BiographyTeachingPersonal interests



Conferences organized

* Frontiers in Computer Vision - a CVPR 2012 workshop (FiCV 2012), Providence, RI, 2012.
* ICCV Workshop on Information Theory in Computer Vision and Pattern Recognition (IWITCVPR 2011), Barcelona, Spain, 2011.
* Energy Minimization Methods in Computer Vision and Pattern Recognition  (EMMCVPR 2005),  St. Augustine, FL, November 2005.
* Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR 2003), Lisbon, Portugal, July 2003.
* Mathematical Methods in Biomedical Image Analysis (MMBIA 2001), Kauai, HI, December 2001.
* Neural Information Processing Systems (NIPS) Workshop on "Statistical and Structural models for Network Vision", Vail, CO, December 1995.
* Neural Information Processing Systems (NIPS) Workshop on "Deterministic Annealing and Combinatorial Optimization", Vail, CO, December 1992.

Software/Demos

* Neil Smith's RaceSpace face database containing normalized face images from different races (partially supported by NSF IIS 1065081).
* HOSVD denoising for grayscale and color images. We acknowledge support from the National Science Foundation (NSF IIS 1143963).
* Matlab code for grayscale and color image compression.
* Matlab Toolbox for 1-D and 2-D Wavelet Density Estimation available at Adrian Peter's site.
* GatorBait_100: Fish Shape Database. We acknowledge the Department of Ichthyology, California Academy of Sciences for providing us with the source images from which the shape database was created and the support of the National Science Foundation (NSF IIS 0307712).
* Non-rigid matching research summary along with code available here. We acknowledge support from the National Science Foundation (NSF IIS 0196457).

Publications

Computer Vision and Machine Learning:

