@article {12442,
	title = {Special Issue on Video Analysis on Resource-Limited Systems},
	journal = {IEEE Transactions on Circuits and Systems for Video Technology},
	volume = {21},
	year = {2011},
	month = {2011/10//},
	pages = {1349 - 1352},
	abstract = {The 17 papers in this special issue focus on resource-limited systems.},
	keywords = {computational complexity, Image Enhancement, Special issues and sections, Video compression},
	isbn = {1051-8215},
	doi = {10.1109/TCSVT.2011.2165795},
	author = {Chellapa, Rama and Cavallaro, A. and Wu,Y. and Shan, C. and Fu, Y. and Pulli, K.}
}
@conference {12445,
	title = {Variable remapping of images from very different sources},
	booktitle = {2011 18th IEEE International Conference on Image Processing (ICIP)},
	year = {2011},
	month = {2011/09/11/14},
	pages = {1501 - 1504},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {We present a system which registers image sequences acquired by very different sources, so that multiple views could be transformed to the same coordinates system. This enables the functionality of automatic object identification and confirmation across views and platforms. The capability of the system comes from three ingredients: 1) image context enlargement through temporal integration; 2) robust motion estimation using the G-RANSAC framework with a relaxed correspondence criteria; 3) constrained motion estimation within the G-RANSAC framework. The proposed system has worked successfully on thousands of frames from multiple collections with significant variations in scale and resolution.},
	keywords = {automatic object identification, Buildings, CAMERAS, Conferences, constrained motion estimation, coordinates system, Estimation, G-RANSAC framework, image context enlargement, Image Enhancement, image registration, image sequence registration, Image sequences, Motion estimation, Robustness, temporal integration, variable image remapping},
	isbn = {978-1-4577-1304-0},
	doi = {10.1109/ICIP.2011.6115729},
	author = {Wei Zhang and Yanlin Guo and Meth, R. and Sokoloff, H. and Pope, A. and Strat, T. and Chellapa, Rama}
}
@article {12474,
	title = {Applications of a Simple Characterization of Human Gait in Surveillance},
	journal = {Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on},
	volume = {40},
	year = {2010},
	month = {2010/08//},
	pages = {1009 - 1020},
	abstract = {Applications of a simple spatiotemporal characterization of human gait in the surveillance domain are presented. The approach is based on decomposing a video sequence into x-t slices, which generate periodic patterns referred to as double helical signatures (DHSs). The features of DHS are given as follows: 1) they naturally encode the appearance and kinematics of human motion and reveal geometric symmetries and 2) they are effective and efficient for recovering gait parameters and detecting simple events. We present an iterative local curve embedding algorithm to extract the DHS from video sequences. Two applications are then considered. First, the DHS is used for simultaneous segmentation and labeling of body parts in cluttered scenes. Experimental results showed that the algorithm is robust to size, viewing angles, camera motion, and severe occlusion. Then, the DHS is used to classify load-carrying conditions. By examining various symmetries in DHS, activities such as carrying, holding, and walking with objects that are attached to legs are detected. Our approach possesses several advantages: a compact representation that can be computed in real time is used; furthermore, it does not depend on silhouettes or landmark tracking, which are sensitive to errors in background subtraction stage.},
	keywords = {algorithms, Artificial intelligence, Automated;Photography;Reproducibility of Results;Sensitivity and Specificity;Video Recording;, Biometry, Computer-Assisted;Pattern Recognition, double helical signatures, Gait, gait analysis, human gait, human motion kinematics, HUMANS, Image Enhancement, Image Interpretation, Image motion analysis, iterative local curve embedding algorithm, Object detection, simple spatiotemporal characterization, video sequence, Video surveillance},
	isbn = {1083-4419},
	doi = {10.1109/TSMCB.2010.2044173},
	author = {Yang Ran and Qinfen Zheng and Chellapa, Rama and Strat, T.M.}
}
@article {12475,
	title = {Robust Height Estimation of Moving Objects From Uncalibrated Videos},
	journal = {IEEE Transactions on Image Processing},
	volume = {19},
	year = {2010},
	month = {2010/08//},
	pages = {2221 - 2232},
