Gäller från och med: Autumn 2013
Beslutad av: FN1/Anders Gustafsson
Datum för fastställande: 2014-04-22
Avdelning: Mathematics
Kurstyp: Ren forskarutbildningskurs
Undervisningsspråk: English
The main aim of the course is to give a basic introduction to theory and mathematical methods used in image analysis, to an extent that will allow research and industrial image processing problems to be handled. In addition the aim is to help the doctoral student develop his or her ability in problem solving, both with or without a computer. Furthermore, the aim is to prepare the postgraduate student for further studies in e.g. computer vision, multispectral image analysis and statistical image analysis.
Kunskap och förståelse
För godkänd kurs skall doktoranden
Färdighet och förmåga
För godkänd kurs skall doktoranden
Basic mathematical concepts: Image transforms, DFT, FFT. Image enhancement: Grey level transforms, filtering. Image restoration: Filterings, inverse methods. Sampling and Interpolation: Continuous versus discrete theory, interpolation. Extraction of special features: Filtering, edge and corner detection. Segmentation: graph-methods, active contours, mathematical morphology. Registration. Machine Learning: Training, testing, generalization, hypothesis spaces.
Szeliski, R.: Computer Vision: Algorithms and Applications. Springer, 2010. ISBN 9781848829343.
It is possible to pass the course without owning the book, using material available through the course home page.
Undervisningsformer: Föreläsningar, projekt
Examinationsformer: Skriftlig rapport, seminarieföredrag av deltagarna
Betygsskala: Underkänd, godkänd
Examinator:
Förutsatta förkunskaper: Basic calculus and linear algebra. Higher skills in experimentation, in project work and in programming.
Kursansvariga:
Hemsida: http://www.maths.lu.se/english/phd-studies/