* Mark Moyou, John Corring, Adrian M. Peter and Anand Rangarajan, A Grassmannian Graph approach to affine invariant feature matching, arXiv preprint, arXiv:1601.07648v2 [cs.CV], 2016.
* Yan Deng, Anand Rangarajan and Baba Vemuri, Supervised learning for brain MR segmentation via fusion of partially labeled multiple atlases, IEEE International Symposium on Biomedical Imaging (ISBI), 2016.
* Yupeng Yan, Manu Sethi, Anand Rangarajan, Ranga Raju Vatsavai and Sanjay Ranka, Graph-Based Semi-Supervised Classification on Very High Resolution Remote Sensing Images, International Journal of Big Data Intelligence (IJBDI), (in press), 2016.
* Kushal Arora and Anand Rangarajan, A compositional approach to language modeling, arXiv preprint, arXiv:1604.00100v1 [cs.CL], 2016.
* Subit Chakrabarti, Tara Bongiovanni, Jasmeet Judge, Anand Rangarajan and Sanjay Ranka, Disaggregation of SMAP L3 brightness temperatures to 9km using kernel machines, arXiv preprint, arXiv:1601.0535v1 [cs.CV], 2016.
* Qi Deng, Guanghui Lan and Anand Rangarajan, Randomized block subgradient methods for convex nonsmooth and stochastic optimization, arXiv preprint, 1509.04609 [math.OC], September 2015.
* Yuan Zhou, Anand Rangarajan and Paul Gader, A spatial compositional model (SCM) for linear unmixing and endmember uncertainty estimation, arXiv preprint, 1509.09243 [cs.CV], October 2015.
* Rana Haber, Anand Rangarajan and Adrian M. Peter, Discriminative interpolation for classification of functional data, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), Portugal, 2015.
* Manu Sethi, Yupeng Yan, Anand Rangarajan, Ranga Raju Vatsavai and Sanjay Ranka, Scalable machine learning approaches for neighborhood classification using very high resolution remote sensing imagery, 21st ACM SIGKDD conference on Knowledge Discovery and Data Mining (KDD), 2015.
* Yuan Zhou, Anand Rangarajan and Paul Gader, A spatial compositional model for linear unmixing, IEEE Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), Japan, 2015.
* Subit Chakrabarti, Jasmeet Judge, Anand Rangarajan and Sanjay Ranka, Downscaling microwave brightness temperatures using self regularized regressive models, Best Student Paper award (2nd place) to Subit Chakrabarti, IGARSS 2015, arXiv preprint, arXiv:1501.07683 [cs.CV], 2015.
* Karthik S. Gurumoorthy and Anand Rangarajan, A Schrödinger formalism for simultaneously computing the Euclidean distance transform and its gradient density, Indian Conference on Computer Vision, Graphics and Image Processing (ICVGIP), (accepted), 2014.
* Rob Heylen, Paul Scheunders, Anand Rangarajan and Paul Gader, Nonlinear unmixing by using non-Euclidean metrics in an unmixing chain, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (JSTARS), 8(6):2655-2664, 2015.
* Ayan Biswas, David Thompson, Wenbin He, Qi Deng, Chun-Ming Chen, Han-Wei Shen, Raghu Machiraju, and Anand Rangarajan, An uncertainty-driven approach to vortex analysis using oracle consensus and spatial proximity, PacificVis 2015, Hangzhou, China, (accepted), 2015.
* Subit Chakrabarti, Jasmeet Judge, Anand Rangarajan and Sanjay Ranka, Disaggregation of remotely sensed soil moisture in heterogeneous landscapes using holistic structure-based models, arXiv preprint, arXiv:1501.07680 [cs.CV], 2015.
* Adrian Peter, Karthik S. Gurumoorthy, Mark Moyou and Anand Rangarajan, A new energy minimization framework and sparse linear system for path planning and shape from shading, Indian Conference on Computer Vision, Graphics and Image Processing (ICVGIP), (accepted), 2014.
* John Corring and Anand Rangarajan, Shape from phase: An integrated level set and probability density shape representation, International Conference on Pattern Recognition (ICPR), (accepted), 2014.
* Manu Sethi, Yupeng Yan, Anand Rangarajan, Ranga Raju Vatsavai and Sanjay Ranka, An efficient computational framework for labeling large scale spatiotemporal remote sensing datasets, IC3 2014: 635-640.
* Yan Deng, Anand Rangarajan, Stephan J. Eisenschenk and Baba Vemuri, A Riemannian framework for matching point clouds represented by the Schrödinger distance transform, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), (accepted), 2014.
* Pegah Massoudifar, Anand Rangarajan, Alina Zare and Paul Gader, An integrated graph cuts segmentation and piecewise convex unmixing approach for hyperspectral segmentation, IEEE Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), (accepted), 2014.
* Rob Heylen, Paul Scheunders, Anand Rangarajan and Paul Gader, Nonlinear unmixing by using non-Euclidean metrics in a linear unmixing chain, IEEE Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), (accepted), 2014.
* Pegah Massoudifar, Anand Rangarajan and Paul Gader, Superpixel estimation for hyperspectral imagery, Perception Beyond the Visible Spectrum (PBVS), An IEEE Computer Vision and Pattern Recognition (CVPR) Workshop, (accepted), 2014.
