Computational Vision
| Title | Προχωρημένη υπολογιστική όραση / Computational Vision |
| Code | DMCI101 |
| Faculty | Sciences |
| School | Informatics |
| Cycle / Level | 2nd / Postgraduate |
| Teaching Period | Winter |
| Coordinator | Ioannis Pitas |
| Common | Yes |
| Status | Active |
| Course ID | 600016138 |
Programme of Study: PMS PSĪFIAKA MESA - YPOLOGISTIKĪ NOĪMOSYNĪ (2018 éōs sīmera) PF
Registered students: 14
| Orientation | Attendance Type | Semester | Year | ECTS |
|---|---|---|---|---|
| KORMOS | Elective Courses belonging to the selected specialization | 1 | 1 | 7.5 |
| Academic Year | 2020 – 2021 |
| Class Period | Winter |
| Faculty Instructors |
|
| Weekly Hours | 3 |
| Class ID | 600177048
|
Course Type 2016-2020
- Scientific Area
Mode of Delivery
- Face to face
Digital Course Content
- e-Study Guide https://qa.auth.gr/en/class/1/600177048
- At the Website of the School: dmci.csd.auth.gr
- Other: www.aiia.csd.auth.gr
Erasmus
The course is also offered to exchange programme students.
Language of Instruction
- Greek (Instruction, Examination)
- English (Instruction, Examination)
Prerequisites
General Prerequisites
Basic mathematical knowledge. Programming knowledge (C/C++, or Python, MATLAB.
Capabilities to study scientific literature in English
Learning Outcomes
Image acquisition. Mathematical modeling of image formation. Inntroduction to image
processing and analysis. Camera calibration. Stereo vision. Depth estimation. Object localization.
3D image analysis. Surface geometry. Object topology. Object landmarks and features. Object
recognition. Object registration. Object description. Applications in medical imaging, image
retrieval, robotic vision.
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 an international context
- Work in an interdisciplinary team
- Generate new research ideas
- Design and manage projects
- Be critical and self-critical
- Advance free, creative and causative thinking
Course Content (Syllabus)
Image acquisition. Mathematical modeling of image formation. Inntroduction to image
processing and analysis. Camera calibration. Stereo vision. Depth estimation. Object localization.
3D image analysis. Surface geometry. Object topology. Object landmarks and features. Object
recognition. Object registration. Object description. Applications in medical imaging, image
retrieval, robotic vision.
Keywords
Advanced computer vision
Educational Material Types
- Notes
- Slide presentations
- Book
Use of Information and Communication Technologies
Use of ICT
- Use of ICT in Course Teaching
- Use of ICT in Communication with Students
Course Organization
| Activities | Workload | ECTS | Individual | Teamwork | Erasmus |
|---|---|---|---|---|---|
| Lectures | 80 | ✓ | |||
| Reading Assigment | 15 | ✓ | |||
| Project | 20 | ✓ | |||
| Written assigments | 15 | ✓ | |||
| Exams | 20 | ✓ | |||
| Total | 150 |
Student Assessment
Student Assessment methods
- Written Exam with Extended Answer Questions (Formative, Summative)
- Written Assignment (Formative, Summative)
Bibliography
Additional bibliography for study
N. Nikolaidis and I. Pitas, 3D Image Processing Algorithms, J. Wiley, 2000.
R.Szelinski, “ Computer Vision ” , Springer 2011
Hartley R, Zisserman A. , “ Multiple view geometry in computer vision” . Cambridge university press; 2003.
Davies, E. Roy. “Computer vision: principles, algorithms, applications, learning ”. Academic Press, 2017
Trucco, Emanuele, and Alessandro Verri. Introductory techniques for 3-D computer vision. Vol.201. Englewood Cliffs: Prentice Hall, 1998.
Last Update
19-01-2024
