Instructor: Jason Corso (UBIT: jcorso)
Course Webpage:
http://www.cse.buffalo.edu/~jcorso/t/CSE672.
Syllabus:
http://www.cse.buffalo.edu/~jcorso/t/CSE672/files/syllabus.pdf.
Downloadable course material can be found on the CSE UNIX network: /home/csefaculty/jcorso/672.
Meeting Times:
TR 12:301:50
Location:
Fronczak Hall 422 (http://goo.gl/maps/0Y4mz)
Email Listserv:
cse672fa12list@listserv.buffalo.edu
Use this list for any and all course discussion, except private matters.
 9/4  Project idea examples in
/home/csefaculty/jcorso/672/examples.pdf on the CSE network.
 8/28  First day of class.
The calendar is given in weeks and will be populated as the semester proceeds based on the
course outline and our progress. There are no slides for this course (lectures are given on the board) and you should crossreference reading materials with the outline below and the bibliography I handed out with the syllabus.

August 28
 Introduction. Statistics of Natural Images.
 Sept. 4

Statistics of Natural Images wrapup.
 Descriptive models on
regular lattices.
 Sept. 11
 Project Proposal Due in Class on 9/11
 Descriptive models on regular lattices.
 Sept. 18
 Project Proposal Due in Class on 9/20
 Sept. 25
 Oct. 2
 Project Milestone 1 Report Due in Class on 10/4
 Oct. 9
 Oct. 16
 Project Milestone 2 Report Due in Class on 10/18
 Oct. 23
 Oct. 30
 Project Milestone 3 Paper Due in Class on 11/1
 Nov. 6
 Peer Reviews Due 11/6
 Nov. 13
 Revised project paper due 11/15
 Nov. 20
 Nov. 27
 Dec. 4
Course Overview:
The course takes an indepth look at various Bayesian methods in computer and
medical vision. Through the language of Bayesian inference, the course will
present a coherent view of the approaches to various key problems such as
detecting objects in images, segmenting object boundaries, and recognizing
activities in video. The course is roughly partitioned into two parts:
modeling and inference. In the first half, it will cover both classical models
such as weak membrane models and Markov random fields as well as more recent
models such as conditional random fields, and topic
models. In the second half, it will focus on inference algorithms. Methods
include PDE boundary evolution algorithms such as region competition, discrete
optimization methods such as graphcuts and graphshifts, and stochastic
optimization methods such as datadriven Markov chain Monte Carlo. An emphasis
will be placed on both the theoretical aspects of this field as well as the
practical application of the models and inference algorithms.
Course Project:
Each student will be required to implement a course project that is either a
direct implementation of a method discussed during the semester or new research
in Bayesian vision. A paper describing the project is required near the end of
the semester (68 pages two column IEEE format). The papers will be
peerreviewed in the course; revisions need to be made based on the peer review
and the final submission needs to include a letter to the editor
describing the paper as if it is in submission to a journal and a description of
the revisions made and why. Working project demos are required at the end of
the semester. This is a ``projects'' course. Your projects can satisfy
a Masters requirement. In most cases, it will involve at least some
new/independent research. Previous offerings of this course have resulted in
numerous papers accepted at major conferences and journals.
Prerequisites:
It is assumed that the students have taken introductory courses in pattern
recognition (CSE 555), and computer vision (CSE 573). Machine learning (CSE
574) is suggested but not required. A strong understanding and ability to
work with probabilities, statistics, calculus and optimization is expected.
Permission of the instructor is required if these prerequisites have not been
met.
Course Goals:
After taking the course, the student should will a clear understanding of the
stateoftheart models and inference algorithms for solving vision problems
within a Bayesian methodology. Through completing the course project, the
student will also have a deep understanding of the lowlevel details of a
particular model/algorithm and application. The student will have completed
some independent research in Bayesian Vision by the end of the course.
The student will also have experience in planning a project, conducting
semiindependent research, and writing up the results; peerreview practice will
also be part of the course.
Textbooks:
There is unfortunately no complete textbook for this course. The
required material will either be distributed by the instructor or
found on reserve at the UB Library. Recommended textbooks are below; it is
suggested you pick up a copy of at least one of the first three (and if all
students do this there will be a half dozen copies of each floating around to
share).
 Li, S. Markov Random Field Modeling in Image Analysis.
SpringerVerlag. 3rd Edition. 2009.
 Winkler, G. Image Analysis, Random Fields and Markov Chain Monte
Carlo Methods: A Mathematical Introduction. Springer. 2006.
 Blake, A., Kohli, P. and Rother, C. Markov Random Fields for Vision and Image Processing. MIT Press.
2011.
 Chalmond, B. Modeling and Inverse Problems in Image Analysis.
Springer. 2003.
 Koller, D. and Friedman, N. Probabilistic Graphical Models:
Principles and Techniques. MIT Press. 2009.
 Bishop, C. M. Pattern Recognition and Machine Learning.
Springer. 2007.
Grading:
Letter grading distributed as follows:
 InClass Discussion/Quizzing (50%)
 Homeworks (0%)
 Project (50%)
InClass Discussion/Quizzing:
Half of the grades in this course are based on the students (1) participation in
the class, (2) ability to answer questions when queried and (3) ask questions.