* L. Zhang, Q. Deng. R. Machiraju, A. Rangarajan, D. Thompson, D.K. Walters and H.-W. Shen, Boosting techniques for physics-based vortex detection, Computer Graphics Forum, 33(1):282-293, 2014.
* Qi Deng, Jeffrey Ho and Anand Rangarajan, Stochastic coordinate descent for nonsmooth convex optimization, OPT 2013, A Neural Information Processing Systems (NIPS) Workshop, 2013.
* Anand Rangarajan, Revisioning the unification of syntax, semantics and statistics in shape analysis, Pattern Recognition Letters, 43:39-46, 2014.
* Manu Sethi, Anand Rangarajan and Karthik S. Gurumoorthy, The Schrödinger distance transform (SDT) for point-sets and curves, IEEE Conf. on Computer Vision and Pattern Recognition (CVPR),  pp. 198-205, 2012. © IEEE.
* Karthik S. Gurumoorthy and Anand Rangarajan, Distance transform gradient density estimation using the stationary phase approximation, SIAM Journal on Mathematical Analysis, 44(6): 4250-4273, 2012. © SIAM.
* Ting Chen, Anand Rangarajan, Stephan J. Eisenschenk and Baba C. Vemuri, Construction of a neuroanatomical shape complex atlas from 3D MRI brain structures, NeuroImage, 60(3):1778-1787, 2012. © Elsevier.
* Ajit Rajwade, Anand Rangarajan and Arunava Banerjee, Image denoising using the higher order singular value decomposition (supplemental material), IEEE Trans. Patt. Anal. Mach. Intell., 35(4): 849-862, 2013. © IEEE.
* Ting Chen, Baba Vemuri, Anand Rangarajan, and Stephan J. Eisenschenk, Mixture of segmenters with discriminative regularization and sparse weight selection, Medical Image Computing and Computer Assisted Intervention (MICCAI), 3, pp. 595-602, Springer LNCS 6893, 2011. Young Scientist award to Ting Chen.
* Adrian Peter and Anand Rangarajan, An information geometry approach to shape density minimum description length model selection, IEEE Intl. Conf. Computer Vision (ICCV) Workshops, pp. 1432-1439, 2011. © IEEE.
* Kittipat Kampa, Jose Principe, Duangmanee Putthividhya and Anand Rangarajan, Data-driven tree-structured Bayesian network for image segmentation, IEEE Conf. Acoust. Speech and Sig. Proc. (ICASSP), pp. 2213-2216, 2012. © IEEE.
* Karthik S. Gurumoorthy, Anand Rangarajan and Arunava Banerjee, The Complex Wave Representation of Distance Transforms, Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR), pp. 413-427, Springer LNCS 6819, 2011.
* Ajit Rajwade, Anand Rangarajan and Arunava Banerjee, Using the Higher Order Singular Value Decomposition (HOSVD) for Video Denoising, Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR), pp. 344-354, Springer LNCS 6819, 2011.
* Karthik S. Gurumoorthy, Anand Rangarajan and Arunava Banerjee, A complex exponential Fourier transform approach to gradient density estimation, Statistics and Probability Letters, (rejected without review), 2011.
* Ting Chen, Anand Rangarajan, Stephan J. Eisenschenk and Baba C. Vemuri, Construction of Neuroanatomical Shape Complex Atlas from 3D Brain MRI, Medical Image Computing and Computer Assisted Intervention (MICCAI), 3, pp. 65-72, Springer LNCS 6363, 2010.
* Ajit Rajwade, Anand Rangarajan and Arunava Banerjee, Automated filter parameter selection using measures of noiseness, Seventh Canadian Conference on Computer and Robot Vision (CRV), 2010.
* Ting Chen, Anand Rangarajan and Baba Vemuri, CAVIAR: Classification via Aggregated Regression and its application in classifying the OASIS brain database, IEEE International Symposium on Biomedical Imaging (ISBI), 2010.
* Karthik Gurumoorthy, Ajit Rajwade, Arunava Banerjee and Anand Rangarajan, A Method for Compact Image Representation using Sparse Matrix and Tensor Projections onto Exemplar Orthonormal Bases, IEEE Transactions on Image Processing, 19(2): 322-334, February 2010.
* Ajit Rajwade, Arunava Banerjee and Anand Rangarajan, Image filtering driven by level curves, Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR), Springer LNCS 5681, 359-372, 2009.
* Jeffrey Ho, Adrian Peter, Anand Rangarajan and Ming-Hsuan Yang, An algebraic approach to affine registration of point sets, IEEE International Conference on Computer Vision (ICCV), 2009.
* Ting Chen, Baba Vemuri, Anand Rangarajan, and Stephan J. Eisenschenck, Group-wise point-set registration using a novel CDF-based Havrda-Charvát Divergence, International Journal of Computer Vision (IJCV), 86(1):111-124, 2010.
* Anand Rangarajan and Karthik S. Gurumoorthy, A Schrödinger wave equation approach to the eikonal equation: Application to image analysis, Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR), Springer LNCS 5681, 140-153, 2009.
* Karthik Gurumoorthy and Anand Rangarajan, A Schrödinger equation for the fast computation of approximate Euclidean distance functions, 2nd International Conference on Scale Space and Variational Methods in Computer Vision (SSVM), Springer LNCS 5567, pp. 100-111, 2009.
