Image Processing
Faculteit  Science and Engineering 
Jaar  2018/19 
Vakcode  INMIP08 
Vaknaam  Image Processing 
Niveau(s)  master 
Voertaal  Engels 
Periode  semester I b 
ECTS  5 
Rooster  rooster.rug.nl 
Uitgebreide vaknaam  Image Processing  
Leerdoelen  At the end of the course, the student is able to: 1. Can 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. Is proficient in the implementation of simple image processing algorithms 3. has the ability to 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. The course provides the necessary knowledge for further study in Computer Vision, Scientific Visualization and Image Pattern Recognition.  
Uren per week  
Onderwijsvorm 
Hoorcollege (LC), Practisch werk (PRC)
(The pratical work is mandatory.) 

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=(E+P)/2. For the resit, Final Grade = resit exam grade. Final grades are rounded to half integers, except for final grades between 5 and 6, which are rounded to integers.) 

Vaksoort  master  
Coördinator  prof. dr. J.B.T.M. Roerdink  
Docent(en)  prof. dr. J.B.T.M. Roerdink  
Verplichte literatuur 


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  
Opgenomen in 
