@conference {13161,
	title = {Tracking Articulating Objects from Ground Vehicles using Mixtures of Mixtures},
	booktitle = {Intelligent Robots and Systems, 2006 IEEE/RSJ International Conference on},
	year = {2006},
	month = {2006/10//},
	pages = {1230 - 1236},
	abstract = {An algorithm for tracking articulating objects from moving camera platforms is presented. Mixtures of mixtures are used to model the appearance of the object and the background. The state of the object is tracked using a particle filter. Egomotion information are estimated and used to set the state variance of the particle filter. Results of tracking human objects from an unmanned ground vehicle are used to evaluate the tracking algorithm},
	keywords = {articulating, filter;unmanned, ground, information;mixtures, mixtures;particle, objects, of, operated, tracking;egomotion, vehicle;remotely, vehicles;robot, vision;},
	doi = {10.1109/IROS.2006.281881},
	author = {Abd-Almageed, Wael and Hussein,M. and Davis, Larry S.}
}
@conference {12650,
	title = {An algebraic approach to surface reconstruction from gradient fields},
	booktitle = {Computer Vision, 2005. ICCV 2005. Tenth IEEE International Conference on},
	volume = {1},
	year = {2005},
	month = {2005/10//},
	pages = {174 - 181 Vol. 1 - 174 - 181 Vol. 1},
	abstract = {Several important problems in computer vision such as shape from shading (SFS) and photometric stereo (PS) require reconstructing a surface from an estimated gradient field, which is usually non-integrable, i.e. have non-zero curl. We propose a purely algebraic approach to enforce integrability in discrete domain. We first show that enforcing integrability can be formulated as solving a single linear system Ax =b over the image. In general, this system is under-determined. We show conditions under which the system can be solved and a method to get to those conditions based on graph theory. The proposed approach is non-iterative, has the important property of local error confinement and can be applied to several problems. Results on SFS and PS demonstrate the applicability of our method.},
	keywords = {algebra;, algebraic, approach;, Computer, confinement;, discrete, domain, error, field;, from, gradient, graph, image, integrability;, linear, local, methods;, photometric, reconstruction;, shading;, SHAPE, stereo;, surface, system;, theory;, vision;},
	doi = {10.1109/ICCV.2005.31},
	author = {Agrawal,A. and Chellapa, Rama and Raskar, R.}
}
@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 {13191,
	title = {VidMAP: video monitoring of activity with Prolog},
	booktitle = {Advanced Video and Signal Based Surveillance, 2005. AVSS 2005. IEEE Conference on},
	year = {2005},
	month = {2005/09//},
	pages = {224 - 229},
	abstract = {This paper describes the architecture of a visual surveillance system that combines real time computer vision algorithms with logic programming to represent and recognize activities involving interactions amongst people, packages and the environments through which they move. The low level computer vision algorithms log primitive events of interest as observed facts, while the higher level Prolog based reasoning engine uses these facts in conjunction with predefined rules to recognize various activities in the input video streams. The system is illustrated in action on a multi-camera surveillance scenario that includes both security and safety violations.},
	keywords = {activities, algorithms;, based, Computer, computerised, engine;, higher, image, level, Logic, monitoring;, multicamera, processing;, programming;, Prolog, PROLOG;, reasoning, recognition;, scenario;, signal, streaming;, streams;, Surveillance, surveillance;, system;, video, VISION, vision;, visual},
	doi = {10.1109/AVSS.2005.1577271},
	author = {Shet,V. D and Harwood,D. and Davis, Larry S.}
}
@conference {12706,
	title = {3D model refinement using surface-parallax},
	booktitle = {Acoustics, Speech, and Signal Processing, 2004. Proceedings. (ICASSP {\textquoteright}04). IEEE International Conference on},
	volume = {3},
	year = {2004},
	month = {2004/05//},
	pages = {iii - 285-8 vol.3 - iii - 285-8 vol.3},