	abstract = {This paper presents an approach for video metrology. From videos acquired by an uncalibrated stationary camera, we first recover the vanishing line and the vertical point of the scene based upon tracking moving objects that primarily lie on a ground plane. Using geometric properties of moving objects, a probabilistic model is constructed for simultaneously grouping trajectories and estimating vanishing points. Then we apply a single view mensuration algorithm to each of the frames to obtain height measurements. We finally fuse the multiframe measurements using the least median of squares (LMedS) as a robust cost function and the Robbins-Monro stochastic approximation (RMSA) technique. This method enables less human supervision, more flexibility and improved robustness. From the uncertainty analysis, we conclude that the method with auto-calibration is robust in practice. Results are shown based upon realistic tracking data from a variety of scenes.},
	keywords = {algorithms, Biometry, Calibration, EM algorithm, geometric properties, Geometry, Image Enhancement, Image Interpretation, Computer-Assisted, Imaging, Three-Dimensional, least median of squares, least squares approximations, MOTION, motion information, multiframe measurements, Pattern Recognition, Automated, Reproducibility of results, Robbins-Monro stochastic approximation, robust height estimation, Sensitivity and Specificity, Signal Processing, Computer-Assisted, stochastic approximation, Subtraction Technique, tracking data, uncalibrated stationary camera, uncalibrated videos, uncertainty analysis, vanishing point, video metrology, Video Recording, video signal processing},
	isbn = {1057-7149},
	doi = {10.1109/TIP.2010.2046368},
	author = {Jie Shao and Zhou,S. K and Chellapa, Rama}
}
@conference {15585,
	title = {Enhancements in robust feature matching},
	booktitle = {Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, 2008. ECTI-CON 2008. 5th International Conference on},
	volume = {1},
	year = {2008},
	month = {2008/05//},
	pages = {505 - 508},
	abstract = {We present in this paper a number of new enhancements to a branch-and-bound algorithm given by Mount, Netanyahu, and Le Moigne [8] for feature matching. We introduce a new distance measure, which is more robust to the presence of outliers than the previously used measure, as well as a new variant of the search algorithm and a new search strategy. We also present experimental results, which show that these enhancements offer significant tangible improvements in performance.},
	keywords = {branch-and-bound algorithm, distance measure, Image Enhancement, Image matching, robust feature matching, tree searching},
	doi = {10.1109/ECTICON.2008.4600481},
	author = {Ratanasanya,S. and Mount, Dave and Netanyahu,N. S and Achalakul,T.}
}
@article {12544,
	title = {Model Driven Segmentation of Articulating Humans in Laplacian Eigenspace},
	journal = {Pattern Analysis and Machine Intelligence, IEEE Transactions on},
	volume = {30},
	year = {2008},
	month = {2008/10//},
	pages = {1771 - 1785},
	abstract = {We propose a general approach using Laplacian Eigenmaps and a graphical model of the human body to segment 3D voxel data of humans into different articulated chains. In the bottom-up stage, the voxels are transformed into a high-dimensional (6D or less) Laplacian Eigenspace (LE) of the voxel neighborhood graph. We show that LE is effective at mapping voxels on long articulated chains to nodes on smooth 1D curves that can be easily discriminated, and prove these properties using representative graphs. We fit 1D splines to voxels belonging to different articulated chains such as the limbs, head and trunk, and determine the boundary between splines using the spline fitting error. A top-down probabilistic approach is then used to register the segmented chains, utilizing their mutual connectivity and individual properties. Our approach enables us to deal with complex poses such as those where the limbs form loops. We use the segmentation results to automatically estimate the human body models. While we use human subjects in our experiments, the method is fairly general and can be applied to voxel-based segmentation of any articulated object composed of long chains. We present results on real and synthetic data that illustrate the usefulness of this approach.},
	keywords = {3D voxel data segmentation, algorithms, Anatomic;Pattern Recognition, Artificial intelligence, Automated;Reproducibility of Results;Sensitivity and Specificity;Whole Body Imaging;, Computer simulation, Computer-Assisted;Imaging, curve fitting, Eigenvalues and eigenfunctions, graph theory, human articulation, human body graphical model, human motion analysis, HUMANS, Image Enhancement, Image Interpretation, Image motion analysis, image registration, Image segmentation, Laplace transforms, Laplacian eigenspace transform, model driven segmentation, probability, representative graph, skeleton registration, Smoothing methods, solid modelling, spline fitting error, splines (mathematics), Three-Dimensional;Joints;Models, top-down probabilistic approach, voxel neighborhood graph},
