| School/Faculty/Institute |
Graduate School |
| Course Code |
ITC 532 |
| Course Title in English |
Computer Vision |
| Course Title in Turkish |
Computer Vision |
| Language of Instruction |
EN |
| Type of Course |
Flipped Classroom |
| Level of Course |
Intermediate |
| Semester |
Spring |
| Contact Hours per Week |
| Lecture: 3 |
Recitation: 0 |
Lab: 0 |
Other: 0 |
|
| Estimated Student Workload |
188 hours per semester |
| Number of Credits |
7.5 ECTS |
| Grading Mode |
Standard Letter Grade
|
| Pre-requisites |
None |
| Expected Prior Knowledge |
None |
| Co-requisites |
None |
| Registration Restrictions |
Only Graduate 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 Competences
Upon 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) Employ edge and corner detection, curve fitting
4) Utilize image understanding and objects recognition
|
| Program Learning Outcomes/Course Learning Outcomes |
1 |
2 |
3 |
4 |
| 1) An ability to develop and deepen their knowledge in the field of
Information Technologies at the level of expertise based on their
undergraduate level qualifications. |
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| 2) An ability to apply scientific and practical knowledge in statistics,
computing and computer science. |
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| 3) A Comprehensive knowledge of analysis and
modeling methods and their limitations. |
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|
| 4) An ability to design and apply analytical, modeling and experimental H 2
based researches, analyzes and interprets complex situations
encountered in this process. |
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|
| 5) An ability to transmit the process and results of the work of
information systems systematically and clearly in written and oral form in
national and international environments. |
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| 6) An understanding of data collection, processing, use,
interpretation and social, scientific and ethical values in all
professional and professional activities. |
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| 7) An ability to take a leadership position in multi-disciplinary teams, develop information-based solution approaches in complex situations and to take
responsibility. |
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| 8) An understanding of the impact of engineering solutions in a global, economic, environmental, and societal context. |
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| 9) An ability to communicate verbally and in writing in English at least at
the level of B2 of CEFR. |
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| 10) An understanding the social and environmental aspects of IT
applications. |
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Relation to Program Outcomes and Competences
| N None |
S Supportive |
H Highly Related |
| |
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| |
Program Outcomes and Competences |
Level |
Assessed by |
| 1) |
An ability to develop and deepen their knowledge in the field of
Information Technologies at the level of expertise based on their
undergraduate level qualifications. |
N |
|
| 2) |
An ability to apply scientific and practical knowledge in statistics,
computing and computer science. |
N |
|
| 3) |
A Comprehensive knowledge of analysis and
modeling methods and their limitations. |
N |
|
| 4) |
An ability to design and apply analytical, modeling and experimental H 2
based researches, analyzes and interprets complex situations
encountered in this process. |
N |
|
| 5) |
An ability to transmit the process and results of the work of
information systems systematically and clearly in written and oral form in
national and international environments. |
N |
|
| 6) |
An understanding of data collection, processing, use,
interpretation and social, scientific and ethical values in all
professional and professional activities. |
N |
|
| 7) |
An ability to take a leadership position in multi-disciplinary teams, develop information-based solution approaches in complex situations and to take
responsibility. |
N |
|
| 8) |
An understanding of the impact of engineering solutions in a global, economic, environmental, and societal context. |
N |
|
| 9) |
An ability to communicate verbally and in writing in English at least at
the level of B2 of CEFR. |
N |
|
| 10) |
An understanding the social and environmental aspects of IT
applications. |
N |
|
| Prepared by and Date |
, |
| Course Coordinator |
TUNA ÇAKAR |
| Semester |
Spring |
| Name of Instructor |
Prof. Dr. MUHİTTİN GÖKMEN |
Course Contents
| 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) |
Optical Flow and Tracking |
| 11) |
Image Understanding |
| 12) |
Object Recognition-PCA |
| 13) |
Object Recognition-Deep Learning |
| 14) |
Project presentations |
| 15) |
Final Examination Period |
| 16) |
Final Examination 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. |
| Homework and Projects | In-class exercises, 3 Projects |
| Laboratory Work | Programming exercises |
| Computer Use | For Programming |
| Other Activities | None |
| Assessment Methods |
| Assessment Tools |
Count |
Weight |
| Quiz(zes) |
4 |
% 20 |
| Project |
2 |
% 40 |
| Final Examination |
1 |
% 40 |
| TOTAL |
% 100 |
|
| Course Administration |
gokmenm@mef.edu.tr
02123953600
Office: 5th Floor, #18
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.
A reminder of proper classroom behavior, code of student conduct: Academic dishonesty and plagiarism will be subject to Law on Higher Education Article 54.
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