Lead, machine vision and perception section, the Johns Hopkins University Applied Physics Laboratory Research Center
Joint faculty appointment, the Johns Hopkins University School of Medicine, Wilmer Eye Institute.
V.P. engineering at eGrail and Director of software development at FileNET (now part of IBM), a company developig enterprise content management and eprocess management software platforms. Co-founder, ImageCorp (now part of SAIC), a company pursuing R&D in machine vision. M.S. and a Ph.D. in Electrical Engineering from the University of Maryland at College Park in Communications and Control. Diplôme d’Ingénieur, Université de Technologie de Compiègne, France.
My work spans several areas of machine perception and software engineering, including machine vision, biomedical image analysis, hyperspectral video, machine learning, enterprise software systems, content and e-process management, and SDLC.
Biomedical Imaging: image analysis for pre-operative surgical planning, patient specific biomechanical modeling, real-time 3D echocardiographic image analysis, retinal image analysis, cell image analysis, hyperspectral medical imaging.
Machine Vision: tracking, motion analysis, bio-mimetic algorithms, distributed visual sensing, machine learning for machine vision.
Hyperspectral Video: tracking, detection, and classification in hyperspectral image and video.
Preoperative surgical planning
We are interested in the exploitation of real time 3D echographic imaging data to develop accurate, patient-specific computational models of the heart to allow surgeons to preoperatively test and simulate reconstructive cardiovascular surgical interventions.
Cardiovascular disease (CVD) is one of the leading causes of death among Americans. The primary treatment for many types of CVDs entails some form of cardiac surgical reconstruction. The complex physiology and 3D anatomy of the heart presents substantial challenges when performing many of these reconstructive operations. These repairs are generally performed on an arrested heart under cardiopulmonary bypass. Because of the complexity and critical nature of this problem, it is important to develop computer aided methods allowing surgeons to preoperatively create more precise surgical plans for a given patient.
Our principal use case is mitral valve disease because of its significance among CVDs and its clinical relevance. Our goals have been to develop the 3D and 4D echocardiographic image analysis tools for computing patient-specific 3D motion and anatomical information through automated segmentation, mesh generation, velocity flow estimation, and dynamic tracking. Using these primitives we have worked to develop modifiable computational biomechanical models that accurately predict the mitral valve closure behavior resulting from a virtual surgical reconstruction. We have also worked to carefully validate our anatomical and computational models. The example below shows an original 3D image obtained using real time 3D Transesophageal Echocardiography (3D TEE), along with the corresponding segmented endocardial walls, and the reconstructed mitral valve model.
4D Ultrasound Image Exploitation
Real time 3D ultrasound (also called 4D ultrasound) is an imaging modality that is unmatched for its ability to image the very fast motion of certain structures in the heart complex, such as the mitral and aortic valves. It has other benefits when compared to other 3D imaging modalities in terms of costs, form factor, and safety. It has however certain drawbacks in terms of image quality (artifacts, obscuration, resolution) which make the automatic segmentation of anatomical structures more challenging.
Patient Specific Biomechanical Modeling
Part of our efforts have consisted in developing computational and physical models of the mitral valve and the heart complex to predict the outcome of a candidate surgical procedure, answering the question "will the candidate reconstructed valve close competently?”. Our modeling approach takes as a starting point the patient specific valve anatomy, recovered from 3D echocardiography, and modified to incorporate the surgeon's proposed reconstruction. It then performs a shape finding procedure using a finite element approach to predict the closed valve configuration based on physical modeling of the closure. It assumes that the valve is subject to several forces including blood pressure, internal hyperelastic forces, tethering forces from the chordae tendineae, and collision forces to avoid leaflets intersection. We predict the closed valve configuration by using an energy minimization process which finds the valve at the closed equilibrium position during systole. The figure below shows a set of intermediary steps in the computation of the closed valve configuration from an assumed open configuration. We have also been developing dynamical models that aim to evaluate the behavior of the valve when immersed in patient specific hemodynamic conditions.
