ECTS - Robot Vision
Robot Vision (MECE445) Course Detail
| Course Name | Course Code | Season | Lecture Hours | Application Hours | Lab Hours | Credit | ECTS |
|---|---|---|---|---|---|---|---|
| Robot Vision | MECE445 | Area Elective | 3 | 0 | 0 | 3 | 5 |
| Pre-requisite Course(s) |
|---|
| N/A |
| 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 Lecturer(s) |
|
| 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;
|
| 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 knowledge of mathematics, science, and engineering. | X | ||||
| 2 | An ability to design and conduct experiments, as well as to analyse and interpret data. | X | ||||
| 3 | An ability to design a system, component, or process to meet desired needs. | X | ||||
| 4 | An ability to function on multi-disciplinary domains. | X | ||||
| 5 | An ability to identify, formulate, and solve engineering problems. | X | ||||
| 6 | An understanding of professional and ethical responsibility. | |||||
| 7 | An ability to communicate effectively. | |||||
| 8 | Recognition of the need for, and an ability to engage in life-long learning. | X | ||||
| 9 | A knowledge of contemporary issues. | X | ||||
| 10 | An ability to use the techniques, skills, and modern engineering tools necessary for engineering practice. | X | ||||
| 11 | Skills in project management and recognition of international standards and methodologies | X | ||||
| 12 | An ability to produce engineering products or prototypes that solve real-life problems. | X | ||||
| 13 | Skills that contribute to professional knowledge. | X | ||||
| 14 | An ability to make methodological scientific research. | X | ||||
| 15 | An ability to produce, report and present an original or known scientific body of knowledge. | X | ||||
| 16 | An ability to defend an originally produced idea. | X | ||||
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 | ||