EE 528: Digital Image Processing, Fall 2009
Announcements
- Complete Homework 5 posted
- I will grade best four out of
the five homeworks
- Homework
4 posted
- Exam on Oct 28
- Homework 3 posted, due October 21
- Homework 2b also posted, entire HW 2
due October 5
- In
each of the homeworks only a
few selected problems will be graded
- Homework 2a posted, due Sept 25
- Homework 1 posted due Sept 18.
- From
a student's email, I just realized that I did not ever explicitly give
out the textbook (Anil
K. Jain, Fundamentals of Digital Image Processing) name. Please feel free to
borrow it from me for your
first homework if you haven't still ordered it
- Grading policy and Midterm exam date posted
Instructor:
Prof. Namrata Vaswani,
Email: namrata AT iastate.edu,
Phone:
515-294-4012,
Office: 3121 Coover Hall
Informal TAs: Samarjit Das
(samarjit AT iastate.edu) and Wei Lu (luwei AT iastate.edu)
Location and Time: 1016 Coover,
M-W-F 12:10-1pm
Office Hours: Tuesdays 10-11, Friday 4:30-5:30
Prerequisites: EE 224 (Signals and
Systems), EE 322
(Probability for EE), some linear algebra, calculus. EE424 is
listed but not really necessary (if you know 224 and 322 well,
it should suffice)
Textbook: Anil K. Jain, Fundamentals of Digital Image Processing (link)
Grading Policy and Midterm exam date
Homeworks
Project Details
Class Syllabus and Handouts
- Signal Processing recap (Prof. Aditya Ramamoorthy)
- Recap (where needed) and Classical Image Processing Topics (from
AK Jain)
- Image Sampling and Quantization - Chapter 4 of AK Jain
- Linear Algebra recap - Chapter 2 of AK Jain and some more
- Image Transforms - Chapter 5 of AK Jain
- Probability
Recap
- Image Enhancement: slides mostly
contain images - details of the material covered in class and available
in the book
- Image Restoration: slides
do not contain all the details covered in class
- Image and Video Compression
- JPEG, MPEG: notes posted in WebCT
- Medical imaging reconstruction: CT and
MRI, compressive sensing
intro
- Computed Tomography (CT): Chapter 10 of AK Jain, my scanned
notes posted in WebCT
- Magnetic Resonance Imaging (MRI):
- Compressive Sensing for MRI / CT
- Computer vision Topics: Recap of earlier topics covered by
Samarjit and Tracking
Grading Policy
- 33% each for homeworks, midterm exam and project, 1% for
attendance
- About 4-6 homeworks will be given
- In each of the homeworks only
a few selected problems will be graded
- Midterm exam date: last week
of October tentatively
Exam
- Exam on Oct 28 (Wed): in class
- Exam Syllabus
- Sampling 4.2, 4.3 (only nonrectangular grid sampling and
interlacing), 4.4 (except Lagrange interpolation
- Quantization 4.5, 4.6
- Transforms 5.1, 5.2, 5.4, 5.5, 5.6
- We also did KLT (5.11) but that will not be on the exam
- Enhancement 7.1, 7.2, 7.3, 7.4
- Restoration 8.1, 8.3, 8.4
- On popular demand, I will make it open book and open notes, but that
means it will be a little more diffucult
- I will strongly
recommend getting a notes sheet (2-3 pages): that will be most
useful
Homeworks
- In each of the homeworks only a
few selected problems will be graded
- Homework 1 due Friday Sept 18
(submit the numerical part on paper, and the Matlab part via email to
ee528homeworks AT gmail.com)
- Homework 2 due Monday Oct 5
(submit the numerical part on paper, and the Matlab part via email to
ee528homeworks AT gmail.com)
- Homework 3 due Wednesday Oct 21
(submit via email to
ee528homeworks AT gmail.com)
- Homework 4 due
November 30
(submit Matlab part via email to
ee528homeworks AT gmail.com)
- Homework 5 due November 30 (submit
via email to ee528homeworks AT gmail.com)
Project Details
Useful Links
My old Image Processing class
MATLAB
Image Processing toolbox
Milan
Sonka's Image Processing notes
An excellent
tutorial on DSP (DFT, sampling, decimation etc)
A
useful set of Linear Algebra notes (Chap 5 and onwards)
Set
of tutorials on the book page of Gonzalez and Woods' Digital
Image Processing 2/E
An
easy, very basic DSP tutorial
Some Other Useful classes at ISU
EE527
(Detection
and Estimation Theory)
STAT580
(Computational Methods on Statistics) - see for handout
on Monte Carlo methods
EE524
(Digital
Signal Processing)
EE523
(Random Processes)