This summer, the meeting is on Wednesdays, 12:30-2, in 32-D407.
Feel free to add papers to the paper stack.
To join the reading group, feel free to subscribe to v-golland email list at csail. To get access to the wiki, please contact Wanmei Ou.
Friston, K., et al.: Statistical parametric maps in functional imaging: A general linear approach. Human Brain Mapping, 1995.friston_glm.pdf
Friston, L., et al.: Assessing the significance of focal activations using their spatial extent. Human Brain Mapping, 1994. friston_focalactivations.pdf
Worsley, KJ, Friston, KJ.: Analysis of fMRI Time-Series Revisited—Again. Neuroimage, 1995. worsley_fmriagain.pdf
Myers, RH, Montgomery, DC. A tutorial on Generalized Linear Models. Journal of Quality Technology, 1997. myers_tutorialonglm.pdf
Davis, B.C., Fletcher, P.T., Bullitt, E., Joshi, S. Population Shape Regression From Random Design Data. ICCV 2007. davis-iccv2007.pdf
E. Sharon, D. Mumford 2D-Shape Analysis Using Conformal Mapping, IJCV 2006 sharonmumfordconformal.pdf
W. Mio, A. Srivastava, and S. Joshi: On Shape of Plane Elastic Curves, IJCV 2007 elasticmiojoshiijcv07.pdf
E. Klassen, A. Srivastava, W. Mio, and S. Joshi, Analysis of Planar Shapes Using Geodesic Paths in Shape Spaces, PAMI 2004 klassensrivastavamiojoshipami04.pdf
Schwartz, Wallace, and Rissanen: Intertwining Themes in Theories of Model Order Estimation lanterman.pdf
Yonggang Shi, Zhuowen Tu, Allan L. Reiss, Rebecca A. Dutton, Agatha D. Lee, Albert M. Galaburda, Ivo D. Dinov, Paul M. Thompson, Arthur W. Toga: Joint Sulci Detection Using Graphical Models and Boosted Priors. IPMI 2007. shi-ipmi07.pdf
Shape description through global spectrum: cad-shapedna-06.pdf
Sparse Bayesian Learning and the Relevance Vector Machine: Michael Tipping, JMLR 1 (2001) pp. 211-244. tipping01a.ps
Surfacelets ndfb_surf.pdf
A rationale and test for the number of factors in factor analysis (parallel analysis) (Horn J.L. Psychometrika 1965)horn_parallel.pdf
We will finish the paper from last week.
Joshua E. Cates, P. Thomas Fletcher, Martin Andreas Styner, Martha Elizabeth Shenton, Ross T. Whitaker: Shape Modeling and Analysis with Entropy-Based Particle Systems. IPMI 2007. cates-ipmi07.pdf
We will continue to discuss the LDDMM paper. The goal is to find a geometric interpretation of the first term in the sum in eq. 4, finish the theorem and talk about the metric (Sec 6).
We will discuss the computation behind LDDMM beg-lddmm.pdf. It will be an easier start than understanding the more theoretical underpinnings of the other LDDMM papers.
Note that the first lemma is the hardest part of the paper, but things get a lot easier after that.
We will discuss the Nystrom Method, which can be used to approximate the eigendecomposition of large matrices. An application of this technique to Spectral Clustering is presented in Fowlkes et al. fowlkes_spectralgrouping_nystrom.pdf
We will discuss Rao’s paper on the uniqueness of the decomposition into a Gaussian and a non-Gausian part. New paper 1966-rao-sankhyasera.pdf.
We will discuss Rao’s paper on the uniqueness of the decomposition into a Gaussian and a non-Gausian part 1969-rao-annmathstatist.pdf.
We will continue with the ICA paper.
We will discuss Beckmann et al.’s Probabilistic ICA for fMRI paper beckmann2003.pdf
We will continue with the LDA discussion.
We will discuss Blei et al.’s Latent Dirichlet Allocation paper bleingjordan2003.pdf
We will also discuss this.
We will discuss Lilla Zollei’s paper. A Marginalized MAP Approach and EM Optimization for Pair-Wise Registration: 2007-zollei-ipmi.pdf
We will continue on the Wisharts paper; the homework is to get more comfortable with that particular distribution.
