BIL 717 - Image Processing (Spring 2014)

Lectures: Wed 09:00-11:45@D5

René Magritte's painting (Empire of Light II), 1950 René Magritte's painting (Empire of Light II), 1950

Instructor: Erkut Erdem

erkut-at-cs-hacettepe.edu.tr
114
+90 312 297 7500, 149

Course Description

This course provides a comprehensive overview of fundamental topics in image processing for graduate students. The goal is to give a deeper understanding of the state-of-the-art methods in image processing literature and to study their connections. In this context, the course makes the students gain knowledge and skills in key topics and provides them the ability to employ them in their advanced-level studies.

Prerequisites

Good math (calculus, linear algebra, statistics) and programming skills. A prior, introductory-level course in image processing is recommended.

Schedule (Tentative)

Week Date Topic Notes
1 Feb 19 Overview of Image Processing Slides: (pdf, 4pp)
Reading: D. Marr, Vision, The Philosophy and the Approach, 1982
2 Feb 26 Linear Filtering, Edge Detection Slides: (pdf, 4pp)
Notes: pdf
Reading: A. P. Witkin, Scale-space filtering: A New Approach to Multi-Scale Description, In Proc. IJCAI, 1983
Reading: D. Marr and E. Hildreth, Theory of Edge Detection, Proc. R. Soc. Lond. B, 1980
3 Mar 5 Nonlinear Filtering Paper selections due (final schedule)
Slides: (pdf, 4pp)
Notes: pdf
Reading: P. Perona and J. Malik, Scale-Space and Edge Detection Using Anisotropic Diffusion, IEEE Trans. Pattern Anal. Mach. Intell., 1990
Reading: L. Rudin et al., Nonlinear Total Variation Based Noise Removal Algorithms, Phys. D., 1992
Paper presentation: J. Weickert, Coherence-Enhancing Diffusion of Colour Images, Image and Vision Computing, 1999
4 Mar 12 Variational Segmentation Models PA1 out: (pdf, code)
Project proposals due
Slides: (pdf, 4pp)
Notes: (Mumford-Shah, Snakes)
Reading: T. Chan and L. Vese, Active contours without edges, IEEE Trans. Image Processing, 2001
Reading: E. Erdem and S. Tari, Mumford-Shah Regularizer with Contextual Feedback, J. Math. Imaging and Vision, 2009
Paper presentation: C. Xu and J. L. Prince, Snakes, Shapes, and Gradient Vector Flow, IEEE Trans. Image Processing, 1998
5 Mar 19 Modern Image Filtering Slides: (pdf, 4pp)
Reading: C. Tomasi and R. Manduchi, Bilateral Filtering for Gray and Color Images , In Proc. ICCV, 1998
Reading: A. Buades et al., A non-local algorithm for image denoising, In Proc. CVPR, 2005
Reading: H. Takeda et al., Kernel Regression for Image Processing and Reconstruction, IEEE Trans. Image Processing, 2007
Paper presentation: L. Pizarro et al., Generalised Nonlocal Image Smoothing, Int. Journal Comp. Vis., 2010
6 Mar 26 Modern Image Filtering (cont’d.) PA1 due­
Slides: (pdf, 4pp)
Reading: L. Karacan et al., Structure Preserving Image Smoothing via Region Covariances, ACM Trans. Graph., 2013
Paper presentation: Xu et al., Structure extraction from texture via relative total variation, ACM Trans. Graph., 2012
Paper presentation: Zontak et al., Separating signal from noise using patch recurrence across scales, In Proc. CVPR, 2013
7 Apr 2 Image Deblurring Slides: (pdf, 4pp)
Reading: R. Fergus et al., Removing camera shake from a single image, SIGGRAPH 2006
Reading: S. Cho and S. Lee, Fast motion deblurring, ACM Trans. Graph., 2009
Paper presentation: A. Levin et al., Understanding and evaluating blind deconvolution algorithms, In Proc. CVPR, 2009
8 Apr 9 Clustering-based Segmentation Models Slides: (pdf, 4pp)
Reading: J. Shi and J. Malik, Normalized Cuts and Image Segmentation, IEEE Trans. Pattern Anal. Mach. Intell., 2000
Reading: D. Comaniciu and P. Meer, Mean Shift: A Robust Approach Toward Feature Space Analysis, IEEE Trans. Pattern Anal. Mach. Intell., 2002
Paper presentation: R. Achanta et al., SLIC Superpixels Compared to State-of-the-art Superpixel Methods, IEEE Trans. Pattern Anal. Mach. Intell., 2012
9 Apr 16 Sparse Coding PA2 out: (pdf, code)
Project progress reports due
Slides: (pdf, 4pp)
Reading: M. Elad et al., On the Role of Sparse and Redundant Representations in Image Processing, IEEE Proceedings, 2010
Reading: M. Aharon et al., K-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation, IEEE Trans. Signal Processing, 2006
Paper presentation: J. Yang et al., Image super-resolution as sparse representation of raw image patches, In Proc. CVPR, 2008
10 Apr 23 No class (The Republic Day) Project progress reports due
11 Apr 30 Graphical Models PA2 due­­­­­­
PA3 out (pdf, code)
Slides: (pdf, 4pp)
Reading: A. Blake and P. Kohli, Introduction to Markov Random Fields, Markov Random Fields for Vision and Image Processing, The MIT Press, 2011.
Reading: C. Rother et al., GrabCut: Interactive foreground extraction using iterated graph cuts, ACM Trans. Graph., 2004.
Paper Presentation: M. P. Kumar et al., OBJCUT: Efficient segmentation using top-down and bottom-up cues, IEEE Trans. Pattern Anal. Mach. Intell., 2010
12 May 7 Semantic Segmentation Slides: (pdf, 4pp)
Reading: J. Shotton et al., TextonBoost for Image Understanding: Multi-Class Object Recognition and Segmentation by Jointly Modeling Texture, Layout, and Context, Int. Journal Comp. Vis., 2007
Reading: C. Liu et al., Nonparametric Scene Parsing via Label Transfer, IEEE Trans. Pattern Anal. Mach. Intell., 2011
Paper Presentation: J. Tighe and S. Lazebnik, SuperParsing: Scalable Nonparametric Image Parsing with Superpixels, In Proc. ECCV, 2010
13 May 14 Visual Saliency PA3 due
Slides: (pdf, 4pp)
Reading:
Reading: L. Itti et al., A model of saliency-based visual attention for rapid scene analysis, IEEE Trans. Pattern Anal. Mach. Intell., 1998
Reading: E. Erdem and A. Erdem, Visual saliency estimation by nonlinearly integrating features using region covariances, Journal of Vision, 2013
Paper Presentation: T. Judd et al., Learning to predict where humans look, In Proc. ICCV, 2009
14 May 21 What we’ve done, Where we’re going

