EEE5550 Computer Vision and Pattern RecognitionBahçeşehir UniversityDegree Programs ELECTRICAL AND ELECTRONICS ENGINEERINGGeneral Information For StudentsDiploma SupplementErasmus Policy StatementNational QualificationsBologna Commission
ELECTRICAL AND ELECTRONICS ENGINEERING
Bachelor TR-NQF-HE: Level 6 QF-EHEA: First Cycle EQF-LLL: Level 6

Course Introduction and Application Information

Course Code Course Name Semester Theoretical Practical Credit ECTS
EEE5550 Computer Vision and Pattern Recognition Fall 3 0 3 12
This catalog is for information purposes. Course status is determined by the relevant department at the beginning of semester.

Basic information

Language of instruction: English
Type of course: Departmental Elective
Course Level: Bachelor’s Degree (First Cycle)
Mode of Delivery: Face to face
Course Coordinator : Dr. Öğr. Üyesi ZAFER İŞCAN
Recommended Optional Program Components: None
Course Objectives: The aim of the course is to study computer vision, which tries to “make computers see and interpret” using the observations in the form of multiple 2D (or 3D) images. In this undergraduate/graduate level course, the focus is on mainly basic computer vision concepts. The course will provide the participants with a background in Computer Vision both in practical aspects as being able to implement computer vision algorithms, and their mathematical understanding.

Learning Outcomes

The students who have succeeded in this course;
1.Discuss the main problems of computer vision, its uses and applications
2.Design various point-wise intensity transformations, neighborhood and spatial filtering operations over images
3.Transform images by geometric transforms: rigid, affine, and polynomial warping
4. Define and compute feature extraction operators: edges and corners, lines, and scale-invariant feature extraction in images
5. Describe mathematical image morphological operators
6. Construct segmentation algorithms: histogram-analysis-based thresholding, clustering intensities, and region growing
7. Discuss visual motion estimation methods
8. Apply object and shape recognition approaches to problems in computer vision
9. Describe basic multiple view geometry and stereo concepts

Course Content

Introduction to Computer Vision, Human Visual System, Pointwise Operations, Intensity Transformations, Histograms, Enhancement, Spatial Filtering, Neighborhood Operations, Edge Detection, Feature Extraction: Corners, Hough, Ellipse Fit, RANSAC, Correlation, SIFT, SURF, Morphological Image Processing, Segmentation: Adaptive Thresholding, Otsu, Region Growing, Active Contours, Segmentation: Adaptive Thresholding, Otsu, Region Growing, Active Contours, Introduction to Pattern Recognition, Review of Probability, Bayesian Decision Theory, Bayesian Estimation, PCA, kNN, SVM, Camera models, Camera Calibration, Stereopsis, Motion Estimation, Tracking

Weekly Detailed Course Contents

Week Subject Related Preparation
2) Spatial Filtering Neighborhood Operations Edge Detection
3) Feature Extraction: Corners, Hough, Ellipse Fit, RANSAC, Correlation
4) Scale Invariant Feature Transform (SIFT), Speeded Up Robust Features (SURF)
5) Morphological Image Processing
6) Segmentation: Adaptive Thresholding, Otsu, Region Growing, Active Contours
7) Review, midterm exam
8) Introduction to Pattern Recognition Review of Probability
9) Bayesian Decision Theory
10) Bayesian Estimation, PCA, kNN, SVM
11) Camera models, Camera Calibration
12) Stereopsis
13) Motion Estimation
14) Tracking

Sources

Course Notes / Textbooks: Computer Vision: A Modern Approach (2nd Edition), David A. Forsyth and Jean Ponce, Prentice Hall, 2011.

Digital Image Processing, R.C. Gonzalez, R.E. Woods, Pearson Prentice Hall 2008.


References: Computer Vision, D. Ballard and C.M. Brown, Prentice Hall, online at: http://homepages.inf.ed.ac.uk/rbf/BOOKS/BANDB/bandb.htm

Evaluation System

Semester Requirements Number of Activities Level of Contribution
Homework Assignments 4 % 10
Project 1 % 25
Midterms 1 % 25
Final 1 % 40
Total % 100
PERCENTAGE OF SEMESTER WORK % 35
PERCENTAGE OF FINAL WORK % 65
Total % 100

ECTS / Workload Table

Activities Number of Activities Duration (Hours) Workload
Course Hours 14 3 42
Study Hours Out of Class 14 4 56
Project 1 15 15
Homework Assignments 4 20 80
Midterms 1 3 3
Final 1 3 3
Total Workload 199

Contribution of Learning Outcomes to Programme Outcomes

No Effect 1 Lowest 2 Low 3 Average 4 High 5 Highest
           
Program Outcomes Level of Contribution
1) Adequate knowledge in mathematics, science and electric-electronic engineering subjects; ability to use theoretical and applied information in these areas to model and solve engineering problems. 4
2) Ability to identify, formulate, and solve complex engineering problems; ability to select and apply proper analysis and modeling methods for this purpose. 4
3) Ability to design a complex system, process, device or product under realistic constraints and conditions, in such a way as to meet the desired result; ability to apply modern design methods for this purpose. (Realistic constraints and conditions may include factors such as economic and environmental issues, sustainability, manufacturability, ethics, health, safety issues, and social and political issues, according to the nature of the design.) 4
4) Ability to devise, select, and use modern techniques and tools needed for electrical-electronic engineering practice; ability to employ information technologies effectively. 4
5) Ability to design and conduct experiments, gather data, analyze and interpret results for investigating engineering problems. 4
6) Ability to work efficiently in intra-disciplinary and multi-disciplinary teams; ability to work individually. 3
7) Ability to communicate effectively in English and Turkish (if he/she is a Turkish citizen), both orally and in writing. 3
8) Recognition of the need for lifelong learning; ability to access information, to follow developments in science and technology, and to continue to educate him/herself. 3
9) Awareness of professional and ethical responsibility. 1
10) Information about business life practices such as project management, risk management, and change management; awareness of entrepreneurship, innovation, and sustainable development.
11) Knowledge about contemporary issues and the global and societal effects of engineering practices on health, environment, and safety; awareness of the legal consequences of engineering solutions. 1