Bing Jian, Baba C. Vemuri: Multi-fiber Reconstruction from Diffusion MRI Using Mixture of Wisharts and Sparse Deconvolution. IPMI 2007. jian-vemuri-ipmi07.pdf
Anastasia will lead the discussion.
Schapire. Boosting overview schapire_msri.pdf
Additional papers:
Jerome Friedman, Trevor Hastie, Robert Tibshirani. Additive Logistic Regression: a Statistical View of Boosting (1998). friedman98additive.pdf
Torralba, Murphy and Freeman. Sharing visual features for multiclass and multiview object detection. sharing.pdf
Boosting Image Retrieval (Tieu, Viola, IJCV 2004) tieu_boostingimageretrieval.pdf
We will continue the discussion about the paper from last meeting.
Chris McIntosh and Ghassan Hamarneh. Is a Single Energy Functional Sufficient? Adaptive Energy Functionals and Automatic Initialization. MICCAI 2007. mcintosh-miccai2007.pdf
Shaohua Kevin Zhou and Dorin Comaniciu. Shape Regression Machine. IPMI 2007. zhou-ipmi2007.pdf
We will discuss the two simple examples defined in last meeting: a closed curve and an open curve. Please work through the examples and come with beatiful matlab figures of the embedding.
We will also talk about wavelets. The first paper is what we already looked at; the other two are longer, more detailed versions.
R. R. Coifman, S. Lafon, A. B. Lee, M. Maggioni, B. Nadler, F. Warner, and S. W. Zucker. Geometric diffusions as a tool for harmonic analysis and structure definition of data: Multiscale methods. PNAS, 2005. geometric_diffusion2.pdf
Additional papers on the topic: Diffusion wavelets and their use in spectral clustering: nadler06.pdf coifmanmaggoni06.pdf
Laplacian-Eigenmaps by Belkin and Niyogilaplacianeigenmaps.pdf
Further papers on discrete Laplace-Beltrami Operators (overview): xu-04-discretelaplace.pdf wardetzky-07-discretelbo.pdf
We will finish the diffusion map discussion and will talk about the second paper. The week after that, we will come back to the operators in the first paper.
We will start by discussing the first paper and go on to the second one. You only need to read the first paper for this meeting, but we will end up reading both by the end of this series.
R. R. Coifman, S. Lafon, A. B. Lee, M. Maggioni, B. Nadler, F. Warner, and S. W. Zucker. Geometric diffusions as a tool for harmonic analysis and structure definition of data: Diffusion maps. PNAS, 2005. geometric-diffusion1.pdf
R. R. Coifman, S. Lafon, A. B. Lee, M. Maggioni, B. Nadler, F. Warner, and S. W. Zucker. Geometric diffusions as a tool for harmonic analysis and structure definition of data: Multiscale methods. PNAS, 2005. geometric_diffusion2.pdf
Additional papers on the topic: Diffusion wavelets and their use in spectral clustering: nadler06.pdf coifmanmaggoni06.pdf
We will finish the fast diff. paper. Mert and Thomas will lead.
Fast diffeomorphic registration. Thomas will lead the discussion.
We will finish Mahony’s paper.
Lie groups tutorial.
Continuing on the basic differential geometric notions required for understanding Mahony’s paper, we will review 5th chapter of:
Wolfgang Kuhnel and Bruce Hunt, Differential Geometry: Curves - Surfaces - Manifolds, AMS, Second Edition, 2005.
Please check the discussion for mote details.
Danial will present: Mahony, R. Manton, J. H., ``The Geometry of the Newton Method on Non-Compact Lie Groups,’’ JOURNAL OF GLOBAL OPTIMIZATION, 2002, VOL 23; NUMBER 3, pages 309–327. mahony.pdf
J.-P. Thirion, Image matching as a diffusion process: an analogy with Maxwell’s demons. Medical Image Analysis (1998), volume 2, number 3. Biz will present. thirion98.pdf
Tom Vercauteren, Xavier Pennec, Aymeric Perchant, Nicholas Ayache. Non-parametric Diffeomorphic Image Registration with the Demons Algorithm. MICCAI 2007.
Min Cut Max Flow and the Ford-Fulkerson method.
Introduction to Algorithms, Second Edition Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest and Clifford Stein.
Graph cuts for energy minimization. Koen will lead the discussion.