	isbn = {0162-8828},
	doi = {10.1109/TPAMI.2007.70823},
	author = {Sundaresan, A. and Chellapa, Rama}
}
@conference {15916,
	title = {A new multiresolution generalized directional filter bank design and application in image enhancement},
	booktitle = {15th IEEE International Conference on Image Processing, 2008. ICIP 2008},
	year = {2008},
	month = {2008/10//},
	pages = {2816 - 2819},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {In this paper, we present an image enhancement technique based on a new multiscale generalized directional filter bank design. The design presented is a shift-invariant overcomplete representation, which is well suited to extracting geometric features such as edges. Special cases of this design method can be made to reduce to different and improved implementations of the shearlet and the contourlet transforms, which are known to represent certain classes of images optimally. Use of this new filter bank design has proven itself competitive in image restoration for noisy images and is well suited for distinguishing noise from weak edges. Experimental results show that our unique image enhancement technique out-performs wavelet and contourlet based enhancement methods.},
	keywords = {Algorithm design and analysis, Approximation error, Channel bank filters, contourlet transform, Design methodology, Discrete transforms, Feature extraction, Filter bank, Frequency, geometric feature extraction, Image Enhancement, IMAGE PROCESSING, image resolution, image restoration, Multidimensional digital filters, Multidimensional systems, multiresolution generalized directional filter bank design, shearlet transform, shift-invariant overcomplete representation, transforms, Wavelet transforms},
	isbn = {978-1-4244-1765-0},
	doi = {10.1109/ICIP.2008.4712380},
	author = {Patel, Vishal M. and Easley,G. R and Healy,D. M}
}
@article {14182,
	title = {A 3D shape constraint on video},
	journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
	volume = {28},
	year = {2006},
	month = {2006/06//},
	pages = {1018 - 1023},
	abstract = {We propose to combine the information from multiple motion fields by enforcing a constraint on the surface normals (3D shape) of the scene in view. The fact that the shape vectors in the different views are related only by rotation can be formulated as a rank = 3 constraint. This constraint is implemented in an algorithm which solves 3D motion and structure estimation as a practical constrained minimization. Experiments demonstrate its usefulness as a tool in structure from motion providing very accurate estimates of 3D motion.},
	keywords = {3D motion estimation, algorithms, Artificial intelligence, CAMERAS, decoupling translation from rotation, Estimation error, Fluid flow measurement, Image Enhancement, Image Interpretation, Computer-Assisted, Image reconstruction, Imaging, Three-Dimensional, Information Storage and Retrieval, integration of motion fields, Layout, minimisation, Minimization methods, Motion estimation, multiple motion fields, parameter estimation, Pattern Recognition, Automated, Photography, practical constrained minimization, SHAPE, shape and rotation., shape vectors, stability, structure estimation, surface normals, Three-dimensional motion estimation, video 3D shape constraint, Video Recording, video signal processing},
	isbn = {0162-8828},
	doi = {10.1109/TPAMI.2006.109},
	author = {Hui Ji and Ferm{\"u}ller, Cornelia}
}
@article {19662,
	title = {MCMC Data Association and Sparse Factorization Updating for Real Time Multitarget Tracking with Merged and Multiple Measurements},
	journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
	volume = {28},
	year = {2006},
	month = {2006/12//},
	pages = {1960 - 1972},
	abstract = {In several multitarget tracking applications, a target may return more than one measurement per target and interacting targets may return multiple merged measurements between targets. Existing algorithms for tracking and data association, initially applied to radar tracking, do not adequately address these types of measurements. Here, we introduce a probabilistic model for interacting targets that addresses both types of measurements simultaneously. We provide an algorithm for approximate inference in this model using a Markov chain Monte Carlo (MCMC)-based auxiliary variable particle filter. We Rao-Blackwellize the Markov chain to eliminate sampling over the continuous state space of the targets. A major contribution of this work is the use of sparse least squares updating and downdating techniques, which significantly reduce the computational cost per iteration of the Markov chain. Also, when combined with a simple heuristic, they enable the algorithm to correctly focus computation on interacting targets. We include experimental results on a challenging simulation sequence. We test the accuracy of the algorithm using two sensor modalities, video, and laser range data. We also show the algorithm exhibits real time performance on a conventional PC},