* Fei Wang, Tanveer Syeda-Mahmood, Baba Vemuri, David Beymer and Anand Rangarajan, Closed-form Jensen-Renyi Divergence for Mixture of Gaussians and Applications to Group-wise Shape Registration, Medical Image Computing and Computer Assisted Intervention (MICCAI), Springer LNCS 5761, 1:648-655, 2009.
* Hongyu Guo and Anand Rangarajan, Diffeomorphic point matching with applications in biomedical image registration, International Journal of Tomography and Statistics, 15(F10):42-57, 2010.
* Karthik Gurumoorthy, Ajit Rajwade, Arunava Banerjee and Anand Rangarajan, Beyond SVD: Sparse Projections Onto Exemplar Orthonormal Bases for Compact Image Representation, 19th International Conference on Pattern Recognition (ICPR), Best Scientific Paper Award, 2008.
* Adrian Peter, Anand Rangarajan, and Jeffrey Ho, Shape L'Âne Rouge: Sliding wavelets for indexing and retrieval, IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2008.
* Ajit Rajwade, Arunava Banerjee and Anand Rangarajan, Probability Density Estimation using Isocontours and Isosurfaces: Application to Information Theoretic Image Registration, IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(3):475-491, March 2009. Code available (under GPL version 2).
* Adrian Peter and Anand Rangarajan, Information Geometry for Landmark Shape Analysis: Unifying Shape Representation and Deformation, IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(2):337-350, February, 2009.
* Fei Wang, Baba Vemuri, Anand Rangarajan, and Stephan J. Eisenschenck, Simultaneous Nonrigid Registration of Multiple Point-Sets and Atlas Construction, IEEE Transactions on Pattern Analysis and Machine Intelligence, 30(11):2011-2022, November 2008. 
* Adrian Peter and Anand Rangarajan, Maximum Likelihood Wavelet Density Estimation with Applications to Image and Shape Matching, IEEE Transactions on Image Processing, vol. 17, no. 4, pp. 458–468, April 2008.
* Jeffrey Ho, Ming-Hsuan Yang, Anand Rangarajan and Baba Vemuri, A New Affine Registration Algorithm for Matching 2D Point Sets, Workshop on Applications of Computer Vision (WACV), 2007.
* Arunabha Roy, Ajay Gopinath and Anand Rangarajan, Deformable Density Matching for 3D Non-rigid Registration of Shapes, Medical Image Computing and Computer Assisted Intervention (MICCAI), Springer LNCS 4791, 1:942-949, 2007.
* Santosh Kodipaka, Baba Vemuri, Anand Rangarajan, Christiana Leonard, Ilona Schmallfuss and Stephan J. Eisenschenk, Kernel Fisher Discriminant for Shape-based Classification in Epilepsy, Medical Image Analysis, 11(1):79-90, 2007
* Adrian Peter and Anand Rangarajan, A New Closed-Form Information Metric for Shape Analysis, Medical Image Computing and Computer Assisted Intervention (MICCAI), 2006.
* Fei Wang, Baba Vemuri and Anand Rangarajan, Groupwise point pattern registration using a novel CDF-based Jensen-Shannon Divergence, IEEE Computer Vision and Pattern Recognition, 2006.
* Ajit Rajwade, Arunava Banerjee and Anand Rangarajan, A New Method of Probability Density Estimation with Application to Mutual Information Based Image Registration, IEEE Computer Vision and Pattern Recognition (CVPR), 2006.
* Adrian Peter and Anand Rangarajan, Shape matching using the Fisher-Rao Riemannian metric: Unifying shape representation and deformation, IEEE International Symposium on Biomedical Imaging (ISBI), 2006.
* Ajit Rajwade, Arunava Banerjee and Anand Rangarajan, Continuous image representations avoid the histogram binning problem in mutual information-based image registration, IEEE International Symposium on Biomedical Imaging (ISBI), 2006.
* Hongyu Guo, Anand Rangarajan and Sarang Joshi, Diffeomorphic Point Matching, Mathematical Models in Computer Vision: The Handbook, 2005.
* Fei Wang, Baba Vemuri, Anand Rangarajan, Ilona M. Schmalfuss and Stephan J. Eisenschenck, Simultaneous registration of multiple point-sets and atlas construction, European Conference on Computer Vision (ECCV), 2006.
* Hongyu Guo, Anand Rangarajan and Sarang Joshi, 3D diffeomorphic shape registration using hippocampal datasets, Medical Image Computing and Computer Assisted Intervention (MICCAI), 2005.
* Jie Zhang and Anand Rangarajan, Multimodality image registration using an extensible information metric and high-dimensional histogramming, Information Processing in Medical Imaging (IPMI), 2005.
* Hongyu Guo, Anand Rangarajan, Sarang Joshi and Laurent Younes,  A new joint clustering and diffeomorphism estimation algorithm for non-rigid shape matching, IEEE Workshop on Articulated and Non-rigid motion (ANM), 2004.
* Jie Zhang and Anand Rangarajan, Affine image registration using a new information metric, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2004.
* Hongyu Guo, Anand Rangarajan, Sarang Joshi and Laurent Younes, Non-rigid registration of shapes via diffeomorphic point matching, IEEE International Symposium on Biomedical Imaging (ISBI), (in press), 2004.
* Jie Zhang and Anand Rangarajan, A unified feature-based registration method for multimodality images, IEEE International Symposium on Biomedical Imaging (ISBI), (in press), 2004.
