UCC/UGC/ECCC
Proposal for New Course
Please attach proposed Syllabus in approved university format.
1. Course subject and number: EE 544
2. Units:
See upper and lower division undergraduate course definitions.
3. College:
CEFNS
4. Academic Unit:
3
Electrical Engineering
and Computer Science
5. Student Learning Outcomes of the new course. (Resources & Examples for Developing Course Learning
Outcomes)
At the completion of this course, students will understand:
 the basics of digital images (spatial resolution, dynamic range, color, etc.)
 binary image analysis
 elementary Bayesian and non-Bayesian pattern recognition techniques
 filtering and convolution of images
 correlation, normalized cross-correlation
 edge detection methods
 insights from biological vision and human visual perception
 extraction of color and texture information
 motion detection
 image segmentation techniques
 2-D matching techniques
 3-D matching techniques
Students will also develop their abilities to read and gain understanding from current
computer vision literature and develop deeper understanding of the above topics in order to
help students in the undergraduate section with their in-class exercises, provide them
constructive feedback on their term papers, and learn how to teach them via a tutorial and
software demonstration.
6. Justification for new course, including how the course contributes to degree program outcomes,
or other university requirements / student learning outcomes. (Resources, Examples & Tools for Developing
Effective Program Student Learning Outcomes).
The field of Computer Vision is a rapidly growing specialty in Electrical and Computer
Engineering. Today’s students are already encountering some computer vision applications in
their daily lives and this will only increase. Examples include face recognition technology and
biometric technology used in security applications, gesture recognition used in some newer
video game systems, and target recognition and surveillance used in military drone aircraft.
The study of computer vision draws upon such diverse areas as biological vision, computer
Effective Fall 2012
science, signal and image processing, physics, and psychology. It is important to prepare
students for careers in this dynamic and emerging area.
7. Effective BEGINNING of what term and year?
See effective dates calendar.
Fall 2013
8. Long course title: COMPUTER VISION
(max 100 characters including spaces)
9. Short course title: COMPUTER VISION
(max. 30 characters including spaces)
10. Catalog course description (max. 60 words, excluding requisites):
Theory and practicality of autonomous interpretation of digital images by computer. Builds
upon concepts from mathematics, signal and image processing, artificial intelligence, and
biological vision. Co convenes with EE 444. Letter grade only.
11. Will this course be part of any plan (major, minor or certificate) or sub plan (emphasis)?
Yes
If yes, include the appropriate plan proposal.
No
12. Does this course duplicate content of existing courses?
Yes
No
If yes, list the courses with duplicate material. If the duplication is greater than 20%, explain why
NAU should establish this course.
This course does not duplicate material in existing courses, It does co-convene with the
proposed EE 444 Computer Vision course. Additionally, it will cover the basic material of
pattern recognition and classification that is explored in much greater depth in the proposed
EE 443/543 Pattern Recognition course. This overlap of approximately 15% is necessary
because the EE 444/544 and EE 443/543 courses will be independent electives.
13. Will this course impact any other academic unit’s enrollment or plan(s)?
If yes, include a letter of response from each impacted academic unit.
14. Grading option:
Letter grade
Yes
Pass/Fail
No
Both
15. Co-convened with:
EE 444
14a. UGC approval date*:
(For example: ESE 450 and ESE 550) See co-convening policy.
*Must be approved by UGC before UCC submission, and both course syllabi must be presented.
16. Cross-listed with:
(For example: ES 450 and DIS 450) See cross listing policy.
Please submit a single cross-listed syllabus that will be used for all cross-listed courses.
17. May course be repeated for additional units?
16a. If yes, maximum units allowed?
Effective Fall 2012
Yes
No
16b. If yes, may course be repeated for additional units in the same term?
Yes
No
18. Prerequisites:
EE 348 with grade C or better.
If prerequisites, include the rationale for the prerequisites.
The prerequisite of EE 348 provides the necessary background in linear systems, discrete-time
signals, convolution, filtering, and frequency domain processing to form a good basis for
exploring the computer vision topic. It also provides an important level of mathematical maturity
that is vital to understanding the material in this course. Finally, EE 348 requires a background
and facility with computer programming, a skill that is at the foundation of this course.