Yuri Boykov, Olga Veksler, and Ramin Zabih. Fast Approximate Energy Minimization via Graph Cuts. IEEE PAMI 23(11), 2001.
Finish the BP paper.
Belief propagation
Our main reference is: Constructing free-energy approximations and generalized belief propagation algorithms: J. Yedidia, et al. yedidiafreemanweiss2005.pdf
The basic mathematical motivation of belief propagation: A. M. Aji and J. McEliece gendislaw.pdf
BP is used for learning problems on different types of graphs: Bayesian Networks, Markov Random Fields, Junction graphs, and Factor graphs. To see examples: Y. Weiss and W. T. Freeman max-product_optimality.pdf and F. R. Kschischang, et al. sum-product.pdf
And a couple of other popular review papers by the same authors as of our main paper: genbp.pdf and understandingbp.pdf.
William T. Freeman, Thouis R. Jones, and Egon C. Pasztor, Example-based super-resolution, IEEE Computer Graphics and Applications, March/April, 2002. cgasres.pdf
W. T. Freeman, E. C. Pasztor, O. T. Carmichael Learning Low-Level Vision International Journal of Computer Vision, 40(1), pp. 25-47, 2000. MERL-TR2000-05. tr2000-05.pdf
We will continue our discussion on projection pursuit and its connection to Infomax ICA.
Original Infomax paper:
InfoMax ICA algorithm and its connection to projection pursuit. An information-maximization approach to blind separation and blind deconvolution. Bell and Sejnowski, Neural Computation, 1995. infomax.pdf
ICA Tutorial (with sections devoted to Infomax ICA):
Independent Component Analysis: Algorithms and Applications, Aapo Hyvärinen and Erkki Oja, 2000. icatutorial.pdf
Projection Pursuit:
Please read the first 17 pages of the following paper.
M. C. Jones; Robin Sibson, What is Projection Pursuit?, Journal of the Royal Statistical Society. Series A (General), Vol. 150, No. 1. (1987), pp. 1-37. jones87.pdf
A much longer, more detailed review paper is the below.
Peter Huber,Projection Pursuit,The annals of Statistics, Vol 13, No2 (June 1985), pp. 435-475. huber-1.pdf
MM algorithms: Hunter and Lange, A Tutorial on MM Algorithms, Am. Stat. 58(1):30-7, Feb. 2004. Clear Version mm_tutorial.pdf
Hunter and Lange, A Tutorial on MM Algorithms, Am. Stat. 58(1):30-7, Feb. 2004. lange_04_amstat.pdf
Jacobson and Fessler, An Expanded Theoretical Treatment of Iteration-Dependent Majorize-Minimize Algorithms, preprint. jacobson,tip.pdf
“Information Theoretic Coclustering,” by Inderjit S. Dhillon, Subramanyam Mallela, and Dharmendra S. Modha p89-dhillon.pdf
“A Log-Euclidean Polyaffine Framework for Locally Rigid or Affine Registration” by Vincent Arsigny, Olivier Commowick, Xavier Pennec, and Nicholas Ayache logeuclidean_wbir.pdf
Finish the CG.
Conjugate gradient algorithm.
Jonathan Richard Shewchuk. An Introduction to the Conjugate Gradient Method Without the Agonizing Pain. This is a nice (although long) tutorial paper: painless-conjugate-gradient.pdf
A (shorter) section from the numerical recipes book: c10-6.pdf
We will finish the paper.
We will continue with the same paper. Please read Section 4 and the appendices. It’s heavy reading; you might want to start early.
Danial: The following paper makes a nice connection between exponential-family-mixture-model and distance-measure-based clustering methods.
A. Banerjee et al. Clustering with Bregman Divergence. J Mach Learn Res 6 (2005).
Please read the first three sections for the first meeting. We will go through the key concepts: Bregman divergence, and information in more detail and try to understand the Bregman hard clustering algorithm.
Mert will lead the discussion on registration of fMRI.
B. Thirion et al. Improving Sensitivity and Reliability of fMRI Group Studies through High Level Combination of Individual Subjects Results. MMBIA 2006.
Serdar will lead the discussion on the multi-modal (not in a classical sense) nature of atlases:
Daniel J. Blezek and James V. Miller. Atlas Stratification. MICCAI 2006.
More Sobolev Active Contours this week...
