UCC/UGC/ECCC
Proposal for New Course
Please attach proposed Syllabus in approved university format.
1. Course subject and number: EE 444
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
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
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.
Effective Fall 2012
Fall 2013
8. Long course title: COMUPUTER VISION
(max 100 characters including spaces)
9. Short course title: COMUPUTER 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 544. 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 significantly duplicate material in existing courses, It does co-convene
with the proposed EE 544 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 foundational overlap of approximately
15% is necessary for success in both classes.
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 544
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?
16b. If yes, may course be repeated for additional units in the same term?
18. Prerequisites:
EE 348 with grade C or better.
If prerequisites, include the rationale for the prerequisites.
Effective Fall 2012
Yes
No
Yes
No
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
02/15/2013
Date
Approvals:
2/14/2013
Department Chair/Unit Head (if appropriate)
Date
Chair of college curriculum committee
Date
Dean of college
Date
For Committee use only:
Effective Fall 2012
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
College of Engineering, Forestry & Natural Sciences
Department of Electrical Engineering & Computer Science
COURSE SYLLABUS: EE 444 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 544. 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
Effective Fall 2012
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 Textbook:
Shapiro and Stockman, “Computer Vision”, 2001, Prentice Hall, ISBN 0-13-030796-3.
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 paper, and a
comprehensive final exam..
Grading System and Assessment Timing
Exam 1
100 points
Exam 2
100 points
Final exam
150 points
Homework
100 points
Term Paper
75 points
Participation
25 points
Total
550 points
Effective Fall 2012
approximately week 6
approximately week 11
approximately once per week
includes presentation
attendance and active classroom participation
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
 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