P. Fieguth - SD770 Homepage
SD770 Home Page for Fall 2015
Multidimensional Signal Modeling and Estimation:
Your presentation will take place on Thursday, December 3rd:
- Please upload your slide(s) ahead of time, to
this dropbox folder
- Please aim for about 3 minutes, and for sure no more than 5 minutes.
- Please prepare a one-page handout (one or two sides). Bring enough copies for the class (1 for yourself, 12 other students, 1 for me -- total of 14)
- The presentation and the handout are both associated with a modest grade. Not a large part of the course grade, but it is still worth doing a good job. Note that the presentation and handout are graded for engagement / interest / communications; I am not grading the problem you are working on itself.
- Please upload your project to this dropbox folder. The project is due by midnight on Wednesday, December 16th. Projects after that date are assessed a late penalty of 1% per day.
This course will study the statistical modeling, analysis, and
numerical methods of data processing, especially multidimensional data
processing, with problems in image processing serving as a motivating
context throughout the term. Thus there will be relatively little
overlap with other courses in image processing, although such courses would
preparation for this course.
The course will begin with a linear systems and statistics review,
followed by an overview of inverse problems, ill-posedness,
estimation theory, and Kalman filtering.
The body of the course will examine specialized Kalman filtering
algorithms, multi-dimensional estimation (marching methods, nested
dissection, multigrid), conditional methods (coordinate descent,
expectation-maximization), changes of bases (wavelets, radial basis
functions, Gabor functions etc.), implicit models (Markov random
fields, Gibbs random fields, simulated annealing), hypothesis testing,
and hypothesis trees.
Models will be illustrated and motivated by examples from current
literature and ongoing research, particularly in computer vision,
remote sensing, and medical imaging.
Prerequisites: One of SD675, SD575, ECE603, ECE604
2015 Course Syllabus
The text for the course is
P. Fieguth, Statistical Image Processing and Multidimensional Modeling, Springer, 2010
There are two primary sources for the text:
There is a home page for the text, showing the table of contents and a few errata.
- A printed, bound version from Amazon.ca.
- An electronic version, to which you have access from any UW computer, via
It will be difficult to get much out of this course without active
participation and keeping up with the reading. The requirements for
an audit are as follows:
Students who are regularly attending the class should either register
for credit or audit, as it is important for the department to receive
credit for the number of students taking its courses.
- Do half of the first four assignments. The work does
not need to be elegant or complete, but at least showing a bit of
attention to the assignment questions.
- Submit a proposal (ie, pick some topic and do a little reading on it)
- Give a brief presentation at the end of the term, mainly discussing
the topic and what it is that makes it interesting.
An approximate outline of how we will move through the text.
Period Reading Topics
Week 1 (Sep 15): 1 Introduction
Week 2 (Sep 22): 2.1 - 2.5 Inverse Problems, Regularization
Week 3 (Sep 29): 3 Static Estimation
Week 4 (Oct 6): 4.1 - 4.2 Dynamic Estimation, Kalman Filtering
Week 5 (Oct 13): 4.2.2 - 4.2.4 Kalman Filter Methods
Week 6 (Oct 20): 5.1 - 5.5 Determinisitic Modelling
Week 7 (Oct 27): 5.6 - 5.7, 6.1 Statistical Modelling
Week 8 (Nov 3): 6.2 - 6.7 Markov / Gibbs Random Fields
Week 9 (Nov 10): 7 Hidden Markov models
Week 10 (Nov 17): 8 Changes of Bases
Week 11 (Nov 24): 9 Linear System Solvers
Week 12 (Dec 1): 10 Large-Scale Kalman Filtering
A major part of the course grade is based on a project of
each student's choosing. I will ask you to prepare a brief
proposal (one or two paragraphs).
Project Proposal: A project topic should be chosen by the middle of
October. This involves selecting a topic (ideally from the list
below, or from subjects in the course notes), and writing one or two
paragraphs, plus a reference or two, describing slightly more
specifically what you would like to look at.
This applies to both regular (credit) and audit students. Please begin
thinking about possible project ideas. A piece of paper with your
short proposal is due in class by Oct 22.
I have a few
project suggestions here; I will work on adding to this list:
Keep in mind that you can't ``re-use'' a project from another course,
nor can you ``borrow'' part of existing thesis work for the project.
Of course you are encouraged to look at something related to your research
area, and maybe some of the insights you gain in doing your project
will find their way into your thesis, however your work for the
project needs to be something you haven't already done.
- Application of statistics to a problem in remote sensing
- A study of linear systems methods (CG, SOR, MG etc.)
- Spectral (eigenvalue distribution) issues in linear systems
- Application / survey of Markov random fields
- Application / survey of simulated annealing
- Application / survey of multigrid methods
- Application / survey of wavelet methods in large-scale processing,
especially for estimation and statistics
- Krylov subspace methods (conj. gradient for statistics)
- Implementation of the marching methods algorithm, especially with smoothing
- FFT and Toroidal fields
- An experimental study of matrix conditioning
- Different Kalman filter algorithms (especially square-root and information forms)
- Nonlinear (extended) Kalman filters
- A study of any hierarchical change-of-basis method (Laplacian
pyramids, Gabor functions, wavelets, hierarchical triangles etc.)
- A study of the mathematics of regularization and conditioning
- Applicability of robust statistics to large-scale problems
In general, because the concepts in this course are fairly advanced, I
think that most of you would benefit more from implementing an
algorithm and doing some simulations rather than trying to read some
state-of-the-art journal papers.
After grading a lot of assignments and course projects, I find myself
writing the same advice over and over again. All students in SD770
should take a look at the following:
If your English writing skills are a litle weak, you can at least
eliminate some of the most common errors by looking at the
If you would like more suggestions on books which talk about
grammar / style, please talk to me.
Project due date:
Projects are due by midnight on Wednesday, December 16th. Projects submitted after that time are considered late, with a late penalty of 1% per day.
Assignment Handout and Due Dates:
|Topic||Assignment||Date Due||Upload Link
|Assign 1: Matrix Conditioning||Problems 2.1-2.4||Oct 1
|Assign 2: Static Estimation, Interpolation||Problems 3.4-3.7||Oct 19||Dropbox Link
|Assign 3: Kalman Filtering||Problems 3.8, 4.1, 4.4||Oct 29||Dropbox Link
|Assign 4: 2D Surface Reconstruction||Problems 5.3, 5.4||Nov 9||Dropbox Link
|Assign 5: Random Fields||Problems 6.1, 6.2, 6.3, 7.1, 7.2||Nov 19||Dropbox Link
|Assign 6: Computational Methods||Problems 9.1, 9.2, 9.3, 10.1||Dec 3||Dropbox Link
|Project Proposal||Oct 25||Dropbox Link
|Project Presentations||Dec 3||Dropbox Link
|Project Submission||Dec 16||Dropbox Link
The assignments are due by midnight on the specified date.
Assignments and projects are to be submitted electronically:
- The assignment must be a PDF file.
- Attach only a single file, don't give me multiple files or a zip archive. Just a single PDF file.
- Use the Dropbox upload link provided.
(The background to this web page is an example of a toroidally-periodic random field, generated
using FFT methods, one of the approaches taught in SD770).
(Page last updated September 13, 2016, [an error occurred while processing this directive])