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 | Bachelor’s Degree (First Cycle) |
| 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 | Possesses sufficient knowledge in mathematics, natural sciences, and discipline-specific topics in Electrical and Electronics Engineering; uses this theoretical and practical knowledge to solve complex engineering problems. | X | ||||
| 2 | Identifies, defines, formulates, and solves complex engineering problems; selects and applies appropriate analytical and modeling methods for this purpose. | X | ||||
| 3 | Designs complex systems, processes, devices, or products under realistic constraints and conditions to meet specific requirements; applies modern design methods for this purpose. (Realistic constraints and conditions may include factors such as economy, environmental issues, sustainability, manufacturability, ethics, health, safety, social and political issues, depending on the nature of the design.) | X | ||||
| 4 | Selects and uses modern techniques and tools necessary for the analysis and solution of complex problems encountered in engineering applications; effectively uses information technologies. | |||||
| 5 | Designs experiments, conducts tests, collects data, analyzes, and interprets results to investigate complex engineering problems or discipline-specific research topics. | |||||
| 6 | Works effectively in disciplinary and interdisciplinary teams; develops the ability to work independently. | |||||
| 7 | Communicates effectively in both written and verbal forms; possesses proficiency in at least one foreign language; writes effective reports, understands written reports, prepares design and production reports, delivers effective presentations, and gives and receives clear instructions. | |||||
| 8 | Recognizes the need for lifelong learning; accesses information, follows developments in science and technology, and continuously renews oneself. | |||||
| 9 | Acts in accordance with ethical principles, assumes professional and ethical responsibility, and possesses knowledge about the standards used in engineering practices. | |||||
| 10 | Possesses knowledge about professional practices such as project management, risk management, and change management; gains awareness of entrepreneurship and innovation; understands the principles of sustainable development. | |||||
| 11 | Understands the universal and societal impacts of engineering practices on health, environment, and safety; recognizes the contemporary issues reflected in the field of engineering and understands the legal implications of engineering solutions. | |||||
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 | ||