Sobolev Active Contours: sobolevactivecontours.pdf
–Thomas
We will continue our asymptotic quest. Let’s hope this process converges to the true value of the parameters!
Here is my last version of summary: Reading: summarydoob.pdf
We will continue the discussion of Wilks’ paper on the asymptotics of LR. In order to make sense of the main assumption of equation (3), we have to go through the reference to Doob’s paper which is a seminal derivation of many basic theorems.
Reading: doob1948.pdf
Danial
Reading: leemput_miccai2006.pdf
–Thomas
Papoulis (pp. 275–278) is a good introductory note on asymptotics of hypothesis testing (photocopies outside Polina’s office). See also his statistics chapter (pp. 241–282) for hypothesis testing in general. The original papers for the asymptotics of the likelihood ratio are:
We will continue the paper from last week.
We also made a note that we need to look into asympotic statistics results (mentioned in the paper) in the future. Kinh might take a lead on that.
Here’s the original DCM paper, for people who want more detail:
Note the special time: 10:00am
Penny W.D. et al. (2004). Modelling functional integration: a comparison of structural equation and dynamic causal models.
We will focus on the structural equation model this week.
Note the special time: 4pm
We will continue our discussion on effective connectivity in neuroimaging. We will focus on the second half of the first paper from last time (Friston 1994). If time is allowed, we will discuss the third paper from last time as well (Friston 1997).
We will discuss functional and effective connectivity in neuroimaging. The first paper is the most general paper. The second paper is a book chapter version of the first paper on functional connectivity and goes a bit deeper. The third paper deals with more exotic topic in effective connectivity. We will mainly discuss the first paper, so if you have limited time, the first one is the paper to read.
Friston, K.J. (1994). Functional and effective connectivity in neuroimaging: A synthesis. Human Brain Mapping, 2, 56-78.
Friston, K.J, Büchel, C (1993). Functional Connectivity: Eigenimages and multivariate analyses.
Friston, K.J., Buchel, C., Fink, G.R., Morris, J., Rolls, E., and Dolan, R. (1997). Psychophysiological and modulatory interactions in Neuroimaging. NeuroImage, 6, 218-229.
We will start the meetings with two papers that Wanmei claims use the same model in two somewhat unrelated applications. Both papers consider the fundamental problem of evidence integration from independent sources.
We will discuss the details of the models and the relationship between them. If you have time to read just one paper, the first one is lighter.
Genovese, C. R., Noll, D. C. and Eddy, W. F. (1997). Estimating Test-Retest Reliability in fMRI I: Statistical Methodology, Magnetic Resonance in Medicine, 38, 497–507.
Simultaneous Truth and Performance Level Estimation (STAPLE): An Algorithm for the Validation of Image Segmentation. Warfield S, Zou KH, Wells WM. IEEE Trans Med Imag 2004; 23:903-921.
We will discuss ICA for fMRI time series analysis. The longer paper discusses the method in details, and will be the basis for our discussion. The shorter one is a nice overview.
We will discuss the Dirichlet process mixture model, as presented in Teh 04, , which develops a variant of the DP mixture for grouped data. Other papers (all cited by Teh) are provided here for those interested in a deeper theoretical background: Ferguson 73 and Antoniak 74 are the seminal papers (with fairly technical measure-theoretic proofs), while Sethuraman 94, followed by Ishwaran and James 01 and Ishwaran and Zarepour 02, are more constructive. Of these, I’d recommend Sethuraman for clarity and brevity. – John
Yee Whye Teh et al.
Hierarchical Dirichlet Processes
teh04hdp.pdf
Thomas Ferguson
A Bayesian Analysis of Some Nonparametric Problems
ferguson.ps.gz
Charles Antoniak
Mixtures of Dirichlet Processes with Applications to Bayesian Nonparametric Problems
antoniak74mixtures.ps
Jayaram Sethuraman
A Constructive Definition of Dirichlet Priors
sethuraman94constructive.pdf
Hemant Ishwaran and Lancelot James
Gibbs Sampling Methods for Stick-Breaking Priors
ishwaran01gibbs.pdf
Hemant Ishwaran and Mahmoud Zarepour
Exact and approximate sum-representations for the Dirichlet process
ishwaran02exact.pdf
We will continue the discussion from the last time. I posted some questions in the Discussion section. Feel free to add comments and more questions. – Polina
We will discuss the Information Bottleneck Method and its uses in determining the number of clusters in fMRI data. The second paper describes the information bottleneck approach and is a background reading for the first paper.
