EE 576 – Machine Vision Spring 2020
The aim of this course is to introduce the students to the fundamental problems of machine vision and the approaches that have been proposed for solving these problems. The topics covered include the following: Images, image formation, light and image formation, color, visual processing, features, segmentation, shape descriptors, object recognition, scene recognition, optical flow, tracking, motion estimation, binocular stereo, photometric stereo.
Lectures: Mondays 11-12 @ Fourier, Wednesdays 9-11 Shannon
Instructor: Prof. H. Isil Bozma
Teaching Assistant: Serhat İşcan serhat_iscan at hotmail.com
Prerequisites : C or C++, Working knowledge of Matlab & Simulink, Image Processing (Advised, but not required)
There is no specific text book specified for the course. The recommended reading for this course includes:
- Robot Vision by Berthold Klaus Paul Horn (MIT Press, 1986)
- Machine Vision: Theory, Algorithms, Practicalities by E. R. Davies (Morgan Kaufmann Publications 2005) ·
- Machine Vision by Ramesh Jain, Rangachar Kasturi and Brian Schunk (McGraw Hill 1995).
- Introductory Techniques for 3-D Computer Vision by Emanuele Trucco and Alessandro Verri (Prentice Hall 1998)
Some additional references:
- Image Processing, Analysis, and Machine Vision by Sonka, Hlavac, and Boyle (ITP, 1999) .
- Digital Image Processing by Rafael C Gonzalez and Richard E Woods (Addison Wesley, 1992) MPSL 621.367 1992 DIG.
Grading will be based on projects, two midterms and a final project.
The weights of each will be roughly.
Projects 40%, Midterm 1 17%, Midterm 2 28%, Final project 15%
This part will be modified! I will announce the new weights. Pls be patient.
All projects must be turned and be completed to a passing degree in order to be eligible for taking the second midterm
Syllabus and Tentative Schedule
Pls note that I will be updating the notes as we progress throughout the semester. Pls make sure that you get the most updated notes.
- Week 2 (10-14 Feb)
- Lecture presentation: Intro
- Week 3 ( 17 – 21 Feb )
- Week 4 (24 -28 Feb )
- Week 5 ( 2 – 6 Mar)
- Week 7 (16 – 20 Mar -> 6 – 10 Apr)
- Week 8 ( 23 – 27 Mar -> 13 – 17 Apr)
- Week 9 -10 (31 Mar -3 Apr -> 20 Apr – 1 May ~ 2 weeks)
- Object recognition:
- Lecture presentation: Object Recognition
- hw 6, hw 7
- Object recognition:
- Week 11 (6- 10 April -> 4 – 8 May)
- Week 12 ( 13 – 17 Apr – > 11-15 May)
- Week 13-14 (11 – 13 May -> 18 – 29 May)
In your projects, BE SURE TO REFER TO ANY LITERATURE/EXTERNAL CODE you have examined or used.
A separate REFERENCE section must be added to the end of all your reports.
Again, I assume that the projects have not been or are currently being done for other courses, etc.· You must present simulation results done in a statistical manner·
Pls send your source code, ( if applicable) data and report in a rar file with a name containing your name and project number – ie. LastnameFirstnamePrX.rar both to me and the course TA Kadir Turksoy. The report must be handed in a printed form either to me or to the course TA.
You must present a conclusion regarding your work.
The grading of the projects will be done as follows:
|Checking and verification of execution of code||30 pts|
|Header of the code inc. author, course,project no, etc.||5 pts|
|Documentation –where every class and method must be commented including comments of statements themselves.||10pts|
|Programming discipline (indentations and ease of following code)||5 pts|
|Code ( Content of code, creativity, etc.)||50 pts|
OpenCV Intel’s OpenCV library, a very comprehensive open source vision library of C functions. A free source code version is available from SourceForge which you are recommended to download and install.