19. Co requisites:
If co requisites, include the rationale for the co requisites.
20. Does this course include combined lecture and lab components?
Yes
If yes, include the units specific to each component in the course description above.
21. Names of the current faculty qualified to teach this course:
No
Dr. Phillip Mlsna, David Scott
Answer 22-23 for UCC/ECCC only:
22. Is this course being proposed for Liberal Studies designation?
If yes, include a Liberal Studies proposal and syllabus with this proposal.
Yes
No
23. Is this course being proposed for Diversity designation?
If yes, include a Diversity proposal and syllabus with this proposal.
Yes
No
FLAGSTAFF MOUNTAIN CAMPUS
Scott Galland
Reviewed by Curriculum Process Associate
03/21/2013
Date
Approvals:
2-14-2013
Department Chair/Unit Head (if appropriate)
Date
Chair of college curriculum committee
Date
Effective Fall 2012
Dean of college
Date
For Committee use only:
UCC/UGC Approval
Date
Approved as submitted:
Yes
No
Approved as modified:
Yes
No
EXTENDED CAMPUSES
Reviewed by Curriculum Process Associate
Date
Approvals:
Academic Unit Head
Date
Division Curriculum Committee (Yuma, Yavapai, or Personal Learning)
Date
Division Administrator in Extended Campuses (Yuma, Yavapai, or Personal
Learning)
Date
Faculty Chair of Extended Campuses Curriculum Committee (Yuma, Yavapai, or
Personal Learning)
Date
Chief Academic Officer; Extended Offices (or Designee)
Date
Approved as submitted:
Yes
No
Approved as modified:
Yes
No
Effective Fall 2012
Department of Electrical Engineering & Computer Science
COURSE SYLLABUS: EE 544 COMPUTER VISION
General Information:
Sequence number:
Class times:
3.0 credit hours. There is no laboratory component to this course.
Instructor: Dr. Phillip Mlsna, Associate Professor of Electrical Engineering
Office: Engineering room 257, 523-2112, phillip.mlsna@nau.edu
Office hours as posted (office door and BlackboardLearn)
Official course webpages are on BlackboardLearn: http://bblearn.nau.edu
Course Prerequisite:
EE 348 (Signals and Systems) with grade C or better.
You are also expected to have good programming skills in both Matlab and C.
Course Description (from catalog) :
Theory and practicality of autonomous interpretation of digital images by computer. Builds upon
concepts from mathematics, signal and image processing, artificial intelligence, and biological vision.
Co convenes with EE 444. Letter grade only.
Student Learning Expectations/Outcomes for this Course
At the completion of this course, students will understand:
 the basics of digital images (spatial resolution, dynamic range, color, etc.)
 binary image analysis
 elementary Bayesian and non-Bayesian pattern recognition techniques
 filtering and convolution of images
 correlation, normalized cross-correlation
 edge detection methods
 insights from biological vision and human visual perception
 extraction of color and texture information
 motion detection
 image segmentation techniques
 2-D matching techniques
 3-D matching techniques
Students will also develop their abilities to read and gain understanding from current
computer vision literature and develop deeper understanding of the above topics in order to
help students in the undergraduate section with their in class exercises, provide them
Effective Fall 2012
constructive feedback on their term papers, and learn how to teach them via a tutorial and
software demonstration.
Course Structure/Approach:
We will be following the textbook rather closely most of the time, with the topic order as shown in the
“Course Outline” section below. The format will largely be lecture and discussion. The textbook
readings are especially important. There will often be important material in the text that we will not
have time to cover in class.
Required Materials:
Shapiro and Stockman, “Computer Vision”, 2001, Prentice Hall, ISBN 0-13-030796-3.
Current journal articles or recent conference papers to be selected by the students and
approved by the instructor for extra homework for those taking the graduate section.
Recommended optional materials/references:
Sonka, Hlavac, and Boyle, “Image Processing, Analysis, and Machine Vision”, PWS Publishing,
1999, ISBN 0-534-95393-X.
