227-0447-00L  Image Analysis and Computer Vision

SemesterAutumn Semester 2019
LecturersL. Van Gool, O. Göksel, E. Konukoglu
Periodicityyearly recurring course
Language of instructionEnglish



Courses

NumberTitleHoursLecturers
227-0447-00 VImage Analysis and Computer Vision3 hrs
Thu13:15-16:00ETF C 1 »
L. Van Gool, O. Göksel, E. Konukoglu
227-0447-00 UImage Analysis and Computer Vision1 hrs
Thu16:15-17:00ETF C 1 »
L. Van Gool, O. Göksel, E. Konukoglu

Catalogue data

AbstractLight and perception. Digital image formation. Image enhancement and feature extraction. Unitary transformations. Color and texture. Image segmentation. Motion extraction and tracking. 3D data extraction. Invariant features. Specific object recognition and object class recognition. Deep learning and Convolutional Neural Networks.
ObjectiveOverview of the most important concepts of image formation, perception and analysis, and Computer Vision. Gaining own experience through practical computer and programming exercises.
ContentThis course aims at offering a self-contained account of computer vision and its underlying concepts, including the recent use of deep learning.
The first part starts with an overview of existing and emerging applications that need computer vision. It shows that the realm of image processing is no longer restricted to the factory floor, but is entering several fields of our daily life. First the interaction of light with matter is considered. The most important hardware components such as cameras and illumination sources are also discussed. The course then turns to image discretization, necessary to process images by computer.
The next part describes necessary pre-processing steps, that enhance image quality and/or detect specific features. Linear and non-linear filters are introduced for that purpose. The course will continue by analyzing procedures allowing to extract additional types of basic information from multiple images, with motion and 3D shape as two important examples. Finally, approaches for the recognition of specific objects as well as object classes will be discussed and analyzed. A major part at the end is devoted to deep learning and AI-based approaches to image analysis. Its main focus is on object recognition, but also other examples of image processing using deep neural nets are given.
Lecture notesCourse material Script, computer demonstrations, exercises and problem solutions
Prerequisites / NoticePrerequisites:
Basic concepts of mathematical analysis and linear algebra. The computer exercises are based on Python and Linux.
The course language is English.

Performance assessment

Performance assessment information (valid until the course unit is held again)
Performance assessment as a semester course
ECTS credits6 credits
ExaminersE. Konukoglu, O. Göksel, L. Van Gool
Typesession examination
Language of examinationEnglish
RepetitionThe performance assessment is offered every session. Repetition possible without re-enrolling for the course unit.
Mode of examinationwritten 120 minutes
Additional information on mode of examinationDifferent parts of the lecture will be assessed in a maximum 2 hours written exam in English.
Written aidsNone
This information can be updated until the beginning of the semester; information on the examination timetable is binding.

Learning materials

 
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