No written quizzes are planned, but the professor reserves the possibility.
Homeworks:
There will be weekly homeworks recommended. They will cover both
theoretical and practical (implementation) aspects of the material.
The homework assignments are not turned in. We will organize a weekly time
where the students in the course will come together to discuss the weekly work
without the professor around.
Programming Language:
Student choice for the project (generally, Python, Matlab, Java, or
C/C++). Any courserelevant aspects of the project need to be independently
developed; e.g., if you are using belief propagation as your project's inference
algorithm, then you need to implement belief propagation from scratch. No
exceptions; don't ask.
For the homeworks and some inclass exercises we will use the UGM library
written by Dr. Mark Schmidt;
http://www.di.ens.fr/~mschmidt/Software/UGM.html. At various points in
the course, you will be asked to either run through a demo/function from the
library or implement/reimplement a different method for pedagogical value.
There will be no introduction to the library in the course, you are expected to
learn it in the first week or two (work through the early and simple demos
``Small,'' ``Chain,'' ``Tree'', and ``ICM.''
Word Processing:
This course forces you to learn L^{A}TEX if you do not already know it. It is the
language of the realm. All things submitted to me must be formatted in L^{A}TEX.
The goal of the project is to have each student solve a real problem using the
ideas learned herein. The professor will distribute/discuss project ideas in
the first week of the class; students are encouraged to design their own project
in conjunction with the professor. The ultimate goal is for each student to do
some new work and learn by doing so. Within reason, camera and video equipment
will be made available to the students from the VPML (my lab). Suitable
arrangements should be made with the instructor to facilitate equipment use.
Project topics can cover a myriad of current problems in vision and must include
some technical aspect developed on top of ideas in the course. A project
focusing on statistics of a class of images/videos is also fair game but will
need to be thoroughly justified.
 9/11
 Project proposal due in class. 1page description of the
proposed project and the type of problem/data. It should include
three milestones in planning. (All writing must be done in L^{A}TEX.)
 9/20
 Project plan due in class (this is the refinement of the project
proposal; i.e., project proposal v 2). 3page description of the proposed
project, the most related work from the literature, the three milestones,
planned data and experiments, and a goal statement that presents a
table with two columns:
Outcome 
Grade 
My project will blah blah blah 
A 
My project will blah blah blah 
B 
My project will blah blah blah 
C 
My project will not work. 
F 
You fill in the blah blah blah and I'll consider it (and approve it or make
you modify it). Hence, your Project plan is a contract and you have just
graded yourself.
 10/4
 Milestone 1 Report due in class. (1paragraph)
 10/18
 Milestone 2 Paper due in class. (4ishpages)
 11/1
 Milestone 3 Paper due in class. (full paper)
 11/16
 Blind Peer Review Period. (Round robin with everyone reviewing
two papers.)
 11/15
 Revised paper due. (Note 11/15 is the CVPR deadline.)
 after 11/15
 Project presentations and demos in class.
The paper should be in standard IEEE conference format at a maximum of 8 pages.
We'll explain in class how to set it up.
It should be approached as a standard paper containing
introduction and related work, methodology, results, and discussion.
The course is roughly divided into two parts. In the first part, we discuss
various modeling and associated learning algorithms. In the second part, we
discuss the computing and inference algorithms which use the previously
discussed models to solve complex inference problems in vision. The topic
outline follows; citations are given and an underlined citation indicates a
primary (mustread) one. All or most papers are available in PDF at the course
directory (location above).
Paper citations are given below (somewhat sparsely), but few references are
given to chapters in the books mentioned above. It is suggested you look in the
books for more information when needed.
 Introduction.
 Discussion of Bayesian inference in the context of vision problems.
(Winkler, 2006, Chapter 1)
(Chalmond, 2003, Chapter 1)
(Hanson, 1993)
Probabilistic Inference Primer: (Griffiths and Yuille, 2006)
 Presentation of relevant empirical findings concerning the statistics
of images motivating the Bayesian approach.
(Field, 1994)
(Field, 1987)
(Julesz, 1981)
(Kersten, 1987)
(Ruderman, 1994)
(Simoncelli and Olshausen, 2001)
(Torralba and Oliva, 2003)
(Wu et al., 2007)
 Model classes: discriminative, generative and descriptive.
(Zhu, 2003)
 Modeling and Learning.
 Descriptive models on regular lattices.
 Markov random field models and Gibbs fields.
(Li, 2001, §1.2)
(Winkler, 2006, §2,3) (Dubes and Jain, 1989)
 The HammersleyClifford theorem.
 Bayes MRF Estimators
(Winkler, 2006, §1.4) (Li, 2001, §1.5)
(Geman and Geman, 1984)
 Examples:
 AutoModels (Besag, 1974)
(Li, 2001, §1.3.1, 2.3, 2.4) (Winkler, 2006, §15)
 Weak membrane models, MumfordShah, TV, etc.
