Gäller från och med: Autumn 2013
Beslutad av: FN1/Anders Gustafsson
Datum för fastställande: 2014-01-31
Avdelning: Mathematics
Kurstyp: Gemensam kurs, avancerad nivå och forskarnivå
Kursen ges även på avancerad nivå med kurskod: FMA170
Undervisningsspråk: English
The aim of the course is to give necessary knowledge of digital image analysis for further research within the area and to be able to use digital image analysis within other research areas such as computer graphics, image coding, video coding and industrial image processing problems. The aim is also to prepare the 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 (Discrete Fourier Transform), FFT (Fast Fourier Transform). Image enhancement: Grey level transforms, filtering. Image restoration: Filterings, inverse methods. Scale space theory: Continuous versus discrete theory, interpolation. Extraction of special features: Filtering, edge and corner detection. Segmentation: graph-methods, active contours, mathematical morphology. Bayesian image handling: MAP(Maximum Aposteriori) estimations, simulation. Pattern recognition: Classification, SVM (Support Vector Machines), PCA (Principal Component Analysis), learning. 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, laborationer, övningar
Examinationsform: Inlämningsuppgifter
Betygsskala: Underkänd, godkänd
Examinator:
Kursansvarig: Karl Åström <karl.astrom@math.lth.se>