	keywords = {algorithms, approximate inference, Artificial intelligence, auxiliary variable particle filter, Computational efficiency, continuous state space, downdating, Image Enhancement, Image Interpretation, Computer-Assisted, Inference algorithms, Information Storage and Retrieval, laser range scanner, laser range scanner., Least squares approximation, least squares approximations, Least squares methods, linear least squares, Markov chain Monte Carlo, Markov processes, MCMC data association, merged measurements, Monte Carlo methods, Movement, multiple merged measurements, multitarget tracking, particle filter, particle filtering (numerical methods), Particle filters, Pattern Recognition, Automated, probabilistic model, QR factorization, Radar tracking, Rao-Blackwellized, real time multitarget tracking, Reproducibility of results, Sampling methods, Sensitivity and Specificity, sensor fusion, sparse factorization updating, sparse least squares, State-space methods, Subtraction Technique, target tracking, updating},
	isbn = {0162-8828},
	author = {Zia Khan and Balch, T. and Dellaert, F.}
}
@article {12618,
	title = {Robust ego-motion estimation and 3-D model refinement using surface parallax},
	journal = {Image Processing, IEEE Transactions on},
	volume = {15},
	year = {2006},
	month = {2006/05//},
	pages = {1215 - 1225},
	abstract = {We present an iterative algorithm for robustly estimating the ego-motion and refining and updating a coarse depth map using parametric surface parallax models and brightness derivatives extracted from an image pair. Given a coarse depth map acquired by a range-finder or extracted from a digital elevation map (DEM), ego-motion is estimated by combining a global ego-motion constraint and a local brightness constancy constraint. Using the estimated camera motion and the available depth estimate, motion of the three-dimensional (3-D) points is compensated. We utilize the fact that the resulting surface parallax field is an epipolar field, and knowing its direction from the previous motion estimates, estimate its magnitude and use it to refine the depth map estimate. The parallax magnitude is estimated using a constant parallax model (CPM) which assumes a smooth parallax field and a depth based parallax model (DBPM), which models the parallax magnitude using the given depth map. We obtain confidence measures for determining the accuracy of the estimated depth values which are used to remove regions with potentially incorrect depth estimates for robustly estimating ego-motion in subsequent iterations. Experimental results using both synthetic and real data (both indoor and outdoor sequences) illustrate the effectiveness of the proposed algorithm.},
	keywords = {3D model refinement, algorithms, Artificial intelligence, Automated;Subtraction Technique;, Computer-Assisted;Imaging, constant parallax model, depth based parallax model, digital elevation map, epipolar field, Image Enhancement, Image Interpretation, iterative algorithm, iterative methods, Motion estimation, robust ego-motion estimation, smooth parallax field, surface parallax, Three-Dimensional;Information Storage and Retrieval;Pattern Recognition},
	isbn = {1057-7149},
	doi = {10.1109/TIP.2005.864167},
	author = {Agrawal,A. and Chellapa, Rama}
}
@article {19663,
	title = {MCMC-based particle filtering for tracking a variable number of interacting targets},
	journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
	volume = {27},
	year = {2005},
	month = {2005/11//},
	pages = {1805 - 1819},
	abstract = {We describe a particle filter that effectively deals with interacting targets, targets that are influenced by the proximity and/or behavior of other targets. The particle filter includes a Markov random field (MRF) motion prior that helps maintain the identity of targets throughout an interaction, significantly reducing tracker failures. We show that this MRF prior can be easily implemented by including an additional interaction factor in the importance weights of the particle filter. However, the computational requirements of the resulting multitarget filter render it unusable for large numbers of targets. Consequently, we replace the traditional importance sampling step in the particle filter with a novel Markov chain Monte Carlo (MCMC) sampling step to obtain a more efficient MCMC-based multitarget filter. We also show how to extend this MCMC-based filter to address a variable number of interacting targets. Finally, we present both qualitative and quantitative experimental results, demonstrating that the resulting particle filters deal efficiently and effectively with complicated target interactions.},