 Applications:
 Image Restoration and Denoising (Li, 2001, §2.2)
 Edge Detection and Line Processes
(Li, 2001, §2.3) (Geman and Geman, 1984)
 Texture
(Li, 2001, §2.4) (Winkler, 2006, §15,16)
 MRF Parameter Estimation (Li, 2001, §6)
(Winkler, 2006, §5,6)
 MaximumLikelihood
 PseudoLikelihood
 Gibbs Sampler (and brief introduction to MCMC)
 Large Margin Methods (Blake et al., 2011, §15)
 Descriptive Models on Regular Lattices: Advanced Topics
 Discontinuities and Smoothness Priors
(Li, 2001, §4)
 FRAME and Minimax entropy learning of potential functionals.
(Zhu et al., 1998) (Zhu et al., 1997)
(Coughlan and Yuille, 2003)
 Hidden Markov random fields.
(Zhang et al., 2001)
 Conditional random fields.
(Lafferty et al., 2001)
(Kumar and Hebert, 2003)
(Wallach, 2004)
(Ladicky et al., 2009)
 MRF as a foundation for multiresolution computing.
(Gidas, 1989)
 Higher Order Extensions (Kohli et al., 2007)
(Kohli et al., 2009) and Field of Experts (Roth and Black, 2009).
 Descriptive and Generative Models on Irregular Graphs and Hierarchies.
 Markov random field hierarchies.
(Derin and Elliott, 1987)
(Krishnamachari and Chellappa, 1995)
(Chardin and Perez, 1999)
 OverComplete Bases and Sparse Coding
(Zhu, 2003, §6)
(Olshausen and Field, 1997) (Coifman and Wickerhauser, 1992)
 Textons
(Julesz, 1981)
(Zhu et al., 2005)
(Malik et al., 1999)
 AndOr graphs and contextsensitive grammars.
(Zhu and Mumford, 2007)
(Han and Zhu, 2005)
 Dirichlet Processes (DP) and Bayesian Clustering
(Ferguson, 1973)
 Latent Dirichlet Allocation, hierarchical DP and authortopic
models.
(Blei et al., 2003) (Teh et al., 2005)
(Steyvers et al., 2004)
 Correspondence LDA (Blei and Jordan, 2003)
 Integrating Descriptive and Generative Models
(Guo et al., 2006)
 Inference Algorithms.
 Boundary methods.
 Level set evolution.
(Chan and Vese, 2001)
 Region competition algorithm.
(Zhu and Yuille, 1996a)
 Exact Inference.
Exploit the structure of the graph or the form of the potentials to search for the global
optimum efficiently (in polynomial time).
 Chains and Trees.
 SumProduct algorithm (exact Belief Propagation).
(Bishop, 2006, §8)
(Yedidia et al., 2001)
(Frey and MacKay, 1997)
(Felzenszwalb and Huttenlocher, 2006)
 GraphCuts: mincut/maxflow relationship.
(Blake et al., 2011, §2)
What energy functions can/can not be minimized by graph cuts?
(Kolmogorov and Zabih, 2004)
 Approximate Inference.
 Discrete Deterministic Inference.
 GraphCuts: 1#1
Expansion algorithm.
(Boykov et al., 2001)
 GraphShifts algorithm.
(Corso et al., 2007)
(Corso et al., 2008b)
 Generalized Belief Propagation.
(Yedidia et al., 2005)
(Yedidia et al., 2000)
 Inference on AndOr graphs.
(Zhu and Mumford, 2007)
(Han and Zhu, 2005)
 Stochastic Inference.
(Forsyth et al., 2001)
 Mean Field Approximation.
 Gibbs sampling.
(Geman and Geman, 1984)
(Winkler, 2006, §5,7)
 MetropolisHastings and Markov chain Monte Carlo methods.
(Winkler, 2006, §10)
(Tierney, 1994)
(Liu, 2002)
 DataDriven MarkovMCMC algorithm. (Tu and Zhu, 2002)
(Tu et al., 2005)
(Green, 1995)
 SwendsenWang algorithm.
(Swendsen and Wang, 1987)
(Barbu and Zhu, 2005)
(Barbu and Zhu, 2004)
 Sequential MCMC and Particle Filters.
(Isard and Blake, 1998)
(Liu and Chen, 1998)
Similar Courses at Other Institutions:
(incomplete and in no important order)
Most items below have been cited above, but there are also some
additional references that extend the content of the course. When
available, PDFs of articles have been uploaded to the UBLearns
``Course Documents'' section. The naming convention is the first two
characters of (up to) the first three authors following by an acronym
for the venue (e.g., CVPR for Computer Vision and Pattern Recognition)
followed by the year. So, the Geman and Geman 1984 PAMI article is
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 Multigrid and Multilevel SwendsenWang Cuts for Hierarchic Graph
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 Generalizing SwendsenWang to Sampling Arbitrary Posterior
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 Spatial interaction and the statistical analysis of lattice systems
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 On the statistical analysis of dirty pictures (with discussion).
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 Pattern Recognition and Machine Learning.
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 Nonlinear Bayesian Image Modelling.
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 Markov Random Fields for Vision and Image Processing.
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 Multilevel Segmentation and Integrated Bayesian Model Classification
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