| School/Faculty/Institute | Faculty of Engineering | ||||||||
| Course Code | COMP 482 | ||||||||
| Course Title in English | Computer Vision | ||||||||
| Course Title in Turkish | Bilgisayarla Görü | ||||||||
| Language of Instruction | EN | ||||||||
| Type of Course | Flipped Classroom | ||||||||
| Level of Course | Introductory | ||||||||
| Semester | Fall | ||||||||
| Contact Hours per Week |
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| Estimated Student Workload | 161 hours per semester | ||||||||
| Number of Credits | 6 ECTS | ||||||||
| Grading Mode | Standard Letter Grade | ||||||||
| Pre-requisites |
COMP 106 - Object-Oriented Programming | COMP 110 - Object-Oriented Programming (JAVA) |
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| Expected Prior Knowledge | Object Oriented Programming, Data Structures | ||||||||
| Co-requisites | None | ||||||||
| Registration Restrictions | Only Undergraduate Students | ||||||||
| Overall Educational Objective | To become familiar with the fundamental concepts of Computer Vision, such as image formation, camera parameters, preprocessing, convolution, segmentation, edge and corner detection, line and ellipse fitting, image understanding and object recognition. | ||||||||
| Course Description | This course provides a comprehensive introduction to some fundamental aspects of Computer Vision. The following topics are covered: Introduction, Image formation, camera parameters, preprocessing, convolution, segmentation, edge and corner detection, line and ellipse fitting, Object Tracking, image understanding and object recognition, Deep Learning. | ||||||||
| Course Description in Turkish | Bu derste; bilgisayarla görünün temel kavramları şu konu başlıklar altında kapsamlı bir şekilde incelenmektedir: Giriş, görüntü oluşumu, kamera parametreleri, önişleme, evriştirme, bölütleme, kenar ve köşe bulma, doğru ve elips uydurma, görüntü analizi, nesne tanıma ve derin öğrenme. |
Course Learning Outcomes and CompetencesUpon successful completion of the course, the learner is expected to be able to:1) understand image formation process, camera parameters and projections; 2) apply convolution for filtering and preprocessing; 3) apply probability and statistics to solve problems in computer vision; 4) develop feature extractors such as edge, corner, line extractors; 5) develop solutions using image stitching and stereo images; 6) develop image understanding and objects recognition solutions; 7) communicate effectively by means of reports and presentations; 8) analyze and interpret data, and use engineering judgment to draw conclusions; 9) acquire and apply new knowledge as needed; |
| Program Learning Outcomes/Course Learning Outcomes | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 |
|---|---|---|---|---|---|---|---|---|---|
| 1) Thorough knowledge of the major concepts, theoretical perspectives, empirical findings, and historical trends in psychology. | |||||||||
| 2) Understanding of and ability to apply essential research methods in psychology, including research design, data analysis, and data interpretation. | |||||||||
| 3) Competence to use critical and creative thinking, skeptical inquiry and a scientific approach to solving problems related to behavior and mental processes. | |||||||||
| 4) Understanding and ability to apply psychological principles, skills and values in personal, social, and organizational contexts. | |||||||||
| 5) Ability to weigh evidence, to tolerate ambiguity, and to reflect other values that underpin psychology as a discipline. | |||||||||
| 6) Internalization and dissemination of professional ethical standards. | |||||||||
| 7) Demonstration of competence in information technologies, and the ability to use computer and other technologies for purposes related to the pursuit of knowledge in psychology and the broader social sciences. | |||||||||
| 8) Skills to communicate the knowledge of psychological science effectively, in a variety of formats, in both Turkish and in English (in English, at least CEFR B2 level). | |||||||||
| 9) Recognition, understanding, and respect for the complexity of sociocultural and international diversity. | |||||||||
| 10) Recognition for the need for, and the skills to pursue, lifelong learning, inquiry, and self-improvement. | |||||||||
| 11) Ability to formulate critical hypotheses based on psychological theory and literature, and design studies to test those hypotheses. | |||||||||
| 12) Ability to acquire knowledge independently, and to plan one’s own learning. | |||||||||
| 13) Demonstration of advanced competence in the clarity and composition of written work and presentations. |
| N None | S Supportive | H Highly Related |
| Program Outcomes and Competences | Level | Assessed by | |
