@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 {13563,
	title = {Handwriting matching and its application to handwriting synthesis},
	booktitle = {Document Analysis and Recognition, 2005. Proceedings. Eighth International Conference on},
	year = {2005},
	month = {2005/09/01/aug},
	pages = {861 - 865 Vol. 2 - 861 - 865 Vol. 2},
	abstract = {Since it is extremely expensive to collect a large volume of handwriting samples, synthesized data are often used to enlarge the training set. We argue that, in order to generate good handwriting samples, a synthesis algorithm should learn the shape deformation characteristics of handwriting from real samples. In this paper, we present a point matching algorithm to learn the deformation, and apply it to handwriting synthesis. Preliminary experiments show the advantages of our approach.},
	keywords = {(artificial, deformation, deformation;, handwriting, image, intelligence);, learning, learning;, matching;, point, recognition;, sampling;, SHAPE, synthesis;},
	doi = {10.1109/ICDAR.2005.122},
	author = {Yefeng Zheng and David Doermann}
}
@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 {14025,
	title = {A robust and self-reconfigurable design of spherical microphone array for multi-resolution beamforming},
	booktitle = {Acoustics, Speech, and Signal Processing, 2005. Proceedings. (ICASSP {\textquoteright}05). IEEE International Conference on},
	volume = {4},
	year = {2005},
	month = {2005/03//},
	pages = {iv/1137 - iv/1140 Vol. 4 - iv/1137 - iv/1140 Vol. 4},
	abstract = {We describe a robust and self-reconfigurable design of a spherical microphone array for beamforming. Our approach achieves a multi-resolution spherical beamformer with performance that is either optimal in the approximation of desired beampattern or is optimal in the directivity achieved, both robustly. Our implementation converges to the optimal performance quickly while exactly satisfying the specified frequency response and robustness constraint in each iteration step without accumulated round-off errors. The advantage of this design lies in its robustness and self-reconfiguration in microphone array reorganization, such as microphone failure, which is highly desirable in online maintenance and anti-terrorism. Design examples and simulation results are presented.},
	keywords = {3D, anti-terrorism;, array, array;, arrays;, audio, beam, beamforming;, beampattern, directivity, Frequency, microphone, multiresolution, omnidirectional, optimisation;, optimization;, processing;, reorganization, response;, robustness;, sampling;, self-reconfigurable, signal, soundfield, spherical, steering;},
	doi = {10.1109/ICASSP.2005.1416214},
	author = {Zhiyun Li and Duraiswami, Ramani}
}
@conference {12704,
	title = {Fusion of gait and face for human identification},
	booktitle = {Acoustics, Speech, and Signal Processing, 2004. Proceedings. (ICASSP {\textquoteright}04). IEEE International Conference on},
	volume = {5},
	year = {2004},
	month = {2004/05//},
	pages = {V - 901-4 vol.5 - V - 901-4 vol.5},
	abstract = {Identification of humans from arbitrary view points is an important requirement for different tasks including perceptual interfaces for intelligent environments, covert security and access control etc. For optimal performance, the system must use as many cues as possible and combine them in meaningful ways. In this paper, we discuss fusion of face and gait cues for the single camera case. We present a view invariant gait recognition algorithm for gait recognition. We employ decision fusion to combine the results of our gait recognition algorithm and a face recognition algorithm based on sequential importance sampling. We consider two fusion scenarios: hierarchical and holistic. The first involves using the gait recognition algorithm as a filter to pass on a smaller set of candidates to the face recognition algorithm. The second involves combining the similarity scores obtained individually from the face and gait recognition algorithms. Simple rules like the SUM, MIN and PRODUCT are used for combining the scores. The results of fusion experiments are demonstrated on the NIST database which has outdoor gait and face data of 30 subjects.},
	keywords = {access, algorithm;, analysis;, combining, control;, covert, cues;, data, decision, Environment, Face, fusion;, Gait, hierarchical, holistic, human, identification;, importance, intelligent, interfaces;, invariant, perceptual, recognition, recognition;, rules;, sampling;, score, scores;, security;, sensor, sequential, similarity, view},
