UCC/UGC/ECCCProposal for New Course

Please attach proposed Syllabus in approved university format.

1. Course subject and number: EE 544 2. Units: 3 See upper and lower division undergraduate course definitions.

3. College: CEFNS 4. Academic Unit: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

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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? Fall 2013 See effective dates calendar.

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 No If yes, include the appropriate plan proposal.

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)?              Yes No       If yes, include a letter of response from each impacted academic unit.

14. Grading option:      Letter grade                     Pass/Fail                        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? Yes    No 16a. If yes, maximum units allowed?Effective Fall 2012

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 No 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: Dr. Phillip Mlsna, David Scott

Answer 22-23 for UCC/ECCC only:

22. Is this course being proposed for Liberal Studies designation?             Yes No         If yes, include a Liberal Studies proposal and syllabus with this proposal.

23. Is this course being proposed for Diversity designation?                                    Yes  No        If yes, include a Diversity proposal and syllabus with this proposal.

FLAGSTAFF MOUNTAIN CAMPUS

Scott Galland 03/21/2013Reviewed by Curriculum Process Associate 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 EngineeringOffice: Engineering room 257, 523-2112, phillip.mlsna@nau.eduOffice 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 CourseAt 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 techniquesStudents 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, 2nd ed., 2009, ISBN 978-0-12-374457-9.

Trucco and Verri, “Introductory Techniques for 3-D Computer Vision,” Prentice Hall, 1998, ISBN 0-13-261108-2.

Course Outline:Week 1 Overview, digital images Chapters 1, 2Week 2 Binary image analysis Chapter 3Week 3 Pattern recognition Chapters 3, 4Week 4 Pattern recognition, image enhancement Chapters 4, 5Week 5 Image filtering, convolution Chapter 5Week 6 Exam 1Week 7 Correlation, edge detection Chapter 5Week 8 Color and texture Chapters 6, 7Week 9 Image retrieval, motion Chapters 8, 9Week 10 Segmentation Chapter 6Week 11 Exam 2Week 12 2-D matching, 3-D from 2-D Chapters 11, 12Week 13 3-D sensing and computation Chapter 13Week 14 3-D matching, project presentations Chapter 14Week 15 Project & term paper presentations, review lecturesWeek 16 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.

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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 approximately week 6 (25 points of unique or

additional problems for graduate section)Exam 2 125 points approximately week 11(25 points of unique or

additional problems for graduate section)Final exam 200 points comprehensive (50 points of unique or

additional problems for graduate section)Homework 120 points approximately once per week (20 points of unique or

additional assignments for graduate section)Term Project 100 points semester project instead of a term paper (advanced

material and classroom teaching/demonstration for 25 more points for those in the graduate section)

CV Journal Article Reports 30 points three written reports for those in the graduate sectionParticipation 25 points attendance and active classroom participationLeadership 25 points leadership activities for those in the graduate sectionTotal 750 points

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 TestsNo 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.)

AttendanceAttendance is required and will be recorded on a random basis. Attendance data will be included in the participation portion of your grade.

Academic DishonestyCheating 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