In addition to modeling, we have been pursuing validation and have been interested in means by which echographic imagery can be combined with echogenic contrast agents to tease out myocardial and blood velocity fields information. The images below show the computation of blood flow through the intraventricular chamber during diastolic and systolic phases using contrast enhanced ultrasound. This information can be exploited using machine learning techniques to provide automated diagnostics. It is also important from a scientific perspective to elucidate the mechanisms that accompany certain heart pathologies such as hypertrophic cardiomyopathy.
Automated Retinal Image Analysis
We have been interested in developing novel methods to pre-screen individuals for retinal anomalies such as Age Related Macular Degeneration (AMD) or Diabetic Retinopathy. AMD is often asymptomatic in early and intermediate stages with regard to vision loss. When undetected and untreated, AMD can advance to a neo-vascular form leading to blindness. We have developed methods to automatically find individuals with intermediate stage AMD which are candidate for anti-Vascular Endothelial Growth Factor (VEGF) therapy where vision can be stabilized. Our diagnostic relies on automated detection of drusen in fundus imagery using machine learning techniques.
Analysis of Cell Microscopy Video
We are interested in methods for tracking the movement of multiple cells and their lineage in microfluidics chips. We have developed methods based on the Gaussian Mixture Probability Hypothesis Density (GM-PHD) filter, a multi-target tracking algorithm, to track the motion of multiple cells over time and to keep track of the lineage of cells as they spawn.
We have been exploring novel methods for performing visual tracking and Bayesian filtering. We have developed kernel based methods to perform efficient Bayesian filtering using particle filters. Particle filters (PFs) are Bayesian filters capable of modeling non-linear, non-Gaussian, and non-stationary dynamical systems. Recent research in PFs has investigated ways to appropriately sample from the posterior distribution, maintain multiple hypotheses, and alleviate computational costs while preserving tracking accuracy. To address these issues, we have proposed to use Support Vector Data Description (SVDD) density estimation methods within the particle filtering framework. The SVDD density estimate can be integrated into various types of PFs yielding several benefits. It yields a sparse representation of the posterior density that reduces the computational complexity of the PF. The proposed approach also provides an analytical expression for the posterior distribution that can be used to identify its modes for maintaining multiple hypotheses and computing the MAP estimate, and to directly sample from the posterior.
have also been investigating ways to perform Bayesian filtering in the
presence of multiple movers while obviating the need for establishing
correspondences in time between tokens, by exploiting recent
developments in unlabeled tracking such as the Probabilistic Hypothesis
Density filter (PHD).
Computer vision research has traditionally considered that visual information acquired by multiple cameras is gathered together and processed by a single central computer. The alternative scenario we have been investigating involves multiple cameras trying to reach a consensus about what they see by locally processing partial information while communicating with only a few of their neighboring cameras through communication channels with limited bandwidth. We have examined how such processing cameras can best reach a consensus about the pose of an object when they each know a model of the object, defined by a set of world point coordinates, but can potentially only see a subset of these points in the midst of clutter points from the background, not knowing at first which image points match which object points, nor which points are object points or background points. We have shown that the cameras can reach the most accurate pose consensus by exchanging the parameters characterizing the object's pose which are generated by 3D world coordinates penalized to agree with the input model. The cameras use these parameters to reconstruct the object's world coordinates using their knowledge of the model, and perform consensus updates on these world coordinates.
We are interested in video exploitation using video rate hyperspectral sensors. Hyperspectral video cameras are much like traditional cameras but measure dozen or hundreds of spectral bands in place of the three RGB channels found in current video cameras, therefore offering additional benefits for detection, classification, or tracking tasks. The exploitation and automated analysis of visual input has been the goal of the computer vision community, while hyperspectral cameras have been mostly studied and exploited by the remote sensing research community. Video rate hyperspectral cameras offer a triplet of spatial, temporal and spectral information and promise to combine capabilities studied by both fields of research.
