. 5},
	abstract = {We present a new fast algorithm for background modeling and subtraction. Sample background values at each pixel are quantized into codebooks which represent a compressed form of background model for a long image sequence. This allows us to capture structural background variation due to periodic-like motion over a long period of time under limited memory. Our method can handle scenes containing moving backgrounds or illumination variations (shadows and highlights), and it achieves robust detection for compressed videos. We compared our method with other multimode modeling techniques.},
	keywords = {(signal);, analysis;, background, codebook, coding;, colour, compression;, construction;, data, image, modeling, modeling;, MOTION, multimode, quantisation, representation;, sequence;, sequences;, subtraction;, technique;, video},
	doi = {10.1109/ICIP.2004.1421759},
	author = {Kim,Kyungnam and Chalidabhongse,T.H. and Harwood,D. and Davis, Larry S.}
}
@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 {12696,
	title = {Role of shape and kinematics in human movement analysis},
	booktitle = {Computer Vision and Pattern Recognition, 2004. CVPR 2004. Proceedings of the 2004 IEEE Computer Society Conference on},
	volume = {1},
	year = {2004},
	month = {2004/07/02/june},
	pages = {I-730 - I-737 Vol.1 - I-730 - I-737 Vol.1},
	abstract = {Human gait and activity analysis from video is presently attracting a lot of attention in the computer vision community. In this paper we analyze the role of two of the most important cues in human motion-shape and kinematics. We present an experimental framework whereby it is possible to evaluate the relative importance of these two cues in computer vision based recognition algorithms. In the process, we propose a new gait recognition algorithm by computing the distance between two sequences of shapes that lie on a spherical manifold. In our experiments, shape is represented using Kendall{\textquoteright}s definition of shape. Kinematics is represented using a Linear Dynamical system We place particular emphasis on human gait. Our conclusions show that shape plays a role which is more significant than kinematics in current automated gait based human identification algorithms. As a natural extension we study the role of shape and kinematics in activity recognition. Our experiments indicate that we require models that contain both shape and kinematics in order to perform accurate activity classification. These conclusions also allow us to explain the relative performance of many existing methods in computer-based human activity modeling.},
	keywords = {activity, algorithm;, algorithms;, analysis;, autoregressive, average, based, classification;, community;, Computer, definition;, dynamical, extraction;, feature, Gait, hidden, human, identification, image, Kendall, linear, manifold;, Markov, modeling;, models;, MOTION, Movement, moving, processes;, recognition, sequences;, SHAPE, spherical, system;, VISION, vision;},
	doi = {10.1109/CVPR.2004.1315104},
	author = {Veeraraghavan,A. and Chowdhury, A.R. and Chellapa, Rama}
}
@conference {12684,
	title = {Simultaneous background and foreground modeling for tracking in surveillance video},
	booktitle = {Image Processing, 2004. ICIP {\textquoteright}04. 2004 International Conference on},
	volume = {2},
	year = {2004},
	month = {2004/10//},
	pages = {1053 - 1056 Vol.2 - 1053 - 1056 Vol.2},
	abstract = {We present a stochastic tracking algorithm for surveillance video where targets are dim and at low resolution. The algorithm builds motion models for both background and foreground by integrating motion and intensity information. Some other merits of the algorithm include adaptive selection of feature points for scene description and defining proper cost functions for displacement estimation. The experimental results show tracking robustness and precision in a challenging video sequences.},
	keywords = {algorithm;, analysis;, background-foreground, displacement, estimation;, image, information;, INTENSITY, modeling;, MOTION, processes;, processing;, resolution;, sequences;, signal, Stochastic, Surveillance, surveillance;, tracking, tracking;, video},
	doi = {10.1109/ICIP.2004.1419483},
	author = {Shao, J. and Zhou,S. K and Chellapa, Rama}
}
@article {12699,
	title = {Wide baseline image registration with application to 3-D face modeling},
	journal = {Multimedia, IEEE Transactions on},
	volume = {6},
	year = {2004},
	month = {2004/06//},
	pages = {423 - 434},
	abstract = {Establishing correspondence between features in two images of the same scene taken from different viewing angles is a challenging problem in image processing and computer vision. However, its solution is an important step in many applications like wide baseline stereo, three-dimensional (3-D) model alignment, creation of panoramic views, etc. In this paper, we propose a technique for registration of two images of a face obtained from different viewing angles. We show that prior information about the general characteristics of a face obtained from video sequences of different faces can be used to design a robust correspondence algorithm. The method works by matching two-dimensional (2-D) shapes of the different features of the face (e.g., eyes, nose etc.). A doubly stochastic matrix, representing the probability of match between the features, is derived using the Sinkhorn normalization procedure. The final correspondence is obtained by minimizing the probability of error of a match between the entire constellation of features in the two sets, thus taking into account the global spatial configuration of the features. The method is applied for creating holistic 3-D models of a face from partial representations. Although this paper focuses primarily on faces, the algorithm can also be used for other objects with small modifications.},