Bovik, “The Essential Guide to Image Processing,” Academic Press, 2 nd ed., 2009, ISBN 978-0-12374457-9.
Trucco and Verri, “Introductory Techniques for 3-D Computer Vision,” Prentice Hall, 1998, ISBN 0-13261108-2.
Course Outline:
Week 1
Week 2
Week 3
Week 4
Week 5
Week 6
Week 7
Week 8
Week 9
Week 10
Week 11
Week 12
Week 13
Week 14
Week 15
Week 16
Overview, digital images
Chapters 1, 2
Binary image analysis
Chapter 3
Pattern recognition
Chapters 3, 4
Pattern recognition, image enhancement
Chapters 4, 5
Image filtering, convolution
Chapter 5
Exam 1
Correlation, edge detection
Chapter 5
Color and texture
Chapters 6, 7
Image retrieval, motion
Chapters 8, 9
Segmentation
Chapter 6
Exam 2
2-D matching, 3-D from 2-D
Chapters 11, 12
3-D sensing and computation
Chapter 13
3-D matching, project presentations
Chapter 14
Project & term paper presentations, review lectures
Final Exam
Assessment of Student Learning Outcomes:
Assessment will be based on two mid-term exams, homework, participation, a term project, and a
comprehensive final exam. Three article reports will require the selection, reading, and
comprehension of materials from the recent research literature in computer vision. Students in EE
544 will also help guide the EE 444 students with in-class exercises and with their EE 444 term
papers. EE 544 students will each review several EE 444 draft term papers and provide constructive
feedback.
Effective Fall 2012
Term Project:
A semester project involving a deeper investigation into a relevant topic and demonstration of
software is required. Students will work individually on the term project. The student will present a
background tutorial to the class along with his/her project results and a demonstration. In this way,
graduate students will provide some educational benefit to the undergraduates in EE 444.
Grading System and Assessment Timing:
Exam 1
125 points
Exam 2
125 points
Final exam
200 points
Homework
120 points
Term Project
100 points
CV Journal Article Reports 30 points
Participation
25 points
Leadership
25 points
Total
750 points
approximately week 6 (25 points of unique or
additional problems for graduate section)
approximately week 11(25 points of unique or
additional problems for graduate section)
comprehensive (50 points of unique or
additional problems for graduate section)
approximately once per week (20 points of unique or
additional assignments for graduate section)
semester project instead of a term paper (advanced
material and classroom teaching/demonstration
for 25 more points for those in the graduate section)
three written reports for those in the graduate section
attendance and active classroom participation
leadership activities for those in the graduate section
Course Policies:
 Late Work
Assignments are due when specified and can be submitted on BBLearn (preferred)
or on paper at the beginning of the class period. Late work will be accepted
electronically only (on BBLearn, not by e-mailing the professor!) up to 24 hours late for a
20% penalty, and not accepted after 24 hours late.
 Retests and Makeup Tests
No makeup exams will be given except by prior arrangement in exceptional or
emergency situations at the discretion of the instructor. Please contact me immediately
if such a situation arises. (Procrastination is not an emergency.)
 Attendance
Attendance is required and will be recorded on a random basis. Attendance data will be
included in the participation portion of your grade.
 Academic Dishonesty
Cheating and plagiarism are strictly prohibited. Incidents of cheating or plagiarism are
treated quite seriously. The NAU policy on academic dishonesty in Appendix G of the
current Student Handbook applies. All work you submit for grading must be your own.
http://home.nau.edu/studentlife/handbook/appendix_g.asp
You are encouraged to discuss the intellectual aspects of homework assignments with
other class participants. However, each student is responsible for formulating solutions
in his or her own words.
University policies:
 Safe Working and Learning Environment
 Students with Disabilities
 Institutional Review Board
Effective Fall 2012



Academic Integrity
Academic Contact Hour
Sensitive Course Material
See the following document for these policy statements:
http://www4.nau.edu/avpaa/UCCPolicy/plcystmt.html.
Effective Fall 2012