	keywords = {algorithms, Animals, Artificial intelligence, Computer simulation, Computer vision, Filtering, filtering theory, HUMANS, Image Enhancement, Image Interpretation, Computer-Assisted, Index Terms- Particle filters, Information Storage and Retrieval, Insects, interacting targets, Markov chain Monte Carlo sampling step, Markov chain Monte Carlo., Markov chains, Markov processes, Markov random field motion, Markov random fields, Models, Biological, Models, Statistical, Monte Carlo Method, Monte Carlo methods, MOTION, Movement, multitarget filter, multitarget tracking, particle filtering, Particle filters, Particle tracking, Pattern Recognition, Automated, Sampling methods, Subtraction Technique, target tracking, Video Recording},
	isbn = {0162-8828},
	author = {Zia Khan and Balch, T. and Dellaert, F.}
}
@article {14230,
	title = {Motion segmentation using occlusions},
	journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
	volume = {27},
	year = {2005},
	month = {2005/06//},
	pages = {988 - 992},
	abstract = {We examine the key role of occlusions in finding independently moving objects instantaneously in a video obtained by a moving camera with a restricted field of view. In this problem, the image motion is caused by the combined effect of camera motion (egomotion), structure (depth), and the independent motion of scene entities. For a camera with a restricted field of view undergoing a small motion between frames, there exists, in general, a set of 3D camera motions compatible with the observed flow field even if only a small amount of noise is present, leading to ambiguous 3D motion estimates. If separable sets of solutions exist, motion-based clustering can detect one category of moving objects. Even if a single inseparable set of solutions is found, we show that occlusion information can be used to find ordinal depth, which is critical in identifying a new class of moving objects. In order to find ordinal depth, occlusions must not only be known, but they must also be filled (grouped) with optical flow from neighboring regions. We present a novel algorithm for filling occlusions and deducing ordinal depth under general circumstances. Finally, we describe another category of moving objects which is detected using cardinal comparisons between structure from motion and structure estimates from another source (e.g., stereo).},
	keywords = {3D motion estimation, algorithms, Artificial intelligence, CAMERAS, Computer vision, Filling, hidden feature removal, Image Enhancement, Image Interpretation, Computer-Assisted, image motion, Image motion analysis, Image segmentation, Layout, MOTION, Motion detection, Motion estimation, motion segmentation, Movement, Object detection, occlusion, occlusions, optical flow, ordinal depth, Pattern Recognition, Automated, Photography, Reproducibility of results, segmentation, Semiconductor device modeling, Sensitivity and Specificity, video analysis., Video Recording},
	isbn = {0162-8828},
	doi = {10.1109/TPAMI.2005.123},
	author = {Ogale, A. S and Ferm{\"u}ller, Cornelia and Aloimonos, J.}
}
@article {12654,
	title = {Statistical bias in 3-D reconstruction from a monocular video},
	journal = {Image Processing, IEEE Transactions on},
	volume = {14},
	year = {2005},
	month = {2005/08//},
	pages = {1057 - 1062},
	abstract = {The present state-of-the-art in computing the error statistics in three-dimensional (3-D) reconstruction from video concentrates on estimating the error covariance. A different source of error which has not received much attention is the fact that the reconstruction estimates are often significantly statistically biased. In this paper, we derive a precise expression for the bias in the depth estimate, based on the continuous (differentiable) version of structure from motion (SfM). Many SfM algorithms, or certain portions of them, can be posed in a linear least-squares (LS) framework Ax=b. Examples include initialization procedures for bundle adjustment or algorithms that alternately estimate depth and camera motion. It is a well-known fact that the LS estimate is biased if the system matrix A is noisy. In SfM, the matrix A contains point correspondences, which are always difficult to obtain precisely; thus, it is expected that the structure and motion estimates in such a formulation of the problem would be biased. Existing results on the minimum achievable variance of the SfM estimator are extended by deriving a generalized Cramer-Rao lower bound. A detailed analysis of the effect of various camera motion parameters on the bias is presented. We conclude by presenting the effect of bias compensation on reconstructing 3-D face models from rendered images.},