	abstract = {We present an approach to update and refine coarse 3D models of urban environments from a sequence of intensity images using surface parallax. This generalizes the plane-parallax recovery methods to surface-parallax using arbitrary surfaces. A coarse and potentially incomplete depth map of the scene obtained from a digital elevation map (DEM) is used as a reference surface which is refined and updated using this approach. The reference depth map is used to estimate the camera motion and the motion of the 3D points on the reference surface is compensated. The resulting parallax, which is an epipolar field, is estimated using an adaptive windowing technique and used to obtain the refined depth map.},
	keywords = {3D, adaptive, arbitrary, camera, coarse, compensation;, Computer, DEM;, depth, digital, ELEVATION, environments;, epipolar, estimation;, field;, image, incomplete, INTENSITY, map;, model, MOTION, parallax;, plane-parallax, reconstruction;, recovery;, refinement;, sequence;, sequences;, surface, surfaces;, urban, vision;, windowing;},
	doi = {10.1109/ICASSP.2004.1326537},
	author = {Agrawala, Ashok K. and Chellapa, Rama}
}
@conference {13216,
	title = {Iterative figure-ground discrimination},
	booktitle = {Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on},
	volume = {1},
	year = {2004},
	month = {2004/08//},
	pages = {67 - 70 Vol.1 - 67 - 70 Vol.1},
	abstract = {Figure-ground discrimination is an important problem in computer vision. Previous work usually assumes that the color distribution of the figure can be described by a low dimensional parametric model such as a mixture of Gaussians. However, such approach has difficulty selecting the number of mixture components and is sensitive to the initialization of the model parameters. In this paper, we employ non-parametric kernel estimation for color distributions of both the figure and background. We derive an iterative sampling-expectation (SE) algorithm for estimating the color, distribution and segmentation. There are several advantages of kernel-density estimation. First, it enables automatic selection of weights of different cues based on the bandwidth calculation from the image itself. Second, it does not require model parameter initialization and estimation. The experimental results on images of cluttered scenes demonstrate the effectiveness of the proposed algorithm.},
	keywords = {algorithm;, analysis;, Bandwidth, calculation;, Color, colour, Computer, density, dimensional, discrimination;, distribution;, distributions;, Estimation, estimation;, expectation, figure, Gaussian, ground, image, initialization;, iterative, Kernel, low, methods;, mixture;, model, model;, nonparametric, parameter, parametric, processes;, sampling, sampling;, segmentation, segmentation;, statistics;, theory;, vision;},
	doi = {10.1109/ICPR.2004.1334006},
	author = {Zhao, L. and Davis, Larry S.}
}
@conference {13209,
	title = {Multi-level fast multipole method for thin plate spline evaluation},
	booktitle = {Image Processing, 2004. ICIP {\textquoteright}04. 2004 International Conference on},
	volume = {3},
	year = {2004},
	month = {2004/10//},
	pages = {1683 - 1686 Vol. 3 - 1683 - 1686 Vol. 3},
	abstract = {Image registration is an important problem in image processing and computer vision. Much recent work in image registration is on matching non-rigid deformations. Thin plate splines are an effective image registration method when the deformation between two images can be modeled as the bending of a thin metal plate on point constraints such that the topology is preserved (non-rigid deformation). However, because evaluating the computed TPS model at all the image pixels is computationally expensive, we need to speed it up. We introduce the use of multi-level fast muitipole method (MLFMM) for this purpose. Our contribution lies in the presentation of a clear and concise MLFMM framework for TPS, which will be useful for future application developments. The achieved speedup using MLFMM is an improvement from O(N<sup>2</sup>) to O(N log N). We show that the fast evaluation outperforms the brute force method while maintaining acceptable error bound.},
	keywords = {(mathematics);, Computer, deformation;, evaluation;, fast, image, MATCHING, matching;, metal, method;, multilevel, multipole, nonrigid, pixel;, plate, plate;, processing;, registration;, resolution;, spline, splines, thin, vision;},
	doi = {10.1109/ICIP.2004.1421395},
	author = {Zandifar,A. and Lim,S. and Duraiswami, Ramani and Gumerov, Nail A. and Davis, Larry S.}
}
@conference {13208,
	title = {Recording and reproducing high order surround auditory scenes for mixed and augmented reality},