* Jie Zhang and Anand Rangarajan, Bayesian Multimodality Non-rigid image registration via conditional density estimation, Information Processing in Medical Imaging (IPMI), Springer LNCS 2732, 499-512, 2003.
* Anand Rangarajan, James Coughlan and Alan Yuille, A Bayesian network framework for relational shape matching, International Conference on Computer Vision (ICCV), volume I, pages 671-678, IEEE Press, 2003.
* Haili Chui, Jie Zhang and Anand Rangarajan, Unsupervised learning of an atlas from unlabeled point-sets, IEEE Trans. Patt. Anal. Mach. Intell, 26(2):160-173, 2004. Code available here.
* Haili Chui, Larry Win, Robert Schultz, Jim Duncan and Anand Rangarajan, A unified non-rigid feature registration method for brain mapping, Medical Image Analysis, 7:112-130, 2003.
* Haili Chui and Anand Rangarajan, A new point matching algorithm for non-rigid registration, Computer Vision and Image Understanding (CVIU), 89:114-141, 2003.  Code available here.
* Alan Yuille and Anand Rangarajan, The Concave-Convex Procedure (CCCP), Neural Computation, 15:915-936, 2003.
* Haili Chui and Anand Rangarajan, Learning an atlas from unlabeled point-sets, IEEE Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA), 179-186, 2001.
* Anand Rangarajan, Haili Chui and Eric Mjolsness A relationship between spline-based deformable models and weighted graphs in non-rigid matching, IEEE Computer Vision and Pattern Recognition (CVPR), volume I, 897-904, 2001.
* Anand Rangarajan Learning matrix space image representations, Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR), Springer LNCS 2134:153-168, 2001.
* Anand Rangarajan and Alan Yuille MIME: Mutual information minimization and entropy maximization for Bayesian belief propagation, Neural Information Processing Systems (NIPS), 14, pp 873-880, MIT Press, 2002.
* Alan Yuille and Anand Rangarajan The Convex-Concave Computational Procedure (CCCP), Neural Information Processing Systems (NIPS), MIT Press, Cambridge, MA, 1033-1040, 2002.
* Haili Chui and Anand Rangarajan A new algorithm for non-rigid point matching, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), volume II, 44-51, 2000.
* Haili Chui and Anand Rangarajan, A feature registration framework using mixture models,  IEEE Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA), 190-197, 2000.
* Anand Rangarajan, Haili Chui and Eric Mjolsness, A new distance measure for non-rigid image matching, Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR), Edwin Hancock and Marcello Pelillo, editors, pages pages 237-252, Springer, 1999.
* Haili Chui, James Rambo, James Duncan, Robert Schultz and Anand Rangarajan, Registration of cortical anatomical structures with robust 3D point matching, Information Processing in Medical Imaging, Attila Kuba, Martin Samal and Andrew Todd-Pokropek, editors, pages 168-181, Springer, 1999.
* Anand Rangarajan, Haili Chui and James S. Duncan, Rigid point feature registration using mutual information, Medical Image Analysis, 3(4):425-440, 1999.
* Anand Rangarajan, Haili Chui and Fred L. Bookstein, The Softassign Procrustes Matching Algorithm, Information Processing in Medical Imaging, James Duncan and Gene Gindi, editors, pages 29-42, Springer, 1997.
* Anand Rangarajan and Eric Mjolsness, A Lagrangian Relaxation Network for Graph Matching, IEEE Transactions on Neural Networks, 7(6):1365-1381, 1996.
* Steven Gold and Anand Rangarajan, A Graduated Assignment Algorithm for Graph Matching, IEEE Transactions on Pattern Analysis and Machine Intelligence, 18(4):377-388, April 1996.
* Steven Gold, Anand Rangarajan and Eric Mjolsness, Learning with Preknowledge: Clustering with point- and graph-matching distance measures, Neural Computation, 8(4):787-804, May 1996.
* Anand Rangarajan, Haili Chui, Eric Mjolsness, Suguna Pappu, Lila Davachi, Patricia S. Goldman-Rakic and James S. Duncan, A Robust Point Matching Algorithm for Autoradiograph Alignment, Medical Image Analysis, 1(4):379-398, 1997.
* Anand Rangarajan, Eric Mjolsness, Suguna Pappu, Lila Davachi, Patricia S. Goldman-Rakic and James S. Duncan, A Robust Point Matching Algorithm for Autoradiograph Alignment, Visualization in Biomedical Computing (VBC), K. H. Hohne and R. Kikinis editors, pp. 277-286, 1996.
* Suguna Pappu, Steven Gold and Anand Rangarajan, A framework for non-rigid matching and correspondence, Advances in Neural Information Processing Systems 8, pp. 795-801, 1996.
* Steven Gold, Anand Rangarajan, Chien-Ping Lu, Suguna Pappu and Eric Mjolsness, New Algorithms for 2D and 3D Point Matching: Pose Estimation and Correspondence, Pattern Recognition, 31(8):1019-1031, 1998.
* Gene Gindi, Anand Rangarajan and George Zubal, Atlas-Guided Segmentation of Brain Images via Optimizing Neural Networks, Proc. SPIE Biomedical Image Processing IV, February, 1993.

