Energy
Minimization Methods
in Computer Vision and Pattern Recognition (EMMCVPR
2005),
Nov. 2005, St. Augustine, FL.
Energy
Minimization Methods
in Computer Vision and Pattern Recognition (EMMCVPR 2003),
July 2003, Lisbon, Portugal.
Mathematical
Methods in
Biomedical Image Analysis (MMBIA
2001).
Neural
Information Processing Systems (NIPS) Workshop on "Statistical
and Structural models for Network Vision", 1995.
Neural
Information Processing Systems (NIPS) Workshop on "Deterministic
Annealing and Combinatorial Optimization", 1992.
GatorBait_100:
Fish Shape Database available under the terms of the GNU General Public
License (version 2). 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 software (with demo) available here.
We acknowledge support
from the National Science Foundation (NSF IIS 0196457). Ajit
Rajwade, Arunava Banerjee and Anand
Rangarajan, Image
filtering driven by level curves,
Energy Minimization Methods in Computer Vision and Pattern Recognition
(EMMCVPR), 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),
(accepted), 2009.
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), 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),
2009.
Karthik
Gurumoorthy and Anand Rangarajan, A fast
eikonal equation
solver using the Schrödinger wave equation,
Technical Report, Center for Computer Vision, Graphics and Medical
Imaging, CVGMI-09-05, University of Florida, March 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.
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, (revised and resubmitted), 2009.
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), 2007.
Santosh
Kodipaka, Baba Vemuri, Anand Rangarajan, Christiana Leonard,
Ilona
Schmallfuss and Stephan 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:
Lili
Zhou, 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