Computational Vision

Course Information
TitleΠροχωρημένη υπολογιστική όραση / Computational Vision
CodeDMCI101
FacultySciences
SchoolInformatics
Cycle / Level2nd / Postgraduate
Teaching PeriodWinter
CoordinatorIoannis Pitas
CommonYes
StatusActive
Course ID600016138

Programme of Study: PMS PSĪFIAKA MESA - YPOLOGISTIKĪ NOĪMOSYNĪ (2018 eōs sīmera) MF

Registered students: 6
OrientationAttendance TypeSemesterYearECTS
KORMOSElective Courses belonging to the selected specialization117.5

Programme of Study: PMS PSĪFIAKA MESA - YPOLOGISTIKĪ NOĪMOSYNĪ (2018 éōs sīmera) PF

Registered students: 14
OrientationAttendance TypeSemesterYearECTS
KORMOSElective Courses belonging to the selected specialization117.5
Class Information
Academic Year2020 – 2021
Class PeriodWinter
Faculty Instructors
Weekly Hours3
Class ID
600177048
Course Type 2016-2020
  • Scientific Area
Mode of Delivery
  • Face to face
Digital Course Content
Erasmus
The course is also offered to exchange programme students.
Language of Instruction
  • Greek (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
ActivitiesWorkloadECTSIndividualTeamworkErasmus
Lectures80✓
Reading Assigment15✓
Project20✓
Written assigments15✓
Exams20✓
Total150
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
07-10-2020