E-posta: edirik@uludag.edu.tr Tel: (224) 294 0655 Adres: Elektronik Mühendisliği Bölümü 4. Kat, No:425
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Website:
http://home.uludag.edu.tr/~edirik
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Objective of the Course:
The main objectives of the course are as follows:
To provide essential knowledge of computer vision and pattern recognition fundamentals.
To develop advanced skills and competency in computer vision and pattern recognition discipline.
To apply these skills to the full spectrum of computer vision and pattern recognition problems, through independent research and investigation.
To develop the students' transferable skills including communication (oral, written and aural), time and project management.
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Contribution of the Course to Professional Development
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Learning Outcomes:
1
Gain sufficient knowledge on computer vision and pattern recognition field; the ability to model and solve computer vision and pattern recognition problems using theoretical and practical knowledge.;
2
Gain the ability to identify, model, and solve complex computer vision and pattern recognition problems; the ability to select and apply appropriate analysis and modeling methods for these problems.;
3
Gain the ability to design partly or fully a complex computer vision and pattern recognition system, process, device or a product meeting specific requirements under realistic constraints and conditions; the ability to apply modern design methods in this context.;
4
Gain the ability to develop, select, and use modern techniques and tools necessary for computer vision and pattern recognition applications; the ability to use information technologies in an efficient way.;
5
Gain the ability to design and conduct complex experiments and to collect, analyze and interpret data for computer vision and pattern recognition problems;
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Course Content:
Week
Theoretical
Practical
1
Projection geometry and perspective, mathematical fundamentals
2
Geometric transformations, Affine transform and image processing application
3
Curve and surface definition
4
Edge definition and contour extraction
5
2D digital filters and edge detection
6
Segmentation, lighting and shadows
7
Classification and recognition
8
Deterministic and statistical learning, multi dimensional probability distribution functions
9
MIDTERM EXAM and Course review
10
Supervised and unsupervised learning
11
Bayes, Maximumlikelihood learning methods and algorithms
12
Statistical error analysis
13
k-nn (kth nearest neighboor) learning and classification
1. Algorithms for Image Processing and Computer Vision , J. R. Parker , McGraw Hill, 2002 2. Handbook of Pattern Recognition & Computer Vision ,C. H. Chen (Editor), L. F. Pau (Editor), Patrick S. P. Wang (Editor) , Prentice Hall ,2001 3.Pattern Recognition and Machine Learning (Information Science and Statistics), Christopher M. Bishop Pentice Hall, 2007)
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Assesment
TERM LEARNING ACTIVITIES
NUMBER
PERCENT
Midterm Exam
1
30
Quiz
0
0
Homeworks, Performances
1
20
Final Exam
1
50
Total
3
100
Contribution of Term (Year) Learning Activities to Success Grade
50
Contribution of Final Exam to Success Grade
50
Total
100
Measurement and Evaluation Techniques Used in the Course
Information
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ECTS / WORK LOAD TABLE
Activites
NUMBER
TIME [Hour]
Total WorkLoad [Hour]
Theoretical
14
3
42
Practicals/Labs
0
0
0
Self Study and Preparation
14
4
56
Homeworks, Performances
1
5
5
Projects
1
20
20
Field Studies
0
0
0
Midtermexams
1
15
15
Others
0
0
0
Final Exams
1
27
27
Total WorkLoad
165
Total workload/ 30 hr
5,5
ECTS Credit of the Course
5,5
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CONTRIBUTION OF LEARNING OUTCOMES TO PROGRAMME QUALIFICATIONS
