Advanced Methods of Digital Image Processing

• 39 hrs lectures

Deeper insight into advanced methods of image data processing, abilities to apply the methods and, if needed, to modify them for a concrete problem.

Providing deeper knowledge of theoretically demanding methods of image data processing and of their applications.

Knowledge of signal processing.

• A.K. Jain: Fundamentals of Digital Image Processing, Prentice Hall Int. Edit., 1989
• J. Jan: Čísl. filtrace, analýza a rest. signálů, kap. 14, Vyd. VUT v Brně 1997 (vyjde rozšířeno a modernizováno 2001)
• J. Jan: Digital Signal Filtering, Analysis and Restoration. IEE Publishing, London, UK, 2000

• A.K. Katsagellos (ed.): Digital Image Restoration. Springer 1991
• R.M. Haralick, L.G. Shapiro: Computer and Robot Vision. Addison - Wesley 1992
• A. Rosenfeld, A.C. Kak: Digital Picture Processing (2nd ed.), Acad. Press 1982
• W.K. Pratt: Digital Image Processing (2nd ed.),J.Wiley 1992
• R.J. Schalkoff: Digital Image Processing and Computer Vision, J. Willey & Sons 1989
• A.N. Netravalli, B.G. Haskell: Digital Pictures Representation, Compression and Standards. Plenum Press, 1995
• B. Furth, J. Greenberg, R. Westwater: Motion Estimation Algorithms for Video Compression. Kluwer Acad. Publishers, 1997
• A.K. Jain: Fundamentals of Digital Image Processing, Prentice Hall Int. Edit., 1989
• J. Jan: Digital Signal Filtering, Analysis and Restoration. IEE Publishing, London, UK, 2000,
• Gonzales, R.C., Wintz, P.: Digital Image Processing. 2nd ed. Addison-Wesley Publ. Comp. 1987
• Hlaváč, V., Šonka, M.: Počítačové vidění. Grada 1992
• V. Bhaskaran, K. Konstantinides: Image and Video Compression Standards (2nd ed.) Kluwer Acad. Publishers, 1997
• J.C. Russ: The Image Processing Handbook (3rd ed.), CRC Press and IEEE Press, 1999
• S.J. Sangwine, R.E.N. Horne: The Colour Image Processing Handbook. Chapman & Hall, 1998

1. Concepts of advanced image processing methods. Overview of the theory of 2D signals and 2D transforms, image as a realisation of a 2D stochastic field.
2. Discrete image representation, discrete linear and non-linear 2D operators, neural 2D filters.
3. Formalised image restoration - concepts, identification of deterioration and noise. Pseudoinversion, Wiener filtering via frequency domain.
4. Image restoration by constrained deconvolution method. Method of maximum entropy.
5. Generalised discrete LMS method, method of impulse response optimisation, approaches based on maximum posterior probability.
6. Image restoration by neural networks using iterative optimisation of network "energy", comparison with classical approaches.
7. Radon transform and projection tomography, image reconstruction from projections. Algebraic iterative methods of reconstruction.
8. Projection-slice theorem, reconstruction from projections via frequency domain. Image reconstruction by filtered back-projection. Generalisation of methods for fan-projections.
9. Disparity analysis and pair-wise image comparison. Movement analysis.
10. 3D surface reconstruction based on disparity analysis of stereo-pairs.
11. Formalised image segmentation, texture analysis, prior-knowledge based segmentation.
12. Object contour restoration, Hough transform. Morfological transforms.
13. Object recognition in images by means of learning neural networks, comparison with feature based recognition procedures using cluster analysis.

Study evaluation is based on marks obtained for specified items. Minimimum number of marks to pass is 50.

Doctoral course: discussions.