Fall 2016
Joint Graduate
and Undergraduate Course in Computer Vision (EENG 5640-001 & EENG 4010-001)
_______________________________________________________________________________________
Instructor: Parthasarathy
(Partha) Guturu
Faculty Office: NTRP
B-235
Phone: 940-891-6877
Email: guturu@unt.edu
Teaching Assistant: Srijita
Mukherjee
Class Hours:
M/W 2:30 PM – 4:50 PM
Class Room: NTRP
B-217.
Office Hours:
T 11:00AM-12:30 PM. Students unable to see me during this time may request
an appointment.
Prerequisite: 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 totally different set of examinations with
emphasis on theory and algorithms, rather than problem solving, vis-a-vis the undegraduate 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 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 and Reporting
Our EE Student Outcomes (SOs)
Upon completion of our BSEE program, the
students will be able to:
[SO-1] Apply knowledge of mathematics,
engineering and science.
[SO-2] Design and conduct experiments to verify
and validate the design projects developed by them, and analyze and interpret
data.
[SO-3] Develop project-based learning skills
through design and implementation of a system, component, or process that meets
the needs within realistic constraints.
[SO-4] Function on multidisciplinary
teams.
[SO-5] Identify, formulate, and solve
engineering problems.
[SO-6] Have an understanding of
professional and ethical responsibility.
[SO-7] Communicate
effectively.
[SO-8] Achieve broad education
necessary to understand the impact of electrical engineering solutions in a
global and societal context.
[SO-9] Understand
learning processes, concepts of learning to learn, and engage in lifelong
learning.
[SO-10]
Achieve knowledge of contemporary issues.
[SO-11] Use
techniques, skills, and computer-based tools for conducting experiments and
carrying out designs.
ABET Outcomes
3a- ability to
apply knowledge of mathematics, science, and engineering
3b- ability to
design and conduct experiments, as well as to analyze and interpret data
3c- ability to
design a system, component, or process to meet desired needs
3d- ability to
function on multi-disciplinary teams
3e-ability to
identify, formulate, and solve engineering problems
3f-
understanding of professional and ethical responsibility
3g- ability to
communicate effectively
3h- the broad
education necessary to understand the impact of engineering solutions in a
global and societal context
3i- recognition
of the need for, and an ability to engage in life-long learning
3j- knowledge
of contemporary issues
3k- ability to
use the techniques, skills, and modern engineering tools necessary for
engineering practice
Relationship of the course to program
outcomes
|
CLO |
Student
Outcomes/ABET Outcomes |
||||||||||
|
|
SO-1/ 3(a) |
SO-2/ 3(b) |
SO-3/ 3(c) |
SO-4/ 3(d) |
SO-5/ 3(e) |
SO-6/ 3(f) |
SO-7/ 3(g) |
SO-8/ 3(h) |
SO-9/ 3(i) |
SO-10/ 3(j) |
SO-11/ 3(k) |
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