	keywords = {2D, 3D, algorithm;, baseline, biometrics;, Computer, configuration;, correspondence, doubly, error, extraction;, Face, feature, holistic, image, matching;, matrix;, minimization;, modeling;, models;, normalization, probability, probability;, procedure;, processes;, processing;, recognition;, registration;, representation;, sequences;, shapes;, Sinkhorn, spatial, statistics;, Stochastic, video, vision;, wide},
	isbn = {1520-9210},
	doi = {10.1109/TMM.2004.827511},
	author = {Roy-Chowdhury, A.K. and Chellapa, Rama and Keaton, T.}
}
@article {13235,
	title = {Efficient kernel density estimation using the fast gauss transform with applications to color modeling and tracking},
	journal = {Pattern Analysis and Machine Intelligence, IEEE Transactions on},
	volume = {25},
	year = {2003},
	month = {2003/11//},
	pages = {1499 - 1504},
	abstract = {Many vision algorithms depend on the estimation of a probability density function from observations. Kernel density estimation techniques are quite general and powerful methods for this problem, but have a significant disadvantage in that they are computationally intensive. In this paper, we explore the use of kernel density estimation with the fast Gauss transform (FGT) for problems in vision. The FGT allows the summation of a mixture of ill Gaussians at N evaluation points in O(M+N) time, as opposed to O(MN) time for a naive evaluation and can be used to considerably speed up kernel density estimation. We present applications of the technique to problems from image segmentation and tracking and show that the algorithm allows application of advanced statistical techniques to solve practical vision problems in real-time with today{\textquoteright}s computers.},
	keywords = {algorithms;, Color, Computer, density, estimation;, fast, function;, Gauss, image, Kernel, modeling;, segmentation;, tracking;, transform;, transforms;, VISION, vision;},
	isbn = {0162-8828},
	doi = {10.1109/TPAMI.2003.1240123},
	author = {Elgammal,A. and Duraiswami, Ramani and Davis, Larry S.}
}
@conference {12733,
	title = {Statistical shape theory for activity modeling},
	booktitle = {Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP {\textquoteright}03). 2003 IEEE International Conference on},
	volume = {3},
	year = {2003},
	month = {2003/04//},
	pages = {III - 493-6 vol.3 - III - 493-6 vol.3},
	abstract = {Monitoring activities in a certain region from video data is an important surveillance problem. The goal is to learn the pattern of normal activities and detect unusual ones by identifying activities that deviate appreciably from the typical ones. We propose an approach using statistical shape theory based on the shape model of D.G. Kendall et al. (see "Shape and Shape Theory", John Wiley and Sons, 1999). In a low resolution video, each moving object is best represented as a moving point mass or particle. In this case, an activity can be defined by the interactions of all or some of these moving particles over time. We model this configuration of the particles by a polygonal shape formed from the locations of the points in a frame and the activity by the deformation of the polygons in time. These parameters are learned for each typical activity. Given a test video sequence, an activity is classified as abnormal if the probability for the sequence (represented by the mean shape and the dynamics of the deviations), given the model, is below a certain threshold The approach gives very encouraging results in surveillance applications using a single camera and is able to identify various kinds of abnormal behavior.},
	keywords = {abnormal, activities, activity, analysis;, behavior;, classification;, data;, image, mass;, matching;, modeling;, monitoring;, moving, normal, particle;, pattern, pattern;, point, polygonal, probability;, problem;, processing;, sequence;, sequences;, SHAPE, shape;, signal, statistical, Surveillance, surveillance;, theory;, video},
	doi = {10.1109/ICASSP.2003.1199519},
	author = {Vaswani, N. and Chowdhury, A.R. and Chellapa, Rama}
}
@conference {13678,
	title = {The processing of form documents},
	booktitle = {Document Analysis and Recognition, 1993., Proceedings of the Second International Conference on},
	year = {1993},
	month = {1993/10//},
	pages = {497 - 501},
	abstract = {An overview of an approach to the generic modeling and processing of known forms is presented. The system provides a methodology by which models are generated from regions in the document based on their usage. Automatic extraction of an optimal set of features to be used for registration is proposed, and it is shown how specialized detectors can be designed for each feature based on their position, orientation and width properties. Registration of the form with the model is accomplished using probing to establish correspondence. Form components which are corrupted by markings are detected and isolated, the intersections are interpreted and the properties of the non-form markings are used to reconstruct the strokes through the intersections. The feasibility of these ideas is demonstrated through an implementation of key components of the system},
	keywords = {AUTOMATIC, business, detectors;, document, documents;, extraction;, feature, form, forms;, generation;, generic, handling;, known, markings;, model, modeling;, non-form, optimal, properties;, reconstruction;, set;, specialized, stroke, width},
	doi = {10.1109/ICDAR.1993.395687},
	author = {David Doermann and Rosenfeld, A.}
}
