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Key Words
Digital image processing, computer
vision, digital image filtering,
image compression.
Objectives
To
learn basic image processing
processes for computer vision deals
with the processing of image data
for use by a computer and to
understand to the major applications
areas of computer vision and image
processing: image analysis, image
restoration, image enhancement, and
image compression.
Contents
1.
Introduction to image
processing, 2.
Imaging geometry, 3.
Preprocessing, 4.
Edge/Line detection, 5.
Segmentation, 6.
Discrete transform (Fourier,
Cosine, Walsh-Hadamard, Wavelet,
etc. )7.
Feature extraction and
Analysis,
8.
Image Restoration, 9.
Image Enhancement, 10.
Image Compression.
Course Material
“Computer Vision &Imp; Image
Processing”, Scott E. Umbaugh,
Prentice Hall, 1998.
Lecture Notes (HIPR)
References
1)
Sonka, Hlavac, Boyle, “Image
Processing, Analysis, and Machine
Vision”, An International Thomson
Publishing Company, 1999,
2)
R. C. Gonzales, R. E. Woods.
“Digital Image Processing”,
Addison-Wesley Publishing Company,
1992,
3)
Bernd Jahne, “Digital Image
Processing” Springer, 1997
4)
M. Ghanbari,“ Video Coding,
an Introduction to standart
codecs“, The Institution of
Electrical Engineering, 1999.
5)
A. Drozdek,” Elements of Data
Compression”, Brooks/cole Thomson
Learning, 2002.
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
None
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