Fall 2017

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: Jyothis Joseph
Class Hours: M/W 2:30 PM – 3:50 PM
Class Room: NTDP B-217.
Office Hours:  T 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.

PowerPoint Slides

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.

 

Student Outcomes (SOs)of Our BSEE Program

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

[SO-1] An ability to identify, formulate, and solve complex engineering problems by applying principles of engineering, science, and mathematics.

 

[SO-2] An ability to apply the engineering design process to produce solutions that meet specified needs with consideration for public health and safety, and global, cultural, social, environmental, economic, and other factors as appropriate to the Electrical Engineering discipline.

 

[SO-3] An ability to develop and conduct appropriate experimentation, analyze and interpret data, and use engineering judgment to draw conclusions.

 

[SO-4] An ability to communicate effectively with a range of audiences.

[SO-5] An ability to 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-6] An ability to recognize the ongoing need to acquire new knowledge, to choose appropriate learning strategies, and to apply this knowledge.

[SO-7] An ability to function effectively as a member or leader of a team that establishes goals, plans tasks, meets deadlines, and creates a collaborative and inclusive environment.

ABET Criterion 3 Outcomes

[1].                        An ability to identify, formulate, and solve complex engineering problems by applying principles of engineering, science, and mathematics.

[2].                        An ability to apply the engineering design process to produce solutions that meet specified needs with consideration for public health and safety, and global, cultural, social, environmental, economic, and other factors as appropriate to the discipline.

[3].                        An ability to develop and conduct appropriate experimentation, analyze and interpret data, and use engineering judgment to draw conclusions.

[4].                        An ability to communicate effectively with a range of audiences.

[5].                        An ability to 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.

[6].                        An ability to recognize the ongoing need to acquire new knowledge, to choose appropriate learning strategies, and to apply this knowledge.

[7].                        An ability to function effectively as a member or leader of a team that establishes goals, plans tasks, meets deadlines, and creates a collaborative and inclusive environment.

 

 

Relationship between the Course Learning Outcomes and Student/ABET Outcomes

The course learning outcomes map onto the program 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