Journals, book chapters
Mukherjee, C. Sprouse, A. Pinheiro, T. Abraham, P. Burlina,” Computing
Myocardial Motion in 4D Echocardiography”, Ultrasound in Medicine and
Biology, 2012, Vol. 38, No. 7, July 2012.
P. Burlina, C. Sprouse, D. DeMenthon, R. Mukherjee, and T. Abraham, “Towards Mitral Valve Closure Prediction using 3D Echocardiography”, IEEE T. Medical Imaging, (in review), 2012.
R. Mukherjee, R. Juang, C. Sprouse, and P. Burlina, “Endocardial Surface Delineation in 3D Transesophageal Echocardiography”, Ultrasound in Medicine and Biology, (in review), 2012.
B. Hoffmann and P. Burlina, “Ultrasound in Trauma – From Civilian to the Combat Casualty Environment”, Shock, 2012, in press.
H.V. Nguyen, A. Banerjee, P. Burlina, J. Broadwater, R. Chellappa, "Tracking And Id Via Object Reflectance Using A Hyperspectral Video Camera", Machine Vision Beyond Visible Spectrum, Guoliang Fan (Ed.), Springer 2011.
A. Banerjee, P. Burlina, and C. Diehl, “One Class SVM for Hyperspectral Anomaly Detection”, in “Kernel Methods for Remote Sensing Data Analysis”, Gustavo Camps-Valls and Lorenzo Bruzzone, editors, Wiley & Sons, 2009.
J. Broadwater, A. Banerjee, P. Burlina, and R. Chellappa, “Kernel Methods for Unmixing Hyperspectral Imagery”, in “Kernel Methods for Remote Sensing Data Analysis”, Gustavo Camps-Valls and Lorenzo Bruzzone, editors, Wiley & Sons, 2009.
A. Banerjee, P. Burlina and C. Diehl, “A Support Vector Method for Anomaly Detection in Hyperspectral Imagery”, IEEE Transactions on Geoscience and Remote Sensing, Vol. 44, No. 8, pp. 2282-2291, 2006.
C. BenAbdelkader, P. Burlina, and L. S. Davis, “Single Camera Multiplexing for Multi-Target Tracking”, in “Advanced Video-based Surveillance Systems”, Ed. by Carlo Regazzoni et al. Kluwer Academic Publishers, 2000.
A. Banerjee, P. Burlina, and R. Chellappa, “Adaptive target detection in foliage-penetrating SAR images using alpha-stable models”, IEEE Transactions on Image Processing, Vol. 8, No. 12, December 1999.
R. Chellappa, P. Burlina, X. Zhang, Q. Zheng, C. L. Lin, V. Parameswaran, L. Davis, and A. Rosenfeld, “Site Model Mediated Detection of Movable Objects Activities”, in RADIUS: Image Understanding for Imagery Intelligence, (O.Firshein, ed.), Morgan Kaufman, pp 285-317, May 1997.
R. Chellappa, Q. Zheng, S. Kuttikkad, C. Shekhar, and P. Burlina, “Site Model Construction for the Exploitation of EO and SAR Imagery”, in RADIUS: Image Understanding for Imagery Intelligence, (O.Firshein, ed.), Morgan Kaufman, pp 185-208, May 1997.
R. Chellappa, Q. Zheng, C. Shekhar, and P. Burlina, “Site Model Supported Targeting”, in RADIUS: Image Understanding for Imagery Intelligence, (O.Firshein, ed.), Morgan Kaufman, pp 357-369, May 1997.
R. Chellappa, Q. Zheng, P. Burlina, C. Shekhar, and K. B. Eom, “On the Positioning of Multisensor Imagery for Exploitation and Target Recognition”, Proceedings of the IEEE, Vol. 85, No. 1, pp 120-138, January 1997.
P. Burlina and R. Chellappa, “Analyzing Looming Motion Components from their Spatiotemporal Spectral Signature”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 18, No. 10, pp 1029-1034, October 1996.
