Course

code	CC516
credit_hours	3
title	Image Processing & Pattern Recognition
arbic title
prequisites	CC416
credit hours	3
Description/Outcomes
arabic Description/Outcomes
objectives	In the field of pattern recognition the aim is to teach a computer to recognize patterns in data sets (e.g. input-output relations). Real data is often noisy, and therefore probabilistic methods are used. Using the Bayesian perspective is the starting point for a treatment of both classical methods (least mean squares methods, discriminant analysis) and modern methods (neural networks, Bayesian learning).
arabic objectives
ref. books	R. Gonzalez and R. Woods, "Digital Image Processing", Pearson Hall, Second Edition.
arabic ref. books
textbook	E. Gose, R. Johnsonbaugh, "Pattern Recognition and Image Analysis", Prentice Hall PTR.
arabic textbook
objective set
content set
course file	65_CC516_CC 516.pdf

Course Content

content serial	Description
1	Week Number 1 : Introduction to Pattern Recognition. .
2	Week Number 2 : Gray scale Transformations. .
3	Week Number 3 : Smoothing Transformations. .
4	Week Number 4 : Edge Detection. .
5	Week Number 5 : Scene Segmentation and labeling. .
6	Week Number 6 : Shape Detection. .
7	Week Number 7 : 7th week exam + Revision. .
8	Week Number 8 : Morphological Operations. .
9	Week Number 9 : Statistical Decision Making. .
10	Week Number 10 : Minimization of Classification Error. .
11	Week Number 11 : Hierarchical Clustering. .
12	Week Number 12 : 12th week exam + Revision. .
13	Week Number 13 : Partitioned Clustering. .
14	Week Number 14 : Feed Forward Neural Networks. .
15	Week Number 15 : Hopfield Networks. .
16	Week Number 16 : Presentation of projects and Final Exam. .