ELECTRICAL AND ELECTRONICS ENGINEERING | |||||
Bachelor | TR-NQF-HE: Level 6 | QF-EHEA: First Cycle | EQF-LLL: Level 6 |
Course Code | Course Name | Semester | Theoretical | Practical | Credit | ECTS |
EEE5550 | Computer Vision and Pattern Recognition | Spring | 3 | 0 | 3 | 6 |
The course opens with the approval of the Department at the beginning of each semester |
Language of instruction: | En |
Type of course: | Departmental Elective |
Course Level: | Bachelor |
Mode of Delivery: | Face to face |
Course Coordinator : | Dr. Öğr. Üyesi ZAFER İŞCAN |
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. |
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 |
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 |
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 |
Course Notes: | 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 |
Semester Requirements | Number of Activities | Level of Contribution |
Attendance | % 0 | |
Laboratory | % 0 | |
Application | % 0 | |
Field Work | % 0 | |
Special Course Internship (Work Placement) | % 0 | |
Quizzes | % 0 | |
Homework Assignments | 4 | % 10 |
Presentation | % 0 | |
Project | 1 | % 25 |
Seminar | % 0 | |
Midterms | 1 | % 25 |
Preliminary Jury | % 0 | |
Final | 1 | % 40 |
Paper Submission | % 0 | |
Jury | % 0 | |
Bütünleme | % 0 | |
Total | % 100 | |
PERCENTAGE OF SEMESTER WORK | % 35 | |
PERCENTAGE OF FINAL WORK | % 65 | |
Total | % 100 |
Activities | Number of Activities | Duration (Hours) | Workload |
Course Hours | 14 | 3 | 42 |
Laboratory | 0 | 0 | 0 |
Application | 0 | 0 | 0 |
Special Course Internship (Work Placement) | 0 | 0 | 0 |
Field Work | 0 | 0 | 0 |
Study Hours Out of Class | 14 | 4 | 56 |
Presentations / Seminar | 0 | 0 | 0 |
Project | 1 | 15 | 15 |
Homework Assignments | 4 | 20 | 80 |
Quizzes | 0 | 0 | 0 |
Preliminary Jury | 0 | 0 | 0 |
Midterms | 1 | 3 | 3 |
Paper Submission | 0 | 0 | 0 |
Jury | 0 | 0 | 0 |
Final | 1 | 3 | 3 |
Total Workload | 199 |
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. | |
2) | Ability to identify, formulate, and solve complex engineering problems; ability to select and apply proper analysis and modeling methods for this purpose. | |
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) | Ability to devise, select, and use modern techniques and tools needed for electrical-electronic engineering practice; ability to employ information technologies effectively. | |
5) | Ability to design and conduct experiments, gather data, analyze and interpret results for investigating engineering problems. | |
6) | Ability to work efficiently in intra-disciplinary and multi-disciplinary teams; ability to work individually. | |
7) | Ability to communicate effectively in English and Turkish (if he/she is a Turkish citizen), both orally and in writing. | |
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. | |
9) | Awareness of professional and ethical responsibility. | |
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. |