Digital image processing: MFF-CUNI


Introductory lecture on digital Image Processing and Recognition

The main attention is paid to image digitization, preprocessing (noise suppression, contrast enhancement, blur removal), edge detection, geometric transformations, object description and automatic recognition (classification) methods. The explanation of the theory will be accompanied by examples of experiments and practical applications.


ZS 2023/2024 - Staré stránky

Odkaz pro studenty k přihlášení na zkoušky.


Winter semester 2024/25:

The lecture will take place at the Institute of Information Theory and Automation of the CAS, Pod vodárenskou věží 4, Prague 8, 182 00. Metro C, station Ládví (see map).

Dates of lectures and labs will be announced at the beginning of the semester.


  • image digitization, sampling and quantization of continuous functions, Shannon's theorem

  • basic image operations, histogram, contrast changes, noise removal, image focusing

  • linear filtering in spatial and frequency domain, convolution, Fourier transform

  • edge and corner detection

  • image degradation and its modelling, removal of basic types of degradation (motion blur and defocus), inverse and Wiener filters

  • image segmentation

    image registration (matching)

  • symbolic description of planar objects - general principles

  • invariants for description and recognition of 2-D objects

  • theory of feature recognition, classifiers with and without learning, NN-classifier, linear classifier, Bayes classifier

  • cluster analysis in feature space, iterative and hierarchical methods


The files contain most of the slides used. This is not the complete lecture content; the knowledge recorded in the files is insufficient to pass the exam!


Image processing:

[1] Pratt W. K.: Digital Image Processing (3rd ed.), John Wiley, New York, 2001

[2] Gonzales R. C., Woods R. E., Digital Image Processing (3rd or 4th ed.)

Image registration:

[3] Zitová B., Flusser J., Image registration methods: a survey. Image and Vision Computing, 21 (2003), 11, pp. 977-1000

Recognition, classifiers:

[4] Introduction to Object Recognition. DOWNLOAD

[5] Duda R.O. et al., Pattern Classification, (2nd ed.), John Wiley, New York, 2001


We highly recommend taking the optional exercises for this course (online):