Tomographic Reconstruction:

* Sangeetha Somayajula, Christos Panagiotou, Anand Rangarajan, Quanzheng Li, Simon R. Arridge, Richard M. Leahy, PET image reconstruction using information-theoretic anatomical priors, IEEE Transactions on Medical Imaging, 30(3):537-549, 2010.
* Lili Zhou, Parmeshwar Khurd, Santosh Kulkarni, Anand Rangarajan and Gene Gindi, Aperture optimization in emission imaging using ideal observers for joint detection and localization, Phys. Med. Biol. 53 (2008) 2019-2034.
* Sangeetha Somayajula, Anand Rangarajan and Richard M. Leahy, PET image reconstruction using anatomical information through mutual information based priors: A scale space approach, IEEE International Symposium on Biomedical Imaging (ISBI), pp. 165-168, 2007.
* Parmeshwar Khurd, Lili Zhou, Anand Rangarajan and Gene Gindi, Aperture Optimization in Emission Imaging Using Optimal LROC Observers, IEEE Medical Imaging Conference (MIC), 2006.
* Ing-Tsung Hsiao, Anand Rangarajan, Parmeshwar Khurd and Gene Gindi, An overview of fast convergent ordered-subsets reconstruction methods for emission tomography based on the incremental EM algorithm, Nuclear Instruments and Methods in Physics Research A, vol 569,  pp. 429-433, Dec 2006
* Ing-Tsung Hsiao, Anand Rangarajan, Parmeshwar Khurd and Gene Gindi, Fast, Globally Convergent Reconstruction in Emission Tomography using COSEM, an Incremental EM Algorithm, IEEE Trans. Medical Imaging, (rejected), 2004.
* Anand Rangarajan, Parmeshwar Khurd, Ing-Tsung Hsiao and Gene Gindi, Convergence Proofs for the COSEM-ML and COSEM-MAP Algorithms, Technical Report, MIPL-03-01, Dec. 2003. 
* Ing-Tsung Hsiao, Anand Rangarajan, Parmeshwar Khurd and Gene Gindi, A New, Fast, Relaxation-free Convergent Ordered Subset Algorithm for Emission Tomography, IEEE International Symposium on Biomedical Imaging, 2004. 
* Parmeshwar Khurd , Ing-Tsung Hsiao, Anand Rangarajan, and Gene Gindi, A Globally Convergent Regularized Ordered Subset EM Algorithm for List Mode Reconstruction, IEEE Trans. Nuclear Science, (submitted), 2004.
* Ing-Tsung Hsiao, Anand Rangarajan, Parmeshwar Khurd and Gene Gindi, An Accelerated Convergent Ordered Subsets Algorithm for Emission Tomography, Physics in Medicine and Biology, (submitted), 2004.
* Ing-Tsung Hsiao, Anand Rangarajan and Gene Gindi, A new convergent MAP reconstruction algorithm for emission tomography using ordered subsets and separable surrogates, IEEE International Symposium on Biomedical Imaging (ISBI), 2002.
* Ing-Tsung Hsiao, Anand Rangarajan and Gene Gindi, A smoothing prior with embedded positivity constraint for tomographic reconstruction, Symposium on Fully 3D reconstruction, 2001.
* Ing-Tsung Hsiao, Anand Rangarajan and Gene Gindi, A new convex edge-preserving median prior with applications to tomography, IEEE Trans. Med. Imaging, 22(5):580-585, 2004.
* Ing-Tsung Hsiao, Anand Rangarajan and Gene Gindi, Joint MAP Bayesian tomographic reconstruction with a Gamma-mixture prior, IEEE Trans. Image Proc. 11(12):1466-1477, 2002.
* Ing-Tsung Hsiao, Anand Rangarajan and Gene Gindi, Bayesian reconstruction for transmission tomography using deterministic annealing, Journal of Electronic Imaging, 2(1):7-16, 2003.
* Anand Rangarajan, Ing-Tsung Hsiao and Gene Gindi, A Bayesian joint mixture framework for the integration of anatomical information in functional image reconstruction, Journal of Mathematical Imaging and Vision, 12:199-217, 2000.
* Anand Rangarajan, Soo-Jin Lee and Gene Gindi, Mechanical Models as Priors in Bayesian Tomographic Reconstruction, Maximum Entropy and Bayesian Methods, K. M. Hanson and R. N. Silver editors, pp. 117-124, 1996.
* Soo-Jin Lee, Anand Rangarajan and Gene Gindi, Bayesian Image Reconstruction in SPECT Using Higher Order Mechanical Models as Priors, IEEE Transactions on Medical Imaging, 14(4):669-680, December 1995.
* Soo-Jin Lee, Gene Gindi, George Zubal and Anand Rangarajan, Using Ground Truth data to design priors in Bayesian SPECT Reconstruction, Information Processing in Medical Imaging, pp. 27-39, 1995.
* Soo-Jin Lee, Anand Rangarajan and Gene Gindi, A Comparative Study of the Effects of Using Higher Order Mechanical Priors in SPECT Reconstruction, IEEE Nuclear Science Symposium and Medical Imaging Conferences, pages 1696-1700, November 1994.
* Gene Gindi and Anand Rangarajan, What can SPECT learn from Autoradiography?, IEEE Nuclear Science Symposium and Medical Imaging Conferences, pages 1715-719, November 1994.
* Gene Gindi, Anand Rangarajan, Mindy Lee, P. J. Hong and George Zubal, Bayesian Reconstruction for Emission Tomography via Deterministic Annealing, Information Processing in Medical Imaging, H. H. Barrett and A. F. Gmitro, editors, LNCS 687, pp. 322-338, Springer-Verlag, 1993 .

