P131 Digital Image Processing

Faculty of Informatics
Autumn 2000
Extent and Intensity
2/0. 2 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium), z (credit).
Teacher(s)
prof. RNDr. Michal Kozubek, Ph.D. (lecturer)
Guaranteed by
prof. RNDr. Luděk Matyska, CSc.
High-Resolution Cytometry Laboratory – Department of Machine Learning and Data Processing – Faculty of Informatics
Contact Person: prof. RNDr. Michal Kozubek, Ph.D.
Prerequisites
( M000 Calculus I || M500 Calculus I || X001 Matematická analýza 1 ) && ( M003 Linear Algebra and Geometry I || M503 Linear Algebra and Geometry I )
Knowledge at the level of the following courses is assumed: M000 Mathematical Analysis I, M003 Linear Algebra I.
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
Course objectives
Acquisition of 2D and 3D image data, process of signal digitization.
Properties of the digital image, types of noise.
Frequency characteristics, Fourier transform and Nyquist sampling theorem.
PSF and OTF functions, image formation as a convolution integral.
Image preprocessing, noise removal, linear and non-linear filters.
Deconvolution, blind and non-blind approach, MLE algorithm.
Edge detection, Laplace and Sobel operators.
Global and local thresholding, binary image and its modification.
Mathematical morphology, basic operations.
Image segmentation, using binary image, region growing, comparison with a template.
Separation of touching objects, object boundary analysis, watershed and LCS algorithms.
Object description, mostly used characteristics.
Object classification, application of neural networks.
Digital image processing in practice, biomedical applications.
Syllabus
  • Acquisition of 2D and 3D image data, process of signal digitization.
  • Properties of the digital image, types of noise.
  • Frequency characteristics, Fourier transform and Nyquist sampling theorem.
  • PSF and OTF functions, image formation as a convolution integral.
  • Image preprocessing, noise removal, linear and non-linear filters.
  • Deconvolution, blind and non-blind approach, MLE algorithm.
  • Edge detection, Laplace and Sobel operators.
  • Global and local thresholding, binary image and its modification.
  • Mathematical morphology, basic operations.
  • Image segmentation, using binary image, region growing, comparison with a template.
  • Separation of touching objects, object boundary analysis, watershed and LCS algorithms.
  • Object description, mostly used characteristics.
  • Object classification, application of neural networks.
  • Digital image processing in practice, biomedical applications.
Literature
  • ŠONKA, Milan and Václav HLAVÁČ. Počítačové vidění. Praha: Grada, 1992, 252 s. ISBN 80-85424-67-3. info
  • ŠONKA, Milan, Václav HLAVÁČ and Roger BOYLE. Image processing analysis and machine vision. London: Chapman & Hall, 1993, xix, 555. ISBN 0412455706. info
  • PRATT, William K. Digital image processing. 2nd ed. New York: John Wiley & Sons, 1991, xiv, 698. ISBN 0471857661. info
Language of instruction
Czech
Further Comments
The course is taught annually.
The course is taught: every week.
Teacher's information
http://www.fi.muni.cz/lom/courses/DigImgProc.shtml
The course is also listed under the following terms Autumn 2001.
  • Enrolment Statistics (Autumn 2000, recent)
  • Permalink: https://is.muni.cz/course/fi/autumn2000/P131