Fall 2019
Joint Graduate
and Undergraduate Course in Computer Vision (EENG 5640-001 & 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: TBD
Class Hours:
M/W 4:00 PM – 5:20 PM
Class Room: NTDP
B-227.
Office Hours:
Th. 11:30AM-1:00 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.
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:
50, Project: 30, and Final Examination: 20. Grades A, B, C, D, and F will be
assigned, respectively, depending upon whether the total tally will be greater
than/equal to 90, 80-89, 70-79, 60-69, or less than 60.
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 Faculty of Electrical
Engineering including this instructor cooperates with the Office of Disability
Accommodation (ODA) to make reasonable accommodations for students with
certified disabilities (cf. Americans with Disabilities Act and Section 504,
Rehabilitation Act). If you have not registered with ODA, we encourage you to
do so immediately and present a written accommodation request along with an
appropriate documentation from the Dean of Students Office http://www.unt.edu/oda/, on or before the 2nd
week of class.
Final Exam Date and Time: TBD.
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 |
|
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|
|
|
|
|
5 |
x |
|
|
|
|
|
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|
6 |
x |
|
|
|
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|
7 |
x |
|
|
|
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|
8 |
x |
|
|
|
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|
9 |
x |
|
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|
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|
10 |
x |
|
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|||
|
11 |
|
x |
x |
|
x |
|
|