Neural Networks and Combinatorial Optimization:

* Anand Rangarajan, Self Annealing and Self Annihilation: Unifying deterministic annealing and relaxation labeling, Pattern Recognition  33:635-649, 2000.
* Anand Rangarajan, Steven Gold and Eric Mjolsness, A novel optimizing network architecture with applications, Neural Computation, 8(5):1041-1060, 1996.
* Anand Rangarajan, Self Annealing: Unifying deterministic annealing and relaxation labeling, Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR), M. Pelillo and E. Hancock, editors, (in press), Springer, 1997.
* Anand Rangarajan, Alan Yuille, Steven Gold and Eric Mjolsness, A convergence proof for the softassign quadratic assignment algorithm, Advances in Neural Information Processing Systems 9, M. Mozer, M. Jordan and T. Petsche, editors, pages 620-626, MIT Press, 1997.
* Anand Rangarajan, Alan Yuille, and Eric Mjolsness, Convergence properties of the softassign quadratic assignment algorithm, Neural Computation, 11:1455-1474, 1999.
* Steven Gold and Anand Rangarajan, Softmax to Softassign: Neural Network Algorithms for Combinatorial Optimization, Journal of Artificial Neural Networks, pages 381-399, Aug. 1996.
* Eric Mjolsness, Anand Rangarajan and Charles Garrett, A neural net for the reconstruction of multiple curves from a visual grammar, International Joint Conference on Neural Networks (IJCNN), vol. 1, pp. 615-620, 1991.