Bertrand Thirion, Olivier Faugeras. Feature Detection in fMRI Data: The Information Bottleneck Approach. thirionmedia.pdf
Naftali Tishby, Fernando C. Pereira, William Bialek. The Information Bottleneck Method
tishby99information.pdf
Uncut Version:
allertoninfobottleneck.pdf
I (Thomas) am posting the writeups for Apr 12. Gheorghe has also kindly provided his short writeup on EM. Happy Reading!!
I (Thomas) will be presenting the papers for this week.
Andrew Ng Michael Jordan and Yair Weiss
On Spectral Clustering: An analysis and an algorithm
ng-spectralclustering.pdf
Optional Papers: I will talk about mixture fitting, but I will not be completely following Michael Collins’ derivations. However, the tutorial is still nice if you have no experience with mixture fitting or EM. We will NOT go over the theorems about convergence, i.e. we will not go beyond section 3.3. The idea is to give everyone an intuitive feel about the concept of mixture fitting, not limiting oneself to using only gaussians or the EM technique.
Michael Collins
michaelcollinstutorialonem.ps
More Variants of Spectral Clustering:
Jianbo Shi and Jitendra Malik
shimalik-normalizedcuts.pdf
Mikhail Belkin and Partha Niyogi
belkin-laplacianeigenmaps.pdf
Marina Meila and Jianbo Shi
randomwalkspectralclustering.ps
We will take a two week break and resume our discussion of FDR at the next meeting.
We’ll start with Efron, B. and Tibshirani, R.
Empirical Bayes Methods and False Discovery Rates for Microarrays
Genetic Epidemiology 23:70-86, 2002.
efrontibshirani_fdr.pdf
And then move onto the Genovese et al. paper on thresholding maps in neuroimaging using FDR nicholsfdr.pdf (listed below).
A comparison of several similar methods (FDR, pFDR, PER, PFP) can be found in
K. Manly, D. Nettleton, and J.T.G. Hwang
Genomics, Prior Probability, and Statistical Tests of Multiple Hypotheses
Genome Research 14: 997-1001, 2004 manlyetal.pdf
I might touch on some of these different options during the discussion.
The PFP paper was
Controlling the Proportion of False Positives in Multiple Dependent Tests
R. L. Fernando, D. Nettletonb, B. R. Southey, J. C. M. Dekkers, M. F. Rothschild, and M. Soller
Genetics, Vol. 166, 611-619, January 2004
Benjamini, Y. and Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. Roy. Statist. Soc. Ser. B 57 289-300, 1995. benjaminifdr.pdf
Christopher R. Genovese, Nicole A. Lazar, Thomas Nichols. Thresholding of Statistical Maps in Functional Neuroimaging Using the False Discovery Rate. NeuroImage 15:870-878, 2002. nicholsfdr.pdf
Yoav Benjamini and Daniel Yekutieli. THE CONTROL OF THE FALSE DISCOVERY RATE IN MULTIPLE TESTING UNDER DEPENDENCY. The Annals of Statistics 2001, Vol. 29, No. 4, 1165-1188.
I think it might be worth switching to one of the pFDR papers for the extensions of the FDR. I’m reading through some of them, and will post suggestions if one of them strikes me. In particular, I think they have a nicer treatment of independence, and it will be nice to see some other approaches to FDR. -Ray
We will continue with the permutation testing.
We also finshed the proof that the sample mean and the sample variance are independent for Gaussian iid case.
See Discussion for additional notes.
Thomas E. Nichols and Andrew P. Holmes.
Nonparametric Permutation Tests For Functional Neuroimaging: A Primer with Examples.
Human Brain Mapping 15:1-25(2001).
nichols-perm.pdf
Wanmei Ou will define the general setup of the fMRI detection problem, preparing us for the series of papers in fMRI analysis.
Notes for the group meeting.
Michael Siracusa will lead a discussion on a classical hypothesis testing, in preparation for some papers in fMRI and DTI statistics.
Notes for the group meeting. hyptest-faq.pdf
Duygu Tosun and Jerry L. Prince.