	booktitle = {Mixed and Augmented Reality, 2004. ISMAR 2004. Third IEEE and ACM International Symposium on},
	year = {2004},
	month = {2004/11//},
	pages = {240 - 249},
	abstract = {Virtual reality systems are largely based on computer graphics and vision technologies. However, sound also plays an important role in human{\textquoteright}s interaction with the surrounding environment, especially for the visually impaired people. In this paper, we develop the theory of recording and reproducing real-world surround auditory scenes in high orders using specially designed microphone and loudspeaker arrays. It is complementary to vision-based technologies in creating mixed and augmented realities. Design examples and simulations are presented.},
	keywords = {array;, audio, auditory, augmented, Computer, graphics;, high, loudspeaker, microphone, mixed, order, processing;, reality, reality;, scene;, signal, surround, system;, technology;, virtual, VISION, vision;},
	doi = {10.1109/ISMAR.2004.51},
	author = {Zhiyun Li and Duraiswami, Ramani 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}
}
@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.}
}
@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}
}
@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 {13247,
	title = {Learning dynamics for exemplar-based gesture recognition},
	booktitle = {Computer Vision and Pattern Recognition, 2003. Proceedings. 2003 IEEE Computer Society Conference on},
	volume = {1},
	year = {2003},
	month = {2003/06//},
	pages = {I-571 - I-578 vol.1 - I-571 - I-578 vol.1},
	abstract = {This paper addresses the problem of capturing the dynamics for exemplar-based recognition systems. Traditional HMM provides a probabilistic tool to capture system dynamics and in exemplar paradigm, HMM states are typically coupled with the exemplars. Alternatively, we propose a non-parametric HMM approach that uses a discrete HMM with arbitrary states (decoupled from exemplars) to capture the dynamics over a large exemplar space where a nonparametric estimation approach is used to model the exemplar distribution. This reduces the need for lengthy and non-optimal training of the HMM observation model. We used the proposed approach for view-based recognition of gestures. The approach is based on representing each gesture as a sequence of learned body poses (exemplars). The gestures are recognized through a probabilistic framework for matching these body poses and for imposing temporal constraints between different poses using the proposed non-parametric HMM.},
	keywords = {arbitrary, body, by, Computer, constraint;, detection;, discrete, distribution, dynamics;, edge, estimation;, example;, exemplar, exemplar-based, extraction;, feature, framework;, gesture, gesture;, hidden, HMM;, human, image, learning, Markov, matching;, model;, models;, motion;, nonparametric, pose, probabilistic, recognition;, sequence;, space;, state;, statistics;, system, temporal, tool;, view-based, vision;},
	doi = {10.1109/CVPR.2003.1211405},
	author = {Elgammal,A. and Shet,V. and Yacoob,Yaser and Davis, Larry S.}
}
@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 {16896,
	title = {Simultaneous pose and correspondence determination using line features},
	booktitle = {Computer Vision and Pattern Recognition, 2003. Proceedings. 2003 IEEE Computer Society Conference on},
	volume = {2},
	year = {2003},
	month = {2003/06//},
	pages = {II-424 - II-431 vol.2 - II-424 - II-431 vol.2},
	abstract = {We present a new robust line matching algorithm for solving the model-to-image registration problem. Given a model consisting of 3D lines and a cluttered perspective image of this model, the algorithm simultaneously estimates the pose of the model and the correspondences of model lines to image lines. The algorithm combines softassign for determining correspondences and POSIT for determining pose. Integrating these algorithms into a deterministic annealing procedure allows the correspondence and pose to evolve from initially uncertain values to a joint local optimum. This research extends to line features the SoftPOSIT algorithm proposed recently for point features. Lines detected in images are typically more stable than points and are less likely to be produced by clutter and noise, especially in man-made environments. Experiments on synthetic and real imagery with high levels of clutter, occlusion, and noise demonstrate the robustness of the algorithm.},
