Robot Vision (MECE445) Course Detail

Course Name Course Code Season Lecture Hours Application Hours Lab Hours Credit ECTS
Robot Vision MECE445 Elective Courses 3 0 0 3 5
Pre-requisite Course(s)
None
Course Language English
Course Type Elective Courses
Course Level Natural & Applied Sciences Master's Degree
Mode of Delivery Face To Face
Learning and Teaching Strategies Lecture, Experiment, Problem Solving.
Course Coordinator
Course Lecturer(s)
  • Assoc. Prof. Dr. Fuad Aliew
Course Assistants
Course Objectives Deriving a symbolic description of the environment from an image and understanding physics of image formation. To introduce the student to computer vision algorithms, methods and concepts. To teach the fundamental concepts in computer vision and to prepare the student to design simple vision systems. To enable the students to implement vision systems to mechatronic systems. To familiarize students with typical vision hardware systems and software tools.
Course Learning Outcomes The students who succeeded in this course;
  • The course enables students to analyze and design a vision system which is a crucial data acquiring and preprocessing method in mechatronic systems where applicable.
Course Content An introduction to the algorithms and mathematical analysis associated with the visual process; binary image processing, regions and segmentation, edge detection, photometric stereo, stereo and calibration, introduction to dynamic vision and motion.

Weekly Subjects and Releated Preparation Studies

Week Subjects Preparation
1 Introduction, Robot Vision Overview (Relation with other areas) N/A
2 Robot Vision Overview (Image formations and sensing, Projections, Brightness, Lenses, Image Sensing) N/A
3 Binary Images and their Properties (Basics, Geometrical Properties, Topological properties) N/A
4 Binary Algorithms, Regions and Segmentation (Histogram Based) N/A
5 Regions and Segmentation (Histogram Based, Spatial Coherence) N/A
6 Edge Detection (Differential Operators, Discrete Approximations ) N/A
7 Edge Detection (Laplacian of Gaussian, Canny Edge Detector ) N/A
8 Photometric Stereo (Image Formation) N/A
9 Photometric Stereo (Radiometry,Reflectance) N/A
10 Stereo (Stereo Imaging , Stereo Matching, 3-D Models ) N/A
11 Calibration (Photogrammetry, Depth) N/A
12 Dynamic Vision (Motion Field and Optical Flow) N/A
13 Dynamic Vision (Motion Field and Optical Flow) N/A
14 Structure from Motion (3-D Motion Models) N/A
15 Case Studies N/A
16 Final Examination N/A

Sources

Course Book 1. Robot Vision (MIT Electrical Engineering and Computer Science), Berthold K. P. Horn, The MIT Press, ISBN-10: 0262081598
Other Sources 2. Stefan Florczyk, Robot Vision, WILEY-VCH Verlag GmbH & Co. KGaA, 2005, ISBN 3-527-40544-5

Evaluation System

Requirements Number Percentage of Grade
Attendance/Participation - -
Laboratory 10 20
Application - -
Field Work - -
Special Course Internship - -
Quizzes/Studio Critics - -
Homework Assignments - -
Presentation - -
Project 1 20
Report - -
Seminar - -
Midterms Exams/Midterms Jury 1 20
Final Exam/Final Jury 1 40
Toplam 13 100
Percentage of Semester Work 60
Percentage of Final Work 40
Total 100

Course Category

Core Courses X
Major Area Courses
Supportive Courses
Media and Managment Skills Courses
Transferable Skill Courses

The Relation Between Course Learning Competencies and Program Qualifications

# Program Qualifications / Competencies Level of Contribution
1 2 3 4 5
1 An ability to apply advanced knowledge in computational and/or manufacturing technologies to solve manufacturing engineering problems .
2 An ability to define and analyze issues related with manufacturing technologies.
3 An ability to develop a solution based approach and a model for an engineering problem and design and manage an experiment.
4 An ability to design a comprehensive manufacturing system based on creative utilization of fundamental engineering principles while fulfilling sustainability in environment and manufacturability and economic constraints.
5 An ability to chose and use modern technologies and engineering tools for manufacturing engineering applications.
6 Ability to perform scientific research and/or carry out innovative projects that are within the scope of manufacturing engineering.
7 An ability to utilize information technologies efficiently to acquire datum and analyze critically, articulate the outcome and make decision accordingly.
8 An ability to attain self-confidence and necessary organizational work skills to participate in multi-diciplinary and interdiciplinary teams as well as act individually.
9 An ability to attain efficient communication skills in Turkish and English both verbally and orally.
10 An ability to reach knowledge and to attain life-long learning and self-improvement skills, to follow recent advances in science and technology.
11 An awareness and responsibility about professional, legal, ethical and social issues in manufacturing engineering.
12 An awareness about solution focused project and risk management, enterpreneurship, innovative and sustainable development.
13 An understanding on the effects of engineering applications on health, social and legal aspects at universal and local level during decision making process.

ECTS/Workload Table

Activities Number Duration (Hours) Total Workload
Course Hours (Including Exam Week: 16 x Total Hours) 14 2 28
Laboratory 14 2 28
Application
Special Course Internship
Field Work
Study Hours Out of Class
Presentation/Seminar Prepration
Project 14 2 28
Report
Homework Assignments
Quizzes/Studio Critics
Prepration of Midterm Exams/Midterm Jury 1 20 20
Prepration of Final Exams/Final Jury 1 20 20
Total Workload 124