Fall 2026

Joint Graduate and Undergraduate Course in Computer Vision (EENG 5640-001, EENG 5640-600 & EENG 4850-001)

_______________________________________________________________________________________

Instructor: Parthasarathy (Partha) Guturu
Faculty Office: DP B-235
Phone: 940-891-6877
Email: Parthasarathy.Guturu@unt.edu or pg0028@unt.edu
Teaching Assistant: "K C, Sandip" <SandipKC@my.unt.edu>

Class Hours: M/W 3:25 PM – 4:45 PM
Classroom: NTDP E-266
Office Hours:  T 3:00 PM-4:00 PM. Students unable to see me during this time may request an appointment.
Prerequisite: Senior/Graduate Standing

Course Description: This course is designed to introduce to the graduate students advanced mathematical principles of computer vision. Binary image processing with techniques of mathematical morphology, grey level image processing with various filters, color fundamentals and texture representation and recognition will be discussed. Advanced topics such as content based image retrieval, shape form X-techniques, 2D/3D object recognition and matching will also be discussed. A programming project on one of the topics will be included in this course.

Reading Requirements: Students are required to come prepared to every class with the material discussed in the previous class.

Reference Book:
1. Computer Vision by Linda G Shapiro, and George Stockman Publisher: Prentice Hall; 1st edition (January 23, 2001) Language: English ISBN-10: 0130307963 ISBN-13: 978-0130307965.

Attendance Policy: In view of the continuous evaluation strategy adopted by the instructor, perfect attendance is recommended for those aspiring to get good grades. .  It is important that you communicate with the professor and the instructional team prior to being absent, so you, the professor, and the instructional team can discuss and mitigate the impact of the absence on your attainment of course learning goals.  Please inform the professor and instructional team if you are unable to attend class meetings because you are ill, in mindfulness of the health and safety of everyone in our community. If you are experiencing any symptoms of COVID (https://www.cdc.gov/coronavirus/2019-ncov/symptoms testing/symptoms.html) please seek medical attention from the Student Health and Wellness Center (940-565-2333 or askSHWC@unt.edu) or your health care provider PRIOR to coming to campus. UNT also requires you to contact the UNT COVID Team at COVID@unt.edu for guidance on actions to take due to symptoms, pending or positive test results, or potential exposure.

Grading Policy:  The graduate students will have a different set of examinations with less problem solving and more theory and algorithms whereas the focus of undergraduate examinations will be on problem solving.  The break-up for overall grading is as follows.

Assignments/Quizzes/Class Tests: 70, and Project: 30. Grades A, B, C, D, and F will be assigned, typically but not necessarily, depending upon whether the total tally will be greater than/equal to 90, 80-89, 70-79, 60-69, or less than 60, respectively.

Academic Dishonesty: Honesty is the best policy. Cheating will not be tolerated. Anyone found guilty of cheating on a test or assignment will be awarded an F grade for the course. Discussions of problems and assignment with your classmates is welcome and encouraged, however, sharing of solutions and generating solutions with AI tools like ChatGPT is not permitted. If you need help, you should ask the instructor. Cheating includes, but is not limited to, all forms of plagiarism and misrepresentation. For your rights and responsibilities please refer to http://www.unt.edu/csrr

Statement regarding Disabled Students: The University of North Texas makes reasonable academic accommodation for students with certified disabilities (cf. Americans with Disabilities Act and Section 504, Rehabilitation Act). Students seeking reasonable accommodation must first register with the Office of Disability Access (ODA) to verify their eligibility. If a disability is verified, the ODA will provide you with a reasonable accommodation letter to be delivered to faculty to begin a private discussion regarding your specific needs in a course. You may request reasonable accommodations at any time; however, ODA notices of reasonable accommodation should be provided as early as possible in the semester to avoid any delay in implementation. Note that students must obtain a new letter of reasonable accommodation for every semester and must meet with each faculty member prior to implementation in each class. Students are strongly encouraged to deliver letters of reasonable accommodation during faculty office hours or by appointment. Faculty members have the authority to ask students to discuss such letters during their designated office hours to protect the privacy of the student. For additional information, refer to the Office of Disability Access website (http://www.unt.edu/oda). You may also contact ODA by phone at (940) 565-4323.

Final Exam Date and Time: There will be no final exam.

Course Outline and Course Delivery Plan

Topic No.	Topic	Time Allocated
1.	Image Formation, Representation, basics of image processing, pattern recognition, and Computer Vision	1 Week
2.	Binary Image Analysis and Mathematical Morphology	1 Week
3.	Grey Level Image Processing- Filtering, Enhancement and Edge Detection	1 Week
4.	Image Segmentation and Representation	1 Week
5.	Color and Shading	1 Week
6.	Texture	1 Week
7.	Content-based   Image Retrieval	1 Week
8.	2D Matching	1 Week
9.	Shape from X ( = shading/texture/motion/stereo/boundary)	3 Weeks
10.	3D Object Recognition	1 Week

Course Learning Outcomes

Course Learning Outcomes (CLOs) for Advanced Topics in Electrical Engineering- Computer Vision (EENG-4010) are as follows:

[CLO-1] Basics of image processing, pattern recognition, and computer vision

[CLO-2] Binary Image Analysis and Mathematical Morphology

[CLO-3] Grey Level Image Processing- histogram methods, filters, edge detection

[CLO-4] Image segmentation, shape representation and recognition

[CLO-5] Color fundamentals

[CLO-6] Texture representation and recognition

[CLO-7] Content-based image retrieval

[CLO-8] 2D-Matching with affine transforms

[CLO-9] Shape from X-techniques (binocular/photometric stereo, motion, etc.)

[CLO-10] 3D-representations and object recognition with applications

[CLO-11] Computer Vision Project Design, Implementation, Testing, and Reporting.

Our EE Program Student Outcomes (SOs) (and ABET Criterion 3 Outcomes)

Upon completion of our BSEE program, the students will be able to:

[SO-1/ABET 3-1] identify, formulate, and solve complex engineering problems by applying principles of engineering, science, and mathematics,

[SO-2/ABET 3-2] apply engineering design to produce solutions that meet specified needs with consideration of public health, safety, and welfare, as well as global, cultural, social, environmental, and economic factors,

[SO-3/ABET 3-3] communicate effectively with a range of audiences,

[SO-4/ ABET 3-4] recognize ethical and professional responsibilities in engineering situations and make informed judgments, which must consider the impact of engineering solutions in global, economic, environmental, and societal contexts,

[SO-5/ABET 3-5] function effectively on a team whose members together provide leadership, create a collaborative and inclusive environment, establish goals, plan tasks, and meet objectives,

[SO-6/ABET 3-6] develop and conduct appropriate experimentation, analyze and interpret data, and use engineering judgment to draw conclusions, and

[SO-7/ABET 3-7] acquire and apply new knowledge as needed, using appropriate learning strategies.

Relationship between Our BSEE Program Student Outcomes and Course Learning Outcomes

The course learning outcomes map onto our program’s student outcomes and ABET outcomes as depicted in the table below:

CLO	Student/ABET Criterion 3 Outcomes
	SO-1/ 3[1]	SO-2/ 3 [2]	SO-3/ 3 [3]	SO-4/ 3 [4]	SO-5/ 3 [5]	SO-6/ 3 [6]	SO-7/ 3 [7]
1	x
2	x
3	x
4	x
5	x
6	x
7	x
8	x
9	x
10	x
11		x	x			x