BIL 717 - Image Processing (Spring 2012)

BIL 717 - Image Processing (Spring 2012)

Lectures: Tuesday 13:30-16:30 @D5

The False Mirror (Courtesy of René Magritte)

Instructor: Erkut Erdem

e-mail: erkut-at-cs.hacettepe.edu.tr

Office: 114

Tel: 297 7500 / 149

Office hours: Friday, 13:00-15:00

Course Description:

This course presents a comprehensive overview of topics in image processing for graduate students. The emphasis is mainly on the connections between statistical approaches, PDE-based methods, variational formulations, etc. The students will gain knowledge and skills in topics not ordinarily covered in depth in regular courses and of specific interest to advanced level studies.

Prerequisites:

Students should have good math background (Calculus, Linear Algebra, Statistical Methods) and programming skills (MATLAB, C/C++). A prior, introductory-level course in image processing is recommended.

Course Content

Image Processing Basics
Degradations in Digital Images
Smoothing via Averaging, Gaussian Filter and Median Filter
PDE-based Formulations, Linear Heat Equation, and Perona-Malik Model
Anisotropic Diffusion (Weickert’s Approach), Osher and Rudin Shock filters
Neighborhood Filters, Nonlocal Means Filter (NL-Means)
Variational Methods and Mumford-Shah Functional
Snakes, Geodesic Active Contours, and Level-Set Methods
Graph-Theoretic Formulations and Normalized Cuts
Selected Topics

Reading Material:

Lecture notes and handouts
Papers and journal articles

Grading Policy:

Assignments, 30%
Project, 30%
Final Exam, 35%
Class participation, 5%

Assignments:

There will be at least four assignments related to the topics covered in the class. Each assignment will involve implementing an algorithm, carrying out a set of experiments to evaluate it, and writing up a report on the experimental results. There will also be some warm-up and reading assignments. All assignments have to be done individually, unless stated otherwise.

Project:

In addition to the assignments given throughout the semester, the students taking the course will be required to do a project in computer vision. Students can choose to work individually or in groups of at most 2 people.

This project may be

An original implementation of a new or published study
A detailed empirical evaluation of two or more related methods not covered in the class

For a detailed description of the course project, follow this link.

On March 20th, there will be a special brainstorming session on project previews. For that, each project team should prepare a brief (3-5 min.) presentation on their specific project idea. Each presentation should introduce the study which is expected to form the basis of the project; or should introduce the research topic to be reviewed, including a list o major papers on that specific area.

A project proposal not longer than two pages must be submitted and approved by April 3rd.

The final reports must be between 8-10 pages and submitted by May 27th. In preparing your project reports, you should use the provided template and submit them electronically in PDF format.

Course Schedule:


Lecture	Date	Topic	Lecture Notes	Additional Readings	Assignments

1	14/02	Introduction	Lecture1.pdf
2	21/02	Image Processing Basics	Lecture2.pdf		Pset 0 out code, images
3	28/02	Gaussian Filtering and Linear Heat Equation Laplacian of Gaussian, the Canny Edge Detector	Lecture3a.pdf Lecture3b.pdf	D. Marr and E. Hildreth, Theory of Edge Detection, Proc. R. Soc. Lond. B, 1980
4	06/03	The Perona-Malik Model and Anisotropic Diffusion	Lecture4.pdf	J. Weickert, Coherence-enhancing diffusion of colour images, Image and Vision Computing, 1999	Pset 0 in Pset 1 out code, images
5	13/03	Nonlinear Structure Tensors and Diffusion Tensors		T. Brox, J. Weickert, B. Burgeth and P. Mrazek, Nonlinear structure tensors, Image and Vision Computing, 2006 G. Steidl and T. Teuber, Diffusion Tensors for Processing Sheared and Rotated Rectangles, IEEE Trans. Image Processing, 2009
6	20/03	Mumford-Shah Functional, Ambrosio-Tortorelli (AT) Formulation	Lecture6.pdf	J. Shah, A Common Framework for Curve Evolution, Segmentation and Anisotropic Diffusion, Proc. IEEE CVPR, 1996	Pset 1 in Pset 2 out code
7	27/03	AT Formulation (cont'd), Total Variation Denoising		E. Erdem and S. Tari, Mumford-Shah Regularizer with Contextual Feedback, Journal of Mathematical Imaging and Vision, 2009 L. Rudin, S. Osher and E. Fatemi, Nonlinear Total Variation based noise removal, Phys. D., 1992
8	03/04	Active Contours/Snakes, Chan-Vese (CV) Formulation	Lecture8a.pdf Lecture8b.pdf	M. Kass, A. Witkin, D. Terzopoulos, Snakes: Active contour models, International Journal of Computer Vision, 1987 T. Chan and L. Vese, Active contours without edges, IEEE Trans. Image Processing, 2001 X. Bresson, S. Esedoglu, P. Vandergheynst, J. P. Thiran and S. Osher, Fast global minimization of the active contour/snake model, J. Math. Imaging and Vision, 2007 C. Xu and J. L. Prince, Snakes, Shapes, and Gradient Vector Flow, IEEE Trans. Image Processing, 1998
9	10/04	Bilateral Filtering, NL-Means	Lecture9.pdf	C. Tomasi and R. Manduchi, Bilateral Filtering for Gray and Color Images, Proc. IEEE ICCV, 1998 F. Porikli, Constant time O(1) Bilateral Filtering, Proc. IEEE CVPR, 1998 A. Buades, B. Coll and J.-M. Morel, A Non-local Algorithm for Image Denoising, Proc. IEEE CVPR, 2005 L. Pizarro, P. Mrazek, S. Didas, S. Grewenig and J. Weickert, Generalised Nonlocal Image Smoothing, International Journal of Computer Vision, 2010	Pset 2 in
10	17/04	No lectures
11	24/04	Markov Random Fields Shape Analysis	Lecture11a.pdf Lecture11b.pdf	A. Blake and P. Kohli, Introduction to Markov Random Fields, Chapter 1, Markov Random Fields for Vision and Image Processing S. Geman and D. Geman, Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images, IEEE Trans. Pattern Anal. Mach. Intell., 1984 S. Roth and M. J. Black, Fields of Experts: A Framework for Learning Image Priors, Proc. IEEE CVPR, 2005 L. Gorelick, M. Galun, E. Sharon, R. Basri and A. Brandt, Shape Representation and Classification Using the Poisson Equation, Proc. IEEE CVPR, 2004 B. W. Hong, E. Prados, S. Soatto and L. Vese, Shape representation based on integral kernels: Application to image matching and segmentation, Proc. IEEE CVPR, 2006 E. Erdem, S. Tari and L. Vese, Segmentation Using the Edge Strength Function as a Shape Prior within a Local Deformation Model, Proc. IEEE ICIP, 2009
12	01/05	No lectures
13	08/05	Project Presentations
14	15/05	Project Presentations

Resources:

Reference Books:
- Mathematical Problems in Image Processing: Partial Differential Equations and the Calculus of Variations, G. Aubert and P. Kornprobst, 2nd Edition, Springer-Verlag, 2006
- Image Processing And Analysis: Variational, PDE, Wavelet, And Stochastic Methods, T. Chan and J. Shen, Society for Industrial and Applied Mathematics, 2005

Related Conferences:
- International Conference on Scale Space and Variational Methods in Computer Vision (SSVM)
- Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR)
- IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Advances in Neural Information Processing Systems (NIPS)
- IEEE International Conference on Computer Vision (ICCV)
- European Conference on Computer Vision (ECCV)
- IEEE International Conference on Pattern Recognition (ICPR)
- IEEE International Conference on Image Processing (ICIP)
- British Machine Vision Conference (BMVC)

Related Journals:
- IEEE Transactions on Pattern Analysis and Machine Intelligence (IEEE TPAMI)
- IEEE Transactions on Image Processing (IEEE TIP)
- Journal of Mathematical Imaging and Vision (JMIV)
- International Journal of Computer Vision (IJCV)
- Computer Vision and Image Understanding (CVIU)
- Image and Vision Computing (IMAVIS)
- Pattern Recognition (PR)

MATLAB Resources:
- Introduction to MATLAB, by Danilo Šćepanović
- MATLAB Tutorial, by Stefan Roth
- MATLAB Primer, by MathWorks
- Code Vectorization Guide, by MathWorks
- Writing Fast MATLAB code, by Pascal Getreuer
- MATLAB array manipulation tips and tricks, by Peter J. Acklam

Linear Algebra:
- A Geometric Review of Linear Algebra, by Eero Simoncelli
- An Introduction to Linear Algebra in Parallel Distributed Processing, by M.I. Jordan

Resources for scientific writing and talks:
- Notes on writing, by Fredo Durand
- How to write a great research paper, by Simon Peyton Jones (video)
- Small Guide To Giving Presentations, by Markus Püschel
- Giving an effective presentation: Using Powerpoint and structuring a scientific talk, by Susan McConnell (video)
- Writing papers and giving talks, by Bill Freeman (notes)

Communication:

The course webpage will be updated regularly throughout the semester with lecture notes, programming and reading assignments and important deadlines. All other communications will be carried out through Piazza. Please enroll it by following the link https://piazza.com/hacettepe.edu.tr/spring2012/bil717