Grading

Paper presentations and Quizzes

The students will be required to present at least one research paper either of their choice or from the suggested reading list. These papers should be read by every student as the quizzes about the presented papers will be given on the weeks of the presentations.

Programming Assignments

There will be three 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. All assignments have to be done individually, unless stated otherwise.

Project

The students will be required to do a project in image processing which should be done in individually. For a detailed description of the course project and the related schedule, follow this link. In preparing your progress and final project reports, you should use the provided template and submit them electronically in PDF format.

Reference Books

Resources

Communication:

The course webpage will be updated regularly throughout the semester with lecture notes, programming and reading assignments and important deadlines. All other course related communications will be carried out through Piazza. Please enroll it by following the link https://piazza.com/hacettepe.edu.tr/spring2014/bil717

Policies:

Attendance to lectures is required. You are responsible for all material presented in lecture.

All work on assignments must be done individually unless stated otherwise. You are encouraged to discuss with your classmates about the given assignments, but these discussions should be carried out in an abstract way. That is, discussions related to a particular solution to a specific problem (either in actual code or in the pseudocode) will not be tolerated.

In short, turning in someone else’s work, in whole or in part, as your own will be considered as a violation of academic integrity. Please note that the former condition also holds for the material found on the web as everything on the web has been written by someone else.

© 2014 Hacettepe University