R. Chellappa, T. Wu, P. Burlina, and Q. Zheng, “Visual Motion Analysis”, in Control and Dynamic Systems: Advances in Theory and Applications, Vol. 67, (C.T. Leondes, ed.), Academic Press, 1994.
P. Burlina, R. Mukherjee, C. Sprouse, “A Personalized Mitral Valve
Closure Simulator”, IEEE Engineering Medicine and Biology Symposium,
C. Sprouse, R. Mukherjee, P. Burlina, “Valvular Closure Prediction using Anisotropic and Hyperelastic Tissue models and Individualized Anatomy Derived from RT3DE”, IEEE Engineering Medicine and Biology Symposium, 2012.
M. Fitch, M. Gross, R. Juang, W. Chowdhury, M. Pomper, R. Rodriguez, P. Burlina, “Toward Hyperspectral Imaging of PSMA Ligand for Prostate-Cancer Resection”, IEEE Int. Symp. Biomedical Imaging, 2012.
R. Mukherjee, A. Pinheiro, J. Gammie, D. Yuh, T. Abraham, E. McVeigh, and P. Burlina, Dense Myocardial Motion From 4d Ultrasound: Comparative Performance Evaluation, IEEE Int. Symp. Biomedical Imaging, 2012.
R. Meth, J. Ahn, A. Banerjee, R. Juang, P. Burlina, “Parameter estimation for support vector anomaly detection in hyperspectral imagery”, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVIII, SPIE 2012.
S. Vyas, H. V. Nguyen, A. Banerjee, P. Burlina, R. Chellappa, “Computational modeling of skin reflectance spectra for biological parameter estimation through machine learning”, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVIII, SPIE 2012
Burlina R. Mukherjee, R. Juang, C. Sprouse, “Recovering Endocardial
Walls from 3D TEE”, in Proc. Sixth Int. Conf. on Functional Imaging and
Modeling of the Heart, 2011.
P. Burlina, B. Hoffmann, and T. Abraham, “Computing Left Ventricular Hemodynamics from Echographic Optical Flow of CEUS Microspheres”, in Proc. IEEE NIH Conf. Life Science Systems and Applications, 2011.
R. Juang, E.R. McVeigh, B. Hoffmann, D.
Yuh, and P. Burlina “Automatic Segmentation of the Left-Ventricular
Cavity and Atrium in 3D Ultrasound Using Graph Cuts and the Radial
Symmetry Transform”, in Proc. IEEE Int. Symp. Biomed. Imaging, 2011.
R. Mukherjee, C. Sprouse, T. Abraham, B. Hoffmann, E. McVeigh, D. Yuh, and P. Burlina, “Myocardial Motion Computation In 4D Ultrasound”, in Proc. IEEE Int. Symp. Biomed. Imaging, 2011.
D. Yuh, F. Contijoch, T. Ngo, D. Herzka, P. Burlina, M. Brady, E. McVeigh, “3D-TEE Based Computational Modeling of the Mitral Valve for Surgical Planning”, Proc. Conf. of American Association for Thoracic Surgery, 2011.
P. Burlina, C. Sprouse, D. DeMenthon, A. Jorstad, R. Juang, F. Contijoch, T. Abraham, D. Yuh, E. McVeigh, “Patient Specific Modeling and Analysis of the Mitral Valve using 3D-TEE”, in Proc. 1st International Conference on Information Processing for Computer Assisted Surgical Intervention, 2010.
P. Burlina, A. Pinhero, B.
Hoffmann, D. Yuh, T. Abraham, “Estimating Blood Flow Motion In The Left
Intraventricular Cavity”, in Proc. Int. Conf. Applied Bionics and
P. Burlina, C. Sprouse, A. Jorstad, D. DeMenthon, F. Contijoch, T. Abraham, E. McVeigh, R. Juang, and D. Yuh, “Individualized Cardiothoracic Surgical Planning using Computer Aided 3D Modeling and Image Analysis”, in Proc. first International AMA-IEEE Medical Technology Conference on Individualized healthcare, 2010.