| 1) | Thorough knowledge of the major concepts, theoretical perspectives, empirical findings, and historical trends in psychology. | N | |
| 2) | Understanding of and ability to apply essential research methods in psychology, including research design, data analysis, and data interpretation. | N | |
| 3) | Competence to use critical and creative thinking, skeptical inquiry and a scientific approach to solving problems related to behavior and mental processes. | H | Exam,HW,Participation |
| 4) | Understanding and ability to apply psychological principles, skills and values in personal, social, and organizational contexts. | N | |
| 5) | Ability to weigh evidence, to tolerate ambiguity, and to reflect other values that underpin psychology as a discipline. | N | |
| 6) | Internalization and dissemination of professional ethical standards. | N | |
| 7) | Demonstration of competence in information technologies, and the ability to use computer and other technologies for purposes related to the pursuit of knowledge in psychology and the broader social sciences. | N | |
| 8) | Skills to communicate the knowledge of psychological science effectively, in a variety of formats, in both Turkish and in English (in English, at least CEFR B2 level). | N | |
| 9) | Recognition, understanding, and respect for the complexity of sociocultural and international diversity. | S | Participation |
| 10) | Recognition for the need for, and the skills to pursue, lifelong learning, inquiry, and self-improvement. | S | HW,Participation |
| 11) | Ability to formulate critical hypotheses based on psychological theory and literature, and design studies to test those hypotheses. | N | |
| 12) | Ability to acquire knowledge independently, and to plan one’s own learning. | S | Exam,HW |
| 13) | Demonstration of advanced competence in the clarity and composition of written work and presentations. | H | Exam,HW |
| Prepared by and Date | MUHİTTİN GÖKMEN , April 2018 |
| Course Coordinator | TUBA AYHAN |
| Semester | Fall |
| Name of Instructor | Prof. Dr. MUHİTTİN GÖKMEN |
| Week | Subject |
| 1) | Introduction |
| 2) | Image formation |
| 3) | Camera parameters |
| 4) | Preprocessing: Histogram modifications |
| 5) | Convolution and noise reduction |
| 6) | Edge and corner detection |
| 7) | Line, circle and ellipse fitting |
| 8) | RANSAC and Homography |
| 9) | Binocular Stereo |
| 10) | Image Understanding |
| 11) | Object Recognition-PCA |
| 12) | Object Recognition-Neural Networks |
| 13) | Object Recognition-Deep Learning |
| 14) | Project presentations |
| 15) | Final Exam/Project/Presentation Period |
| 16) | Final Exam/Project/Presentation Period |
| Required/Recommended Readings | Computer Vision: Algorithms and Applications, Richard Szeliski, Springer Science & Business Media, 2010 Introductory Techniques for 3-D Computer Vision, by Emanuele Trucco, Alessandro Verri, Prentice-Hall, 1998 | |||||||||||||||
| Teaching Methods | Lecturing and exercises in the classroom with computers. In-class exercises and 3 Projects will be carried out by students | |||||||||||||||
| Homework and Projects | In-class exercises, 2 Projects, 2 Midterm exams, Term project | |||||||||||||||
| Laboratory Work | Programming exercises | |||||||||||||||
| Computer Use | For Programming | |||||||||||||||
| Other Activities | None | |||||||||||||||
| Assessment Methods |
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| Course Administration |
gokmenm@mef.edu.tr Instructor’s office and phone number, office hours, email address: To be announced -Office: 5th Floor, #18 -Phone number: 0 212 395 36 26 - Email address: gokmenm@mef.edu.tr Rules for attendance: Minimum of 70% attendance required. Missing a quiz: Provided that proper documents of excuse are presented, each missed quiz by the student will be given a grade which is equal to the average of all of the other quizzes. No make-up will be given. Missing a midterm: Provided that proper documents of excuse are presented, each missed midterm by the student will be given the grade of the final exam. No make-up will be given. Missing a final: Faculty regulations. A reminder of proper classroom behavior, code of student conduct: YÖK Regulations Statement on plagiarism: YÖK Regulations http://3fcampus.mef.edu.tr/uploads/cms/webadmin.mef.edu.tr/4833_2.pdf |
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| Activity | No/Weeks | Hours | Calculation | ||||
| No/Weeks per Semester | Preparing for the Activity | Spent in the Activity Itself | Completing the Activity Requirements | ||||
| Course Hours | 14 | 1 | 3 | 1 | 70 | ||
| Project | 1 | 35 | 7 | 42 | |||
| Quiz(zes) | 3 | 4 | 1 | 15 | |||
| Midterm(s) | 2 | 15 | 2 | 34 | |||
| Total Workload | 161 | ||||||
| Total Workload/25 | 6.4 | ||||||
| ECTS | 6 | ||||||