Key Words

 Image processing, computer vision, image segmentation, shape recognition, motion analysis.  

Objectives

The principal objectives of this course continue to be to provide an introduction to basic concepts and methodologies for computer vision, and to develop a foundation that can be used as the basis for further study and research in this field. 

Contents

1.      Introduction to computer vision, 2.      Image Segmentation 3.      Texture, Classification, Matching4.      Shape Representation and Description5.      Object Recognition,6.      Image Understanding,7.      3D Vision, geometry, Use of 3D Vision 8.      Mathematical Morphology, 9.      Motion Anaalysis, 10.  Knowledge-Based Vision 

Course Material

   1)   Sonka, Hlavac, Boyle, “Image Processing, Analysis, and Machine Vision”, An International Thomson Publishing Company, 1999,

 2)  Lecturer praticles exercises, and slayts.

References

1)      R. C. Gonzales, R. E. Woods. “Digital Image Processing”, Addison-Wesley Publishing Company, 1992,

2)      Bernd Jahne, “Digital Image Processing” Springer, 1997,

3)      R. M. Haralick, L. G. Shapiro, “Computer and Robot Vision”, Vol. I-II, Addision-Wesley Publishing Company, 1993.

4)      Scott E. Umbaugh, “Computer Vision & Image Processing”,  , Prentice Hall, 1998

5)      M. Ghanbari,“ Video Coding, an Introduction to standart codecs“,  The Institution of Electrical Engineering, 1999.

Teaching Methods

Lectures and PC-based practical exercises. 

Evaluation Methods

 One examinations in the course period, One semester project and one examination at the end of the course 

Examination Methods

 Written  

Prerequisites

 SEC401 Image Processing