	keywords = {3D face models, 3D video reconstruction, algorithms, artifacts, Artificial intelligence, Automated;Signal Processing, bias compensation, bundle adjustment, camera motion estimation, Computer simulation, Computer-Assisted;Imaging, Computer-Assisted;Subtraction Technique;Video Recording;, depth estimate, error covariance estimation, error statistics, generalized Cramer-Rao lower bound, Image Enhancement, Image Interpretation, Image reconstruction, initialization procedures, least squares approximations, linear least-squares framework, monocular video, motion compensation, Motion estimation, statistical bias, Statistical;Pattern Recognition, structure from motion algorithms, Three-Dimensional;Information Storage and Retrieval;Models, video signal processing},
	isbn = {1057-7149},
	doi = {10.1109/TIP.2005.849775},
	author = {Roy-Chowdhury, A.K. and Chellapa, Rama}
}
@article {12698,
	title = {An information theoretic criterion for evaluating the quality of 3-D reconstructions from video},
	journal = {Image Processing, IEEE Transactions on},
	volume = {13},
	year = {2004},
	month = {2004/07//},
	pages = {960 - 973},
	abstract = {Even though numerous algorithms exist for estimating the three-dimensional (3-D) structure of a scene from its video, the solutions obtained are often of unacceptable quality. To overcome some of the deficiencies, many application systems rely on processing more data than necessary, thus raising the question: how is the accuracy of the solution related to the amount of data processed by the algorithm? Can we automatically recognize situations where the quality of the data is so bad that even a large number of additional observations will not yield the desired solution? Previous efforts to answer this question have used statistical measures like second order moments. They are useful if the estimate of the structure is unbiased and the higher order statistical effects are negligible, which is often not the case. This paper introduces an alternative information-theoretic criterion for evaluating the quality of a 3-D reconstruction. The accuracy of the reconstruction is judged by considering the change in mutual information (MI) (termed as the incremental MI) between a scene and its reconstructions. An example of 3-D reconstruction from a video sequence using optical flow equations and known noise distribution is considered and it is shown how the MI can be computed from first principles. We present simulations on both synthetic and real data to demonstrate the effectiveness of the proposed criterion.},
	keywords = {3D reconstruction, algorithms, Artificial intelligence, Automated;Reproducibility of Results;Sensitivity and Specificity;Signal Processing, Computer Graphics, Computer-Assisted;Imaging, Computer-Assisted;Software Validation;Subtraction Technique;Video Recording;, Image Enhancement, Image Interpretation, Image reconstruction, Image sequences, information theoretic criterion, Mutual information, NOISE, noise distribution, optical flow equations, second order moments, statistical analysis, Three-Dimensional;Information Storage and Retrieval;Information Theory;Movement;Pattern Recognition, Video sequences, video signal processing},
	isbn = {1057-7149},
	doi = {10.1109/TIP.2004.827240},
	author = {Roy-Chowdhury, A.K. and Chellapa, Rama}
}
@article {12682,
	title = {Visual tracking and recognition using appearance-adaptive models in particle filters},
	journal = {IEEE Transactions on Image Processing},
	volume = {13},
	year = {2004},
	month = {2004/11//},
	pages = {1491 - 1506},
