Fall 2022
Joint Graduate
and Undergraduate Course in Computer Vision (EENG 5640-001, EENG 5640-600 &
EENG 4010-001)
_______________________________________________________________________________________
Instructor: Parthasarathy
(Partha) Guturu
Faculty Office: DP
B-235
Phone: 940-891-6877
Email: Parthasarathy.Guturu@unt.edu or pg0028@unt.edu
Teaching Assistant:
Kandhimalla, Jesmitha
<JesmithaKandhimalla@my.unt.edu>
Class
Hours: M/W
4:00 PM – 5:20 PM
Class
Room:
NTDP E-264
Office Hours:
T 11:30AM-12:30 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 is not. 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.
Inclusive Learning Environment
and Support for Your Success
I value the many perspectives students bring to our campus. Please work with me to create a classroom culture of open communication, mutual respect, and inclusion. All discussions should be respectful and civil. Although disagreements and debates are encouraged, personal attacks are unacceptable. Together, we can ensure a safe and welcoming classroom for all. If you ever feel like this is not the case, please stop by my office and let me know. We are all learning together
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 |
|
|
|
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|
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|
10 |
x |
|
|
|
|||
|
11 |
|
x |
x |
|
x |
|
|