P. Burlina, D. Freund, B. Dupas, Shiri Zayit-Soudry, N. Bressler, “An Automated System for Prescreening Individuals with Retinal Abnormalities”, in Proc. first International AMA-IEEE Medical Technology Conference on Individualized healthcare, 2010.
P. Burlina, A. Pinhero, D. Yuh, T. Abraham, “Estimating Blood Flow Motion In The Left Intraventricular Cavity”, in Proc.Int. Conf. Applied Bionics and Biomechanics, 2010.
F. Contijoch, L. Fernandez-de-Manuel, T. Ngo, D. A. Herzka, J. Stearns, K. L. Grogan, M Brady, P. Burlina, A. Santos, D.D. Yuh, M.J. Ledesma-Carbayo, E. R. McVeigh, “Increasing Temporal Resolution of 3D Transeusophageal Ultrasound by Rigid Body Registration of Sequential Temporally Offset Sequences”, in Proc. IEEE Int. Symp. Biomedical Img., 2010.
A. Banerjee, R. Juang, J. Broadwater, and P. Burlina, “Sparse Feature Extraction for Support Vector Data Description Applications”, in Proc. IEEE IGARSS, 2010.
H.V. Nguyen,, A. Banerjee, P. Burlina, J. Broadwater, R. Chellappa, "Tracking via Object Reflectance using a Hyperspectral Camera", CVPR Object Tracking and Classification Beyond and in the Visible Spectrum workshop 2010.
A. Jorstad, P. Burlina, I-J. Wang, D. Lucarelli, D. DeMenthon, R. Tron, A. Terzis, R. Vidal, “Estimating the Pose of an Object in a Distributed Camera Network”, in JHU APL Tech Digest, Vol 8, No 3, 2010.
C. Sprouse, A. Jorstad, D. DeMenthon, P. Burlina, F. Continjoch, T. Ngo, D. Herzka, E. McVeigh, J. Stearns, K. Grogan, M. Brady, T. Abraham and D. Yuh, “Computational Cardiac Modeling Based on Transesophageal Echocardiographic Imaging”, in JHU APL Tech Digest, Vol 8, No 3, 2010.
D. Freund, P. Burlina, and N. Bressler, “A Machine Learning Approach to the Detection of Intermediate Stage of Age-related Macular Degeneration”, in Proc. Int. Conf. American Association for Research in Vision and Ophthalmology, 2009.
D. Freund, P. Burlina, and N. Bressler, “Automated Detection of Drusen in the Macula”, in Proc. IEEE Int. Symp. Biomedical Img., 2009.
C. Sprouse, D. Yuh, T. Abraham, P. Burlina, “Computational Hemodynamic Modeling based on Transesophageal Echocardiographic Imaging”, in Proc. IEEE Engineering in Medicine and Biology Conference, Minneapolis, Sept 2009.
D. Freund, P. Burlina, A. Banerjee, and E. Justen, “Comparison of Kernel-Based Estimation Methods”, Proc. SPIE Automatic Target Recognition Conference, 2009.
A. Jorstad, P. Burlina, I-J. Wang, D. Lucarelli, D. DeMenthon, R. Tron, A. Terzis, R. Vidal, “Estimating the Pose of an Object in a Distributed Camera Network”, APL S&T Research Symposium, 2009.
A. Jorstad, P. Burlina, I.J. Wang, D. DeMenthon, D. Lucarelli, “Model-Based Pose Estimation by Consensus”, Proc. Int. Conf. Intelligent Sensors, Sensor Networks and Information Processing, 2008.
D. Freund, P., Burlina, and A. Banerjee, “Characterization of Spatial Ordering of Corneal Fibers”, in Proc. IEEE Int. Symp. Biomed. Img., 2008.
R. Juang, P. Burlina, and A. Banerjee, “Level Set Segmentation of Hyperspectral Images Using Joint Spectral Edge and Signature Information”, in Proc.11th International Conference on Information Fusion, 2008.