	abstract = {We present an approach that incorporates appearance-adaptive models in a particle filter to realize robust visual tracking and recognition algorithms. Tracking needs modeling interframe motion and appearance changes, whereas recognition needs modeling appearance changes between frames and gallery images. In conventional tracking algorithms, the appearance model is either fixed or rapidly changing, and the motion model is simply a random walk with fixed noise variance. Also, the number of particles is typically fixed. All these factors make the visual tracker unstable. To stabilize the tracker, we propose the following modifications: an observation model arising from an adaptive appearance model, an adaptive velocity motion model with adaptive noise variance, and an adaptive number of particles. The adaptive-velocity model is derived using a first-order linear predictor based on the appearance difference between the incoming observation and the previous particle configuration. Occlusion analysis is implemented using robust statistics. Experimental results on tracking visual objects in long outdoor and indoor video sequences demonstrate the effectiveness and robustness of our tracking algorithm. We then perform simultaneous tracking and recognition by embedding them in a particle filter. For recognition purposes, we model the appearance changes between frames and gallery images by constructing the intra- and extrapersonal spaces. Accurate recognition is achieved when confronted by pose and view variations.},
	keywords = {adaptive filters, adaptive noise variance, algorithms, appearance-adaptive model, Artificial intelligence, Cluster Analysis, Computer Graphics, Computer simulation, Feedback, Filtering, first-order linear predictor, hidden feature removal, HUMANS, Image Enhancement, Image Interpretation, Computer-Assisted, image recognition, Information Storage and Retrieval, Kinematics, Laboratories, Male, Models, Biological, Models, Statistical, MOTION, Movement, Noise robustness, Numerical Analysis, Computer-Assisted, occlusion analysis, Particle filters, Particle tracking, Pattern Recognition, Automated, Predictive models, Reproducibility of results, robust statistics, Sensitivity and Specificity, Signal Processing, Computer-Assisted, State estimation, statistical analysis, Subtraction Technique, tracking, Training data, visual recognition, visual tracking},
	isbn = {1057-7149},
	doi = {10.1109/TIP.2004.836152},
	author = {Zhou,Shaohua Kevin and Chellapa, Rama and Moghaddam, B.}
}
@article {17849,
	title = {The virtual microscope},
	journal = {IEEE Transactions on Information Technology in Biomedicine},
	volume = {7},
	year = {2003},
	month = {2003/12//},
	pages = {230 - 248},
	abstract = {We present the design and implementation of the virtual microscope, a software system employing a client/server architecture to provide a realistic emulation of a high power light microscope. The system provides a form of completely digital telepathology, allowing simultaneous access to archived digital slide images by multiple clients. The main problem the system targets is storing and processing the extremely large quantities of data required to represent a collection of slides. The virtual microscope client software runs on the end user{\textquoteright}s PC or workstation, while database software for storing, retrieving and processing the microscope image data runs on a parallel computer or on a set of workstations at one or more potentially remote sites. We have designed and implemented two versions of the data server software. One implementation is a customization of a database system framework that is optimized for a tightly coupled parallel machine with attached local disks. The second implementation is component-based, and has been designed to accommodate access to and processing of data in a distributed, heterogeneous environment. We also have developed caching client software, implemented in Java, to achieve good response time and portability across different computer platforms. The performance results presented show that the Virtual Microscope systems scales well, so that many clients can be adequately serviced by an appropriately configured data server.},
	keywords = {biomedical optical imaging, client software, client/server architecture, Computer architecture, Computer Graphics, computer platforms, Computer simulation, Concurrent computing, configured data server, data server software, database management systems, database software, Database systems, digital slide images, digital telepathology, diseases, emulation, Environment, Equipment Design, Equipment Failure Analysis, high power light microscope, Image databases, Image Enhancement, Image Interpretation, Computer-Assisted, Image retrieval, Information retrieval, Information Storage and Retrieval, java, local disks, microscope image data, Microscopy, multiple clients, optical microscopy, PACS, software, Software design, software system, Software systems, Systems Integration, Telepathology, User-Computer Interface, virtual microscope design, Virtual reality, Workstations},
	isbn = {1089-7771},
	doi = {10.1109/TITB.2004.823952},
	author = {Catalyurek,U. and Beynon,M. D and Chang,Chialin and Kurc, T. and Sussman, Alan and Saltz, J.}
}