	keywords = {algorithm;, algorithms;, annealing;, clutter;, cluttered, Computer, correspondence, detection;, determination;, deterministic, environment;, extraction;, feature, feature;, image, image;, imagery;, images;, joint, line, local, man-made, MATCHING, matching;, measurement;, model-to-image, noise;, occlusion;, optimum;, perspective, point, pose, position, problem;, processing;, real, realistic, registration, simulated, softassign;, SoftPOSIT, stereo, synthetic, vision;},
	doi = {10.1109/CVPR.2003.1211499},
	author = {David,P. and DeMenthon,D. and Duraiswami, Ramani and Samet, Hanan}
}
@conference {12765,
	title = {3D face reconstruction from video using a generic model},
	booktitle = {Multimedia and Expo, 2002. ICME {\textquoteright}02. Proceedings. 2002 IEEE International Conference on},
	volume = {1},
	year = {2002},
	month = {2002///},
	pages = {449 - 452 vol.1 - 449 - 452 vol.1},
	abstract = {Reconstructing a 3D model of a human face from a video sequence is an important problem in computer vision, with applications to recognition, surveillance, multimedia etc. However, the quality of 3D reconstructions using structure from motion (SfM) algorithms is often not satisfactory. One common method of overcoming this problem is to use a generic model of a face. Existing work using this approach initializes the reconstruction algorithm with this generic model. The problem with this approach is that the algorithm can converge to a solution very close to this initial value, resulting in a reconstruction which resembles the generic model rather than the particular face in the video which needs to be modeled. We propose a method of 3D reconstruction of a human face from video in which the 3D reconstruction algorithm and the generic model are handled separately. A 3D estimate is obtained purely from the video sequence using SfM algorithms without use of the generic model. The final 3D model is obtained after combining the SfM estimate and the generic model using an energy function that corrects for the errors in the estimate by comparing local regions in the two models. The optimization is done using a Markov chain Monte Carlo (MCMC) sampling strategy. The main advantage of our algorithm over others is that it is able to retain the specific features of the face in the video sequence even when these features are different from those of the generic model. The evolution of the 3D model through the various stages of the algorithm is presented.},
	keywords = {3D, algorithm;, algorithms;, analysis;, Carlo, chain, Computer, Face, from, function;, generic, human, image, Markov, MCMC, methods;, model;, Monte, MOTION, optimisation;, OPTIMIZATION, processes;, processing;, recognition;, reconstruction, reconstruction;, sampling;, sequence;, sequences;, SfM, signal, structure, surveillance;, video, vision;},
	doi = {10.1109/ICME.2002.1035815},
	author = {Chowdhury, A.R. and Chellapa, Rama and Krishnamurthy, S. and Vo, T.}
}
@conference {12747,
	title = {Wide baseline image registration using prior information},
	booktitle = {Multimedia Signal Processing, 2002 IEEE Workshop on},
	year = {2002},
	month = {2002/12//},
	pages = {37 - 40},
	abstract = {Establishing correspondence between features in two images of the same scene taken from different viewing angles in a challenging problem in image processing and computer vision. However, its solution is an important step in many applications like wide baseline stereo, 3D 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 2D shapes of the different features of the face. 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 constellations of features in the two sets, thus taking into account the global spatial configuration of the features. The method is applied for creating holistic 3D 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;, alignment;, angles;, baseline, Computer, configuration;, constellation;, correspondence, creation;, doubly, error, extraction;, Face, feature, global, holistic, image, images;, matching;, matrix;, model, models;, normalization, panoramic, probability;, procedure;, processes;, processing;, registration;, robust, sequences;, SHAPE, signal, Sinkhorn, spatial, statistics;, stereo;, Stochastic, video, view, viewing, vision;, wide},
	doi = {10.1109/MMSP.2002.1203242},
	author = {Chowdhury, AM and Chellapa, Rama and Keaton, T.}
}
