Instructor: B. S. Manjunath
This course provides an overview of the fundamental topics in image processing. Lectures emphasize the basic theory and the course projects explore some current topics and emphasize algorithm development and implementation. Course outline and additional information distributed in class on 01/04/2011 (Course Outline)
Topics for Winter 2011
<![if !supportLists]>1. <![endif]>Introduction: an overview of some of the preliminaries.
<![if !supportLists]>a. <![endif]>Digital picture transforms, 2-D DFT.
<![if !supportLists]>b. <![endif]>Random fields and statistical image models.
<![if !supportLists]>c. <![endif]>Image representations: 2-D sampling theory, generalization to random fields, representation using orthonormal basis functions.
<![if !supportLists]>2. <![endif]>Digital Image Transforms and Coding
<![if !supportLists]>a. <![endif]>Overview of transform compression
<![if !supportLists]>b. <![endif]>Various transforms: Karhunen-Loeve, Fourier, Cosine, Hadamard, Walsh, É
<![if !supportLists]>c. <![endif]>2-D Wavelet transforms
<![if !supportLists]>d. <![endif]>JPEG and JPEG-2000.
<![if !supportLists]>3. <![endif]>Image Restoration
<![if !supportLists]>a. <![endif]>Inverse and Least-squares filtering
<![if !supportLists]>b. <![endif]>Maximum-likelihood (ML) and Maximum a-posteriori (MAP) estimation
<![if !supportLists]>4. <![endif]>Image Reconstruction from Projections
<![if !supportLists]>a. <![endif]>Image Projections and the Fourier Slice Theorem
<![if !supportLists]>b. <![endif]>Filtered back projection algorithm
<![if !supportLists]>c. <![endif]>Iterative reconstruction techniques
<![if !supportLists]>5. <![endif]>Selected Topics: these will cover the general areas of image segmentation and image registration. Papers and course projects will explore these topics in more detail.
30% for H/W, preparing critiques, paper presentations, and participation in discussions; 30% for the class project; 40% for the final. HWs are due in class on the due dates. Class projects should involve some kind of computer implementation. Individual projects strongly encouraged but groups of two ok if the project needs are justified
There is no required text book for the class. Lecture notes will be posted (will try my best to post them before the lectures). However, not everything that is discussed in class will be in these notes. I will also handout additional reading materials during the quarter.
Jan 13, 2011
Paper assignments, groups
Project proposals are due on or before this date. Include a brief summary of proposed work and any appropriate references. Proposals should be no longer than 2 pages and hard-copies only. I will return the proposals with my comments on February 1. You are strongly encouraged to meet with me and discuss your proposals before submitting it.
Feb 3: Paper Critiques Due Before Class (email the electronic version, PDF files only, that will be posted on the class web site).
Presentation schedule (plan for ~35 mins presentations and 10 mins Q&A)
Distribute the critiques by Feb 3 (all topics).
(a) Compressed sensing [slides] (b)Face recognition [slides]
(a) Image Forensics [slides], (b) Mobile Image Processing [slides]
In-class presentations (approx.. 15 minutes) of your project.
Final report due by 5PM
March 15, 12-3PM
HW#4 (due Feb 15, by email) Use the following images in your experiments, note that one of them is color. You can either process the gray scale image or work in color (extra credit). Temple, Monkey.
2007 Exam with solutions (this was a take-home exam)
2009 Exam (solution) (this was also a take-home exam)
Lecture 1 Lecture 2 Lecture 3 Lecture 4 (handout on sampling)
Lecture 05 (handout on digital transforms part1 part2)
Lecture 07 Paper by Burt and Adelson on Laplacian pyramids (required reading). Additional Reading: Multiresolution representation using wavelets by Mallat. (pdf)
Lecture 09 ( see also Figures from G&W Chap 7 and a brief overview of JPEG compression)
Lecture 10 [new: MSE derivation from Prof John Shynk]
Lecture 11 [student paper presentations][compressive sensing] [face recognition using sparse representations]
Lecture 12 [student paper presentations][image forensics][mobile image processing]
Lecture 13 [deblurring examples in Matlab][examples of inpainting][review of histogram equalization]
Lecture 16 (mean-shift clustering)
Lecture 17 (Level set segmentation Notes)
Lecture 18 (continue Level set discussions and wrap-up)
Note on Image Registration: Many of you are working on the image registration project. You may find the lecture notes developed by Marco Zuliani very useful.