Image Processing

Uitgebreide vaknaam	Image Processing
Leerdoelen	At the end of the course, the student is able to: 1. reproduce and apply knowledge of the basic mathematical and computational concepts regarding image acquisition, digitization, enhancement, colour models, (non)linear image filtering, wavelet analysis, edge-detection, segmentation, and image description; 2. show proficiency in the implementation of simple image processing algorithms; 3. collaborate with others in performing practical assignments and reporting about it.
Omschrijving	The course provides insight in the basic concepts of digital image processing, from image acquisition, digitization, and preprocessing, to image enhancement, compression, restoration, segmentation and description. Both standard linear image processing algorithms are treated, as well as some concepts and algorithms from nonlinear morphological image processing. Also, some results on multiscale image analysis are presented. The student acquires experience with the design and implementation of image processing algorithms.
Uren per week
Onderwijsvorm	Hoorcollege (LC), Practisch werk (PRC)
Toetsvorm	Practisch werk (PR), Schriftelijk tentamen (WE) (Let P = mark practicals, E = mark written exam. The final grade F for this course is obtained as follows: if E<5 then F=E else F= (2E+P)/3. In case of a resit exam, the resit grade replaces the exam grade E in this formula. Final grades are rounded to half integers, except for final grades between 5 and 6, which are rounded to integers.)
Vaksoort	master
Coördinator	dr. A. Meijster
Docent(en)	J. Guo, PhD. ,dr. A. Meijster
Verplichte literatuur	Titel Auteur ISBN Prijs Syllabus Lab Sessions Image Processing Digital Image Processing, 4th ed. - Global Edition (Pearson, 2018). R.C. Gonzalez and R.E. Woods 9781292223049
Entreevoorwaarden	The course unit assumes prior knowledge acquired from linear algebra, analysis (integration and differentiation, Fourier analysis), discrete structures (set theory, lattices), signals and systems, elementary statistics, and basic programming skills with a working knowledge of Matlab.
Opmerkingen	This course has limited enrollment: - CS students can always enter the course, regardless of whether the course is mandatory for them or not. - The number of enrolments for other non-CS students is limited. These students need to meet the course prerequisite requirements as mentioned on Ocasys. Priority is given to students for which the course is an official elective (see list below). - An exception can be made for exchange students, if they have a CS background: please contact the FSE International Office. See here for more info about the enrollment procedure.
Opgenomen in	Opleiding Jaar Periode Type MSc Astronomy: Quantum Universe  (Optional Courses in Data Science (DS)) - semester II a keuze MSc Computing Science: Data Science and Systems Complexity  (Guided choice course units) - semester II a keuze MSc Computing Science: Intelligent Systems and Visual Computing  (Compulsory course units) 1 semester II a verplicht MSc Computing Science: Science Business and Policy  (Elective course units) 1 semester II a keuze MSc Courses for Exchange Students: AI - Computing Science - Mathematics - semester II a