Fall 2011 Advanced Elective
(EENG 4010)-Section 001
Instructor:
Parthasarathy
(Partha) Guturu
Faculty Office: NTRP
B-235
Phone: 940-891-6877
Email: guturu@unt.edu
Teaching Assistant: TBD
Class Hours:
M/W 3:30 PM - 4:50 PM
Class Room: NTRP
B-217.
Office Hours:
T 2:00 PM-3:30 PM. Students unable to see me during this time may request an
appointment.
Prerequisite: Senior
standing
Course
Description
This advanced elective course is designed to introduce to the senior undergraduate students 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.
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 |
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
undergraduate students will have a totally different set of examinations with
emphasis on problem solving rather than theory and algorithms, vis-a-vis the graduate students. 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 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 and Reporting
Relationship
of the course to program outcomes
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CLO |
Program Outcomes/ABET Outcomes |
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PO-1/
3(a) |
PO-2/
3(b) |
PO-3/
3I |
PO-4/
3(d) |
PO-5/
3(e) |
PO-6/
3(f) |
PO-7/
3(g) |
PO-8/
3(h) |
PO-9/
3(i) |
PO-10/
3(j) |
PO-11/
3(k) |
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