Continuation Methods and Markov Random Fields:

* Anand Rangarajan and Rama Chellappa, Markov random field models in image processing, The Handbook of Brain Theory and Neural Networks, M. A. Arbib, editor, pp. 564-567, The MIT Press, 1995.
* Michael J. Black and Anand Rangarajan, On the unification of line processes, outlier rejection and robust statistics with applications in early vision, International Journal of Computer Vision, 19(1):57-91, 1996.
* Anand Rangarajan, Rama Chellappa and B. S. Manjunath, Markov random fields and neural networks with applications to early vision problems, Artificial Neural Networks and Statistical Pattern Recognition: Old and New Connections, I. K. Sethi and A. K. Jain, editors, pp. 155-174, Elsevier Science Press, 1991.
* Anand Rangarajan and Rama Chellappa, Generalized graduated nonconvexity algorithm for maximum a posteriori image estimation, 10th International Conference on Pattern Recognition (ICPR), vol. 2, pp. 127-133, 1990.

Older Work:

* B. Yegnanarayana, J. Sreekanth and Anand Rangarajan, Waveform estimation using group delay processing, IEEE Trans. Acoust. Speech and Signal Proc., 33(4):832-836, Aug. 1985.

Notes:

* Anand Rangarajan, Tutorial on the EM algorithm
anand@cise.ufl.edu