COURSE DESCRIPTION
Cp Eng 345 – Digital Image Processing
(Offered Every Spring Semesters)
Required or Elective Course: Elective
Catalog Description:
[Lec. 3.0] Fundamentals of human perception, sampling and quantization, image
transforms, enhancement, restoration, image and video compression and coding.
Prerequisites: El Eng 267 Linear Systems II.
Prerequisites by topic: Linear algebra and matrix operations, linear time-invariant
systems, Fourier transform analysis and filtering
Textbooks and other required material: R.C. Gonzalez and R.E. Woods, Digital Image
Processing, 2nd ed., (Prentice Hall 2002).
Course learning outcomes/expected performance criteria:
1. Understand image formation and the role human visual system plays in
perception of gray and color image data.
2. Get broad exposure to and understanding of various applications of image
processing in industry, medicine, and defense.
3. Learn the signal processing algorithms and techniques in image enhancement
and image restoration.
4. Acquire an appreciation for the image processing issues and techniques and be
able to apply these techniques to real world problems.
5. Be able to conduct independent study and analysis of image processing
problems and techniques.
Topics covered:
1. Introduction Digital Image Processing (1 week)
2. Image Fundamentals and Human Visual Perception (1 week)
3. Image Enhancement in Spatial Domain (2 weeks)
4. Image Transforms (1 week)
5. Image Enhancement in Frequency Domain (1.5 week)
6. Image Restoration (2 week)
7. Image Morphology – Introductory (1 week)
8. Color Image Processing – Introductory (1 week)
9. Image Compression and Coding (2.5 week)
10. Wavelet Transforms – Introductory (1 week)
11. Student Presentations, Reviews, Examinations, and Final (3 weeks)
Class/laboratory schedule:
Three 50-minute lectures per week are typical. A host of image processing algorithms
are demonstrated using available mathematical tools (particularly MATLAB) during
the class and students are expected to use these tools to learn and apply these tools for
computer assignment and exercises.
Contribution of course to meeting the professional component:
Digital image processing builds upon the tools and techniques learned by students in
linear systems and probability courses. The course provides a mathematical basis for
further study and research in image and video processing, computer vision, and image
understanding. The students are required to use available mathematical tools
(particularly MATLAB) and implement various image processing algorithms and
evaluate their results. The homework is designed to reinforce fundamental theory and
techniques. Class projects are aimed at providing practical insight into the
implementation and constraints of real world image processing problems. The
students are asked to write/present a survey technical paper on a subject of their
choice related to image processing. Students present selected survey papers to the
class as a short seminar in the last week of the class. The students are also required to
do an individual term project on a topic of their interest related to image processing.
Relationship of course learning outcomes to ECE program outcomes:
ECE
Outcome
Course Outcomes
1
2
3
4
a
b
c
S
W
d
e
W
S
S
M
S
S
M
S
M
5
M
M
S
S
f
M
g
S
h
i
j
M
M
S
M
M
M
M
M
k
W
W
S
W
S
l
M
M
M
M
Comments
A balanced approach is taken in introducing mathematical
tools and concepts and engineering intuition is stressed.
Students are introduced to multidisciplinary applications of
image processing techniques particularly in industrial
automation, medicine and defense.
Students are encouraged towards life-long learning and
scholarship through self-guided research for survey paper
and term project.
Requirement of the course to write survey paper and term
project foster ethical and professional judgment in students.
Selected students are encouraged to present their project to
class. All students are required to write a project report.
Use of available computer tools (particularly MATLAB) is
promoted in learning the subject matter
S – strong connection; M – medium connection; W – weak connection
Prepared by: Sanjeev Agarwal
Date: February 1, 2008