R. Meth, A. Banerjee, P. Burlina, and T. Strat, “Rapid, High Performance Hyperspectral Anomaly Detection via Global Support Vector Data Description”, Proc. SPIE, 2008.
A. Banerjee, P. Burlina, and R. Meth, “Fast Hyperspectral Anomaly Detection Via SVDD”, Proc. IEEE Int. Conf. Image Processing, 2007.
A. Banerjee, P. Burlina, and R. Meth, “Fast Hyperspectral anomaly detection using SVDD”, Proc. IEEE ICIP, San Antonio, TX, 2007.
C. Sprouse, P. Burlina, and R. Awadallah, “Polarization-Based High Resolution Radar Scatterer Classification”, Fourth IEEE (SAM-2006), Waltham, Massachusetts 12-14 July 2006.
C. BenAbdelkader, P. Burlina, and L. Davis, “Single camera multiplexing for multi-target tracking”, Proc. of IEE Int'l Conf. on Image Processing and its Application (IPA '99), pp. 1140-1143, 1999.
A. N. Rajagopalan, P. Burlina, and R. Chellappa, “Higher order statistical learning for vehicle detection in images”, Proceedings of the Seventh International Conference on Computer Vision, volume 2, pages 1204-1209, 1999.
A. N. Rajagopalan, P. Burlina, and R. Chellappa, “Detection of people in images”, Proc. International Joint Conference on Neural Networks, Vol. 4, 10-16, Pages: 2747–2752, vol. 4, 1999.
P. Burlina, “MPEG-Domain Sprite Generation”, Proc 10th International Multi Dimensional Signal Processing, July 12-16, 1998, Alpbach, Austria, 1998.
C. Shekhar, S. Moisan, R. Vincent, P. Burlina, and R. Chellappa, “Use of Knowledge-Based Control for Vision Systems”, Proceedings of the 11th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems: Tasks and Methods, pp. 427-436, 1998.
A. Banerjee, P. Burlina, R. Chellappa, and R. Kapoor, “Frequency dependence of ATD performance in foliage-penetrating SAR images”, Proc. International Conference Image Processing, Vol. 1, pp5 78 – 582, 1998.
D. Cetintemel and P. Burlina, “On-the-fly snake construction from
video”, Proc. IEEE International Conference on Image Processing, pp
638–642, vol. 3, 1998.P. Burlina and. D. Cetintemel, “On-the-Fly Road
Tracking from video”, Proc. DARPA IU Workshop, 1998.
R. Chellappa, P. Burlina, L. Davis, and A. Rosenfeld, “SAR/EO Vehicular Activity Analysis System Guided by Temporal and Contextual Information”, Proc. DARPA IU Workshop, 1998.
A. Banerjee, P. Burlina, and F. Alajaji, “Contagion-based Image Segmentation and Labeling”, Proc. International Conference on Computer Vision, (Bombay, India), January 1998.
F. Alajaji, S. Al-Semari, and P. Burlina, “An unequal error protection trellis coding scheme for still image communication”, Proceedings of 1997 IEEE International Symposium on Information Theory, 1997.
A. Banerjee and P. Burlina, “Segmentation based Detection of targets in SAR”, Proc SPIE, 1997.
P. Burlina, V. Parameswaran, and R. Chellappa, “Sensitivity analysis and learning strategies for context-based detection algorithms”, DARPA Image Understanding Workshop, New Orleans, LA, May 1997.
R. Chellappa, Q. Zheng, S. Kuttikkad, C. Shekhar, and P. Burlina, “Site Model Construction for the Exploitation of E-O and SAR Images”, in RADIUS: Image Understanding got Imagery Intelligence, (eds.), Oscar Firschein and Thomas Strat, Morgan Kaufmann Publishers, San Mateo, CA, pp. 185-208, 1997.
V. Parameswaran, P. Burlina, and R. Chellappa, “Performance Analysis and Learning Approaches for Vehicle Detection and Counting in Aerial Imagery”, Proc. ICASSP 1997.
