COMPUTER VISION
| Title | ΤΕΧΝΗΤΗ ΟΡΑΣΗ / COMPUTER VISION |
| Code | DM05 |
| Faculty | Sciences |
| School | Informatics |
| Cycle / Level | 2nd / Postgraduate |
| Teaching Period | Winter |
| Coordinator | Ioannis Pitas |
| Common | No |
| Status | Active |
| Course ID | 40002280 |
| Academic Year | 2016 – 2017 |
| Class Period | Winter |
| Faculty Instructors |
|
| Weekly Hours | 3 |
| Class ID | 600039946
|
Course Type 2016-2020
- Scientific Area
Course Type 2011-2015
Specific Foundation / Core
Mode of Delivery
- Face to face
Digital Course Content
- e-Study Guide https://qa.auth.gr/en/class/1/600039946
- At the Website of the School: http://pileas.csd.auth.gr/course/view.php?id=107
Language of Instruction
- Greek (Instruction, Examination)
Prerequisites
General Prerequisites
Basic knowledge in digital image processing. Programming skills. Good level of English.
Learning Outcomes
a) Knowledge: Familiarization with the fundamental principles, theory algorithms and technology of image analysis and computer vision. Acquaintance with digital video processing programming using C/C++ and MATLAB. Introduction to computer vision systems and their application in areas such as image description and retrieval, biometrics, robotic vision, human centered interfaces etc.
b) Skills: Setting the foundations for advanced studies on computer vision and applications in image description and retrieval, biometrics, robotic vision, human centered interfaces. Acquisition of skills in the use and development of computer vision algorithms. Promoting analytical and programming skills. Ability to develop basic computer vision applications using C/C++, MATLAB, OpenCV.
General Competences
- Apply knowledge in practice
- Retrieve, analyse and synthesise data and information, with the use of necessary technologies
- Adapt to new situations
- Make decisions
- Work autonomously
- Work in teams
Course Content (Syllabus)
Image texture and shape description. Mathematical morphology. 3D image analysis. Surface geometry. Feature detection (edge, line, corner). Camera calibration. Static and synamic stereo image analysis. Shape information from video extraction. Depth information extraction. Recognition of 2D and 3D objects. Object localization. Αpplications in image description and retrieval, biometrics, robotic vision, human centered interfaces.
Educational Material Types
- Slide presentations
- Book
Use of Information and Communication Technologies
Use of ICT
- Use of ICT in Course Teaching
Course Organization
| Activities | Workload | ECTS | Individual | Teamwork | Erasmus |
|---|---|---|---|---|---|
| Lectures | 126 | 4.2 | ✓ | ||
| Reading Assigment | 30 | 1 | |||
| Project | 45 | 1.5 | |||
| Exams | 24 | 0.8 | |||
| Total | 225 | 7.5 |
Student Assessment
Description
Written exams.
Literature survey or programming assignments or mid-term exams or oral presentations provide an additional 2-4 points, if the exam mark is at least 4.
Student Assessment methods
- Written Exam with Short Answer Questions (Formative, Summative)
- Written Assignment (Formative, Summative)
- Performance / Staging (Formative, Summative)
- Written Exam with Problem Solving (Formative, Summative)
Bibliography
Course Bibliography (Eudoxus)
Πήτας Ι. «Ψηφιακή Επεξεργασία Εικόνας», Θεσσαλονίκη, 2010.
Trucco and Alessandro Verri 'Introductory Techniques for 3-D Computer Vision, 1998
Additional bibliography for study
Richard Szeliski, Computer Vision: Algorithms and Applications, 2010
Richard Hartley and Andrew Zisserman, Multiple View Geometry in Computer Vision, 2004
Last Update
01-04-2016