Cortical Surface Alignment Using Geometry Driven Multispectral Optical Flow,
Information Processing in Medical Imaging (IPMI), Colorado, USA, July 11-15, 2005.
tosun2005.pdf.gz
Additional papers: Duygu Tosun, Maryam E. Rettmann, Jerry L. Prince.
Mapping techniques for aligning sulci across multiple brains.
Medical Image Analysis 8 (2004) 295-309.
tosun2004a.pdf.gz
Xiao Han, Dzung L. Pham, Duygu Tosun, Maryam E. Rettmann, Chenyang Xu, and Jerry L. Prince.
CRUISE: Cortical reconstruction using implicit surface evolution.
NeuroImage 23 (2004) 997-1012.
han-2004.pdf.gz
Xiao Han, Chenyang Xu, and Jerry L. Prince.
A Topology Preserving Level Set Method for Geometric Deformable Models.
PAMI, VOL. 25, NO. 6, JUNE 2003.
han-2003.pdf.gz
Also on the list for future reading: Thompson, P. Toga, A.W.
A surface-based technique for warping three-dimensional images of the brain.
IEEE Transactions on Medical Imaging, Volume 15, Issue 4, 402 - 417, 1996.
thompson96.pdf.gz
Additional papers:
Xianfeng Gu; Yalin Wang; Chan, T.F.; Thompson, P.M.; Shing-Tung Yau;
Genus zero surface conformal mapping and its application to brain surface mapping.
IEEE Transactions on Medical Imaging, Volume 23, Issue 8, 949 - 958, 2004.
thompson2004.pdf.gz
Bruce Fischl, Martin I. Sereno and Anders M. Dale.
Cortical Surface-Based Analysis: II: Inflation, Flattening, and a Surface-Based Coordinate System.
Neuroimage. 9(2):195-207. 1999.
fischl99.pdf.gz
Bruce Fischl, Martin I. Sereno, Roger B.H. Tootell, Anders M. Dale.
High-resolution inter-subject averaging and a coordinate system for the cortical surface.
Human Brain Mapping, Volume 8, Issue 4, Pages 272 - 284, 1999. fischl99a.pdf.gz
Additional papers:
Anders M. Dale, Bruce Fischl and Martin I. Sereno.
Cortical Surface-Based Analysis: I. Segmentation and Surface Reconstruction.
Neuroimage, 9(2):179-194, 1999. dale99.pdf.gz
We will continue the discussion on the permutation tests. The future meetings will be shifted by one week.
Timothy B. Terriberry, Sarang C. Joshi, and Guido Gerig.
Hypothesis Testing with Nonlinear Shape Models.
IPMI 2005, LNCS 3565, pp. 15-26, 2005.
multperm2005.pdf.gz
Additional papers:
Blair, R.C., Higgins, J.J., Karniski, W., Kromrey, J.D.
A study of multivariate permutation tests which may replace Hotelling T2 test in prescribed circumstances.
Multivariate Behavioral Research 29 (1994) 141-164.
multperm94.pdf.gz
Book:
Pesarin, Fortunato
Multivariate permutation tests : with applications in biostatistics.
I have the book.
GE Christensen, RD Rabbit, MI Miller.
Deformable Templates Using Large Deformation Kinematics.
IEEE Transactions on Image Processing, 5(10), 1996, pp. 1435-1447.
deformabletemplatesusinglargedeformationkinematics.pdf.gz
Additional papers:
Ain A. Sonin.
Fundamental Laws of Motion for Particles, Material Volumes, and Control Volumes, 2001.
fundamental_laws.pdf.gz
On Choosing and Using Control Volumes: Six Ways of Applying the Integral Mass Conservation Theorem to a Simple Problem.
choosingcontrolvolumes.pdf.gz
We will also finish the discussion on using prior examples to bias registration.
D. M. Blei, A. Y. Ng, and M. I. Jordan.
Latent Dirichlet allocation.
Journal of Machine Learning Research, 3, 993-1022, 2003.
blei03a.pdf.gz
Additional papers: Thomas Hofmann.
Probabilistic Latent Semantic Analysis.
UAI 1999.
http://www.cs.brown.edu/people/th/papers/Hofmann-UAI99.pdf.gz
Josef Sivic, Bryan C. Russell, Alexei A. Efros, Andrew Zisserman, William T. Freeman.
Discovering objects and their location in images.
