The lectures are given by Karen Egiazarian (karen <dot> egiazarian <at> tut <dot> fi).
Time and place: Mondays at 12:15 – 14:00 in TB 219 and Wednesdays 14:15 – 16:00 in TB 222
For more details and weekly assignments, check the classroom exercises webpage.
Will be updated soon
· In-dept view of selected topics of image processing
· Practical tasks in image processing laboratory
· Basic or introductory signal processing
· Digital Image Processing I or Digitaalinen kuvankäsittely I
· Pre-required for the course Digital Image Processing III
No exam.
Final mark is computed based on four units as follows:
· Classroom exercises, 40% of the mark
· First laboratory work, 10% of the mark
· Second laboratory work, 20% of the mark
· Third laboratory work, 30% of the mark
All four units must be passed otherwise final mark is not given
Wavelets and Multiresolution Processing, 4h.
· Background: image pyramids, subband coding, Haar transform
· Multiresolution: series expansion, scaling functions, wavelets
· Fast wavelet transform, Mallat’s algorithm, lifting scheme
· Curvelets
For extra reading - presentation1b (pdf)
Image Compression, 4h.
· Fundamentals: coding redundancy, psycho-visual redundancy, fidelity criteria
· Source and channel encoding
· Information theory elements: information measures, coding theorems
· Loss-lees compression: variable-length coding, bit-plane coding, predictive coding
· Lossy compression: predictive coding, transform coding
Image Compression (cont.), 4h.
· Lossy compression: wavelet coding
· Compression of the wavelet coefficients: EZT, SPIHT, EBCOT algorithms
· Standards: JBIG, JPEG, JPEG2000, MPEG
Object Recognition, 1h.
· Patterns and pattern classes
· Decision-theoretic recognition methods: matching, optimum statistical classifications, neural networks
· Structural methods: matching shape numbers, strings and trees
presentation3a (pdf); presentation3b (pdf)
Image Segmentation, 4h.
· Detection of discontinuities
· Edge linking, boundary detection, thresholding
· Region-based segmentation
· Motion-based segmentation
Local Approximations in Image Processing, 4h.
· Local polynomial approximation
· Anisotropic nonparametric image restoration
· Image denoising
· Image deblurring
Material presentation5a (first 40 slides) ; presentation5b
Further reading :
http://www.cs.tut.fi/~lasip/papers/LPA-ICI_Book-TICSP_Series_no_19-2003.pdf
http://www.cs.tut.fi/~foi/papers/Foi-Anisotropic_non_parametric_image_processing-2005.pdf
Non-local Imaging
Given by Prof. Vladimir Katkovnik, 4h.
presentation 6 (pdf) (slides 1–16 and 52–227)
Image Sampling and Interpolation
Introduction to laboratory works
[1] R. Gonzalez and R. Woods, "Digital Image Processing, 2nd ed.", Prentice-Hall, 2002.
[2] John Russ, “The Image Processing Handbook, Fourth Edition”, CRC Press, 2002
[3] Y. Shi, H. Sun, “Image and Video Compression for Multimedia Engineering: Fundamentals, Algorithms, and Standards”, CRC Press, 1999
[4] K. Rao and P. Yip, “The Transform and Data Compression Handbook”, CRC Press, 2001
[5] V.Katkovnik, K.Egiazarian, and J.Astola, “Local Approximations in Signal and Image Processing”, SPIE Press, vol. PM157, 2006, 576 pages
Last modified: Friday, 11 February 2011, 14:31 EET