	doi = {10.1109/ICASSP.2004.1327257},
	author = {Kale, A. and Roy Chowdhury, A.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 {12686,
	title = {Robust Bayesian cameras motion estimation using random sampling},
	booktitle = {Image Processing, 2004. ICIP {\textquoteright}04. 2004 International Conference on},
	volume = {2},
	year = {2004},
	month = {2004/10//},
	pages = {1361 - 1364 Vol.2 - 1361 - 1364 Vol.2},
	abstract = {In this paper, we propose an algorithm for robust 3D motion estimation of wide baseline cameras from noisy feature correspondences. The posterior probability density function of the camera motion parameters is represented by weighted samples. The algorithm employs a hierarchy coarse-to-fine strategy. First, a coarse prior distribution of camera motion parameters is estimated using the random sample consensus scheme (RANSAC). Based on this estimate, a refined posterior distribution of camera motion parameters can then be obtained through importance sampling. Experimental results using both synthetic and real image sequences indicate the efficacy of the proposed algorithm.},
	keywords = {3D, baseline, Bayesian, CAMERAS, cameras;, coarse-to-fine, consensus, density, estimation;, feature, function;, hierarchy, image, images;, importance, matching;, MOTION, posterior, probability, probability;, processing;, random, RANSAC;, real, realistic, sample, sampling;, scheme;, sequences;, stereo, strategy;, synthetic, wide},
	doi = {10.1109/ICIP.2004.1419754},
	author = {Qian, G. and Chellapa, Rama and Qinfen Zheng}
}
@conference {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 {12758,
	title = {Bayesian structure from motion using inertial information},
	booktitle = {Image Processing. 2002. Proceedings. 2002 International Conference on},
	volume = {3},
	year = {2002},
	month = {2002///},
	pages = {III-425 - III-428 vol.3 - III-425 - III-428 vol.3},
	abstract = {A novel approach to Bayesian structure from motion (SfM) using inertial information and sequential importance sampling (SIS) is presented. The inertial information is obtained from camera-mounted inertial sensors and is used in the Bayesian SfM approach as prior knowledge of the camera motion in the sampling algorithm. Experimental results using both synthetic and real images show that, when inertial information is used, more accurate results can be obtained or the same estimation accuracy can be obtained at a lower cost.},
	keywords = {3D, analysis;, Bayes, Bayesian, camera, estimation;, image, images;, importance, inertial, information;, methods;, MOTION, motion;, parameter, processing;, real, reconstruction;, sampling;, scene, sensors;, sequence;, sequences;, sequential, signal, structure-from-motion;, synthetic, systems;, video},
	doi = {10.1109/ICIP.2002.1038996},
	author = {Qian,Gang and Chellapa, Rama and Qinfen Zheng}
}
@conference {12766,
	title = {Probabilistic recognition of human faces from video},
	booktitle = {Image Processing. 2002. Proceedings. 2002 International Conference on},
	volume = {1},
	year = {2002},
	month = {2002///},
	pages = {I-41 - I-44 vol.1 - I-41 - I-44 vol.1},
	abstract = {Most present face recognition approaches recognize faces based on still images. We present a novel approach to recognize faces in video. In that scenario, the face gallery may consist of still images or may be derived from a videos. For evidence integration we use classical Bayesian propagation over time and compute the posterior distribution using sequential importance sampling. The probabilistic approach allows us to handle uncertainties in a systematic manner. Experimental results using videos collected by NIST/USF and CMU illustrate the effectiveness of this approach in both still-to-video and video-to-video scenarios with appropriate model choices.},
	keywords = {Bayes, Bayesian, CMU;, distribution;, Face, faces;, gallery;, handling;, human, image, images;, importance, likelihood;, methods;, NIST/USF;, observation, posterior, probabilistic, probability;, processing;, propagation;, recognition;, sampling;, sequential, signal, still, Still-to-video, Uncertainty, video, Video-to-video},
	doi = {10.1109/ICIP.2002.1037954},
	author = {Chellapa, Rama and Kruger, V. and Zhou,Shaohua}
}
