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 | Gains the ability to apply advanced computational and/or manufacturing technology knowledge to solve manufacturing engineering problems. | |||||
| 2 | Develops the ability to analyze and define issues related to manufacturing technologies. | |||||
| 3 | Develops an approach for solving encountered engineering problems, and designs and conducts models and experiments. | |||||
| 4 | Designs and manufactures a comprehensive manufacturing system —including method, product, or device development— based on the creative application of fundamental engineering principles, under constraints of economic viability, environmental sustainability, and manufacturability. | |||||
| 5 | Selects and uses modern techniques and engineering tools for manufacturing engineering applications. | |||||
| 6 | Conducts scientific research in the field of manufacturing engineering and/or plans and carries out a project involving innovative manufacturing technologies. | |||||
| 7 | Effectively uses information technologies to collect and analyze data, think critically, interpret results, and make sound decisions. | |||||
| 8 | Works effectively as a member of multidisciplinary and intra-disciplinary teams or individually; demonstrates the confidence and organizational skills required. | X | ||||
| 9 | Communicates effectively in both spoken and written Turkish and English. | |||||
| 10 | Engages in lifelong learning, accesses information, keeps up with the latest developments in science and technology, and continuously renews oneself. | |||||
| 11 | Demonstrates awareness and a sense of responsibility regarding professional, legal, ethical, occupational safety, and social issues in the field of Manufacturing Engineering. | |||||
| 12 | Effectively utilizes resources (personnel, equipment, costs) to enhance national competitiveness and improve manufacturing industry productivity; conducts solution-oriented project and risk management; and demonstrates awareness of entrepreneurship, innovation, and sustainable development. | |||||
| 13 | Gathers knowledge about the health, environmental, social, and legal impacts of engineering practices at both global and local levels when making decisions. | |||||
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 | ||