R. Chellappa, Q. Zheng, C. Shekhar, and P. Burlina, “Site Model Supported Targeting”, DARPA Image Understanding Workshop, New Orleans, LA, May 1997.
M. Srinivasan, R. Chellappa, and P. Burlina, “Adaptive Source-Channel Subband Video Coding for Wireless Channels”, Proc. of the First IEEE Workshop on Multimedia Signal Processing, Princeton, NJ, June 1997.
C. Morimoto, P. Burlina, and R. Chellappa, “Video Coding Using Hybrid Motion Compensation”, Proc. International Conference on Image Processing, (Santa Barbara, CA), October 1997.
F. Alajaji, S. Al-Semari, and P. Burlina, “An Unequal Error Protection Scheme for Still Image Communication”, Proc. International Symposium on Information Theory, (Ulm, Germany), June 1997.
C. Shekhar, P. Burlina, and S. Moisan, “Design of self-tuning IU systems”, DARPA Image Understanding Workshop, May 1997, vol. 1, pp. 529--536, New Orleans, LA, 1997.
P. Burlina, C. L. Lin, and R. Chellappa, “On a Spectral Attentional Mechanism”, Proc. of Conference on Computer Vision and Pattern Recognition, (San Francisco, CA), June 1996.
C. Morimoto, P. Burlina, R. Chellappa, and Y. S. Yao, “Performance Analysis of Model-Based Video Coding”, Proc. International Conference on Image Processing, (Lausanne), September 1996.
F. Alajaji, P. Burlina, and R. Chellappa, “Map decoding of gray-level images over binary channels with memory”, Proc. International Conference on Image Processing (Lausanne), September 1996.
X. Zhang, P. Burlina, Q. Zheng and R. Chellappa, “Automatic Image to Site Model Registration”, Proc. International Conference on Acoustics, Speech and Signal Processing, (Atlanta, GA), June 1996.
R. Chellappa, S. Kuttikad, R. Meth, P. Burlina, K. Eom and C. Shekhar, “Model Supported Exploitation for Synthetic Aperture Radar Images”, Proc. of ARPA IU Workshop, (Palm Springs, CA), 1996.
Y. Yao, P. Burlina, and R. Chellappa, “Stabilization of Images Acquired by Unmanned Ground Vehicles”, Proc. of ARPA IU Workshop, (Palm Springs, CA), 1996.
R. Chellappa, X. Zhang, P. Burlina, Q. Zheng, C. L. Lin and A. Rosenfeld, “An Integrated System for Site Model Supported Change Detection”, Proc. of ARPA IU Workshop, (Palm Springs, CA), 1996.
Chellappa, S. Kuttikkad, R. Meth, P. Burlina, and C. Shekhar,
“Model-supported exploitation of synthetic aperture radar images”,
Proc. SPIE, 1996.
P. Burlina, F. Alajaji, and R. Chellappa, “Transmission of Two-tone Images over Noisy Channels with Memory”, Proc. International Conference on Information Theory, (Vancouver, BC), June 1995.
F. Alajaji and P. Burlina, “Image Modeling and Restoration Through Contagion Urn Schemes”, Proc. International Conference on Image Processing, (Crystal City, VA), June 1995.
R. Chellappa, P. Burlina, S. Kuttikkad, C. L. Lin, and X. Zhang, “Context-Based Exploitation of Remotely Sensed Imagery”, Proc. Context-Based Vision Workshop, (Cambridge, MA), June 1995.
Y. S. Yao, P. Burlina, R. Chellappa, and T. H. Wu, “Electronic Image Stabilization from Integrated Visual Cues”, Proc. International Conference on Image Processing" (Crystal City, VA), June 1995.
R. Chellappa, Y. S. Yao, and P. Burlina, “Processing and Understanding of Image Sequences”, Proc. Imaging Science and Technology Annual Conference, Washington, D.C., May 1995.
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