Digital Image Processing
- MW 4:30 - 5:45 PM
- DEH 130
Dr. J. P. Havlicek
Office Hours: MW 3:30 - 4:30 and by appointment
- MATLAB ASSISTANT:
Office Hours: M 2:00 - 4:00, W 11:00 - 1:00, and by appointment
- TEXT & REFERENCES:
- "ECE 5273 Digital Image Processing" Lecture Notes Pack.
The lecture notes will be made available on Canvas.
- Recommended text: A. Bovik, The Essential Guide to Image
Processing, Academic Press, Burlington, MA, 2009,
This book has significant overlap with the course lecture notes.
The examples are implemented in LabVIEW.
- Recommended text: R.C. Gonzalez and R.E. Woods, Digital Image
Processing, Prentice Hall, Upper Saddle River, NJ, 2007,
ISBN 978-0-13-168728-8. Previous editions were published by
Addison-Wesley with ISBN 0-201-50803-6. This is a classical
comprehensive text on digital image processing.
- COURSE WEB PAGE:
You will submit your homework assignments electronically on Canvas.
Announcements and lecture notes will also be posted on Canvas.
In addition, important information will also be distributed by Canvas notifications.
Make sure to configure your Canvas notifications!
ECE 3793, Signals and Systems, or elementary knowledge of the
Fourier transform, the Fourier series, the discrete Fourier transform, and
their use in linear system analysis.
- REASONABLE ACCOMMODATION POLICY:
The University of Oklahoma is committed to providing reasonable
for all students with disabilities. Students with disabilities who require
accommodations in this course are requested to speak with the instructor as
early in the semester as possible. Students with disabilities must be
registered with the Disability Resource Center prior to receiving
accommodations in this course. The Disability Resource Center is located
in Goddard Health Center, Suite 166, (405) 325-3852 (Tel)
or (405) 325-4173 (TDD only). The Disability Resource Center web site is
- RELIGIOUS HOLIDAYS:
It is the policy of the University to excuse absences of students that result
from religious observances and to provide without penalty for the
rescheduling of examinations and additional required classwork that may fall
on religious holidays. It is the responsibility of the student to
make alternate arrangements with the instructor at least one week prior
to the actual date of the religious holiday.
- UNIVERSITY POLICY ON ACADEMIC HONESTY:
This page outlines the University's expectations of academic honesty, defines
misconduct, provides examples of prohibited conduct, and explains the sanctions
available for those found guilty of misconduct. Additional information
clarifying the precise meaning of academic misconduct in this course
is provided below.
The UOSA Statement of Academic Integrity will be used in this course.
- COURSE DESCRIPTION:
This introductory graduate-level course provides an overview of the main
concepts, results, and techniques that are the foundations of current
academic research and industry practice in digital image processing.
Homework will be assigned during class.
Homework solutions will be posted on the course web page.
You are encouraged to
work together on homework, but DO NOT COPY! Each problem solution
that you turn in must be your own;
- if you copy another person's solution and turn it in as your own,
then you are guilty of academic misconduct.
- If you copy an old homework solution without working the problem yourself
and turn it in, then you are guilty of academic misconduct.
All computer codes and results that you submit in this course must be your own
- If you obtain code from another person in an electronic format and
incorporate it into the solution that you turn in, then
you are guilty of academic misconduct.
- If you obtain code from another person in electronic or hardcopy
formats and then type it in yourself and include it
in the solution that you turn in, then you are guilty of
- In certain cases, it may be acceptable to incorporate existing public
domain and/or library computer algorithms and codes into a solution
that you submit. In such cases, however, you must always obtain prior
authorization from the instructor and you must always document the source
of any algorithms and/or code that is not your own original work.
- LATE HOMEWORK POLICY:
Homework assignments will be submitted electronically
on Canvas and will generally be due at midnight on the published due date. Late
homework will not be accepted.
There are two reasons for this policy. First,
accepting a late homework assignment from one student is unfair to other
students who may have stayed up all night to get the assignment done
and may also
have sacrificed grades in other classes to get it done.
Second, it would be detrimental to the overall learning outcomes of the class
to delay the posting of homework solutions in order to accommodate late assignments.
- TESTS & EXAMS:
There will be two tests. There will NOT be a final exam.
Test I will be announced in class at least one week in advance.
Test II will be given during the scheduled final exam period for the course.
You may use calculators
on the tests, but you may NOT use calculator programs.
The tests are OPEN NOTES. This means that you may bring a clean copy of the course lecture
OTHER MATERIALS ARE NOT ALLOWED.
All work that you submit on your
test paper must be your own; collaboration on a test constitutes a serious
case of academic misconduct.
If you miss a test and your absence is NOT officially excused,
then you will receive a zero grade.
If you miss a test and your absence IS officially excused,
then a makeup test will be given by arrangement with the instructor. Makeup
tests may be written or oral at the discretion of the instructor.
- TERM PROJECT:
A term project will be required of all students in the class. You will design
your own term project. Written proposals for the term project will be due
near the middle of the semester; the due date will be announced in class and
posted on the course web site. A written progress report will also be due
sometime between the proposal and the final project submission; the due date
will be announced in class and posted on the course web site.
There is a required format for the written project proposal and written
progress report; they are required to conform to the IEEE Signal Processing
Society requirements for conference papers. More information on this
requirement will be given in class.
The term project must involve significant creative activity and analysis or
design. Look in recent journal articles and conference
proceedings for ideas (analysis, extension, and implementation of a technique
described in a published paper do constitute creative activity).
The term project should not simultaneously be used
to satisfy requirements for
another class unless advance permission is obtained from the instructor.
For substantial projects, joint or group work may be acceptable, but must be
approved by the instructor in advance.
The standards of academic honesty given above for homework apply to the term
project as well.
- Development of new techniques.
- Extension of existing techniques.
- Simulation and analysis of existing techniques that yields new insight.
- Application of existing techniques to real-world image processing or
machine vision problems.
- COMPUTER USE:
Computer use will be required for the homework assignments and term project. You may
use CoE computers or any other computer that you have access to. Use of
C, C++, and/or Matlab are endorsed and recommended.
Matlab will be required for some homework assignments.
You can download a license key and instructions for installing
Matlab from the OU IT Store at
Alternatively, you can purchase the Matlab and Simulink
Student Suite for $99:
Matlab is also available on the College of Engineering
Virtual Lab (see handout on the course web site).
Your final numerical grade will be calculated as shown in the following table.
|Project Progress Report
These numerical grades will be converted into letter grades using a curve
determined by the instructor.
The same curve will be applied to all students in the class.
The curve will never hurt your grade relative to the
- Course Introduction, Image Types, Imaging Geometry, Image Acquisition,
Imaging Devices, Image Representation.
- Binary Image Processing.
- Histogram, Point Operations, Algebraic & Geometric Image Operations.
- Digital Fourier Transform, Sampling Theorem.
- Convolution, Linear Filtering, Linear Image Enhancement, Linear Image
- Nonlinear Image Filtering.
- Introduction to Digital Image Analysis.
- Digital Image Coding and Compression.
Updated: January 23, 2017