ICCV 2005.
http://people.csail.mit.edu/brussell/research/SREZF05.pdf.gz
Brian Russell’s RQE paper. http://www.csail.mit.edu/~brussell/research/rqe.pdf.gz
Mert R. Sabuncu and Peter J. Ramadge.
Gradient based optimization of an EMST registration function.
IEEE Conference on Acoustics, Speech and Signal Processing, Philadelphia, March 2005.
sabuncu_icassp05-1.pdf.gz
Mert R. Sabuncu and Peter J. Ramadge.
Graph theoretic image registration using prior examples.
European Signal Processing Conference 2005, Antalya, Turkey, September 2005.
eusipco05_submit-1.pdf.gz
Additional papers:
B. Ma, A.O. Hero, J.D. Gorman and O. Michel.
Image Registration with Minimum Spanning Tree Algorithm.
IEEE International Conf. on Image Processing, vol.1, pp.481-484, Vancouver, BC, Canada, Sept. 2000.
ma_icip00.pdf.gz
A. O. Hero, B. Ma, O. Michel and J. Gorman.
Applications of entropic spanning graphs.
IEEE Signal Proc. Magazine (Special Issue on Mathematics in Imaging), Vol 19, No. 5, pp 85-95, Sept. 2002.
spmag_ma_01-1.pdf.gz
Additional papers:
Beirlant, J., Dudewicz, E. J., Gyorfi, L., and van der Meulen, E. C.
Nonparametric entropy estimation: An overview.
International Journal of the Mathematical Statistics Sciences, 6, 17-39, 2001.
beirlant.pdf.gz
No meeting, many of us are at ICCV.
P.D. Grünwald.
A Tutorial Introduction to the Minimum Description Principle.
grunwald-mdlintro.pdf.gz
Also use material from Septermber 27 meeting.
A minimum description length approach to statistical shape modeling.
Davies, R.H.; Twining, C.J.; Cootes, T.F.; Waterton, J.C.; Taylor, C.J.
IEEE Transactions on Medical Imaging, 21(5):525 - 537, 2002.
shape-mdl.pdf.gz
Erik Learned-Miller.
Data Driven Image Models through Continuous Joint Alignment.
to appear in IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 2005.
pami_congeal.pdf.gz
Additional papers:
Lilla Zollei, Erik Learned-Miller, Eric Grimson and William Wells.
Efficient population registration of 3D data.
Workshop on Computer Vision for Biomedical Image Applications: Current Techniques and Future Trends, at the International Confernece of Computer Vision (ICCV), 2005.
congeal_3d.pdf.gz
Connection between the code length and entropy - Section 5.4 (and around) in Cover and Tomas.
J. Rissanen.
An Introduction to the MDL Principle.
rissanen-intro.pdf.gz
Additional papers on MDL:
Rissanen, J.
Stochastic Complexity and Modeling.
Annals of Statistics, Vol 14, 1080-1100, 1986.
rissanen86.pdf.gz
P.D. Grünwald.
A Tutorial Introduction to the Minimum Description Principle.
grunwald-mdlintro.pdf.gz
Additional papers on BIC and AIC:
Schwarz, G. (1978).
Estimating the dimension of a model. Annals of Statistics, 6, 461-464.
scwarzbic.pdf.gz
Akaike, H. (1974).
A new look at the statistical model identification.
IEEE Transactions on Automatic Control, AC-19, 716-723. akaike74.pdf.gz
A Unified Information-Theoretic Approach to Groupwise Non-rigid Registration and Model Building.
Carole J. Twining, Tim Cootes, Stephen Marsland, Vladimir Petrovic, Roy Schestowitz and Chris J. Taylor.
Information Processing in Medical Imaging: 19th International Conference, IPMI 2005, Glenwood Springs, CO, USA, July 10-15, 2005.
twining-ipmi-2005.pdf.gz
Additional papers:
Carole J. Twining, Stephen Marsland, and Chris Taylor.
Groupwise Non-Rigid Registration: The Minimum Description Length Approach.
BMVC 2004.
twining-bmvc-2004.pdf.gz
Carole Twining and Stephen Marsland.
A Unified Information-Theoretic Approach to the Correspondence Problem in Image Registration.
International Conference on Pattern Recognition, Cambridge, U.K. 2004.
twining-icpr-2004.pdf.gz
First meeting, general intros.