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