EE576

EE 576 – Machine Vision Spring 2017  Course Information

The aim of this course is to provide an overview of machine vision. This 1-semester course covers the fundamentals of the following topics:

Images, extraction of low-level features, boundary and region based analysis, segmentation and grouping, lightness and color, shape from shading. photometric and binocular stereo, optical flow and motion estimation, tracking, texture analysis.

 

Class

Lectures: Mondays 11-12 @ Fourier, Wednesdays 9-11 Fourier

Instructor: Prof. H. Isil Bozma

Teaching Assistant: Mehmet Yamaç

Prerequisites : C or C++, Image Processing, Working knowledge of Matlab & Simulink,

Textbooks

There is no specific text book specified for the course. The recommended reading for this course includes:

  • Image Processing, Analysis, and Machine Vision by Sonka, Hlavac, and Boyle (ITP, 1999) MPSL 006.37 1999 IMG. · Introductory Techniques for 3-D Computer Vision by Emanuele Trucco and Alessandro Verri (Prentice Hall 1998) MPSL 006.37 1998 INT
  • 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). MPSL 006.42 1995 MAC
  • Digital Image Processing by Rafael C Gonzalez and Richard E Woods (Addison Wesley, 1992) MPSL 621.367 1992 DIG.
  • Robot Vision by Berthold Klaus Paul Horn (MIT Press, 1986) MPSL 629.892 1986 ROB.

Grading

Grading will be based on  class participation and performance,  projects , one midterm and a final. The weights of each will be roughly.

Class participation 10%

Projects 45%

Midterm 17%

Final 28%

The following are required to be able to take the final exam:

Class participation of at least 70%

Projects – All projects must be turned in.

 

Syllabus – 

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.

Tentative Schedule

    • Scene  recognition:
  • Week 9 (3 – 7 April)

 

  • Week 10 ( 10 – 14 April)
    • Motion Field and Optical Flow
  • Week 11 ( 17 – 21 April)
  • Spring Break

 

  • Week 12 (24- 28 April)
  • Week 13 (1 – 5 May)
  • Week 14 (8 – 12  May)
    • Photometric Stereo
      • Lecture notes:
      • Lecture presentation: Photometric stereoProjects
      • You will be required to do five projects for the course. For each project, you should do some paper and book search. You will be required to give a 5 minute demo presentation for each project along with a CD that contains your fully documented source code and a readme file, the project report and the PPT presentation.You are expected to do some literature survey (papers, etc. not just web sites!) and relate what you are doing to the work described therein.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· You must present a conclusion regarding your workThe grading of the projects will be done as follows:
Explanation Points
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. 15 pts
Programming discipline (indentations and ease of following code) 5 pts
Code ( Content of code, creativity, etc.) 45 pts
Total 100 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.