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Higher Education Technical Challenges Hub: Module Specification

ICT11M2 Image Processing

pdf version of module specification

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pdf version of module specification








Module name:

Image Processing

Scope and form:

This module deals with the application of main methods of image processing to complex digital images (textures). It covers the fundamentals in image analysis and fractal theory, algorithms and their practical application in different problems appearing in IT industry, Biology and Medicine. The aims of this module are to provide a basic understanding of image processing methods and give practical skills in design and implementation of the algorithms

Duration (weeks; Hours/week):

15 weeks; 4 h/week

Type of assessment:

Distributed evaluation with final exam. Practical labs are organized.

Qualified Prerequisites:

Basics in calculus, algebra, programming.

General module objectives:

The aim of this module is to develop a understanding of main methods of digital images processing and analysis and give practical skills to the application of these methods to analysis and classification of texture images.

Topics and short description:

Digital images and their representation. Palettes and transformation of images. Statistical texture methods.. The first and the second order stastistics. Haralick matrices and main features (energy, correlation, entropy, difference moment, inertia)
Morphological methods. Dilatation,erosion, closing. Detection of connectivity components. Skeleton construction.
Filtration and segmentation methods. Detection of points, lines, edges. Intensity threshold. Split and merge segmentation algorithm. K-mean clastering.
Fractal analysis methods. Digital images and grey level function. Blanket technique and calculation of fractal signature. Vector of fractal signatures as an image characteristic. Modified signature method and its application to images segmentation.
Multifractal methods. Generalized statistical sum and the spectrum of Regny dimensions. Multifractal spectrum and methods of calculations. Density function and level sets. Calculation of multifractal spectrum based on generalized statistical sum. Image segmentation based on density function calculation.

Learning outcomes:




The fundamentals of digital images representation and transformation

Able to comprehend main color palettes and transformation from one to another

Able to select the most appropriate image representation

The fundamentals of morphological methods, filtration and segmentation

Able to implement algorithms for main methods of filtration and segmentation

Able to comprehend the applicability of morphological  methods and filtration to different sorts of images

The fundamentals of fractal dimensions and fractal analysis methods

To design and implement fractal analysis algorithms to classify images with complex structures  

Students have to be able to select, design and implement the appropriate method of analysis

Recommended literature:

Topic 1
R. Haralick, K. Shanmugam, I. Dinstein. Textural Features for Image Classification. IEEE Transactions on Systems, Man and Cybernetics, No. 6, pp. 610-621, (1973).
R. Haralick. Statistical and Structural Approaches to Texture. Proceedings of the IEEE, Vol. 67, No. 5, pp. 786-804, (1979).
M. Tuceryan, A. Jain. Texture analysis. The Handbook of Pattern Recognition and Computer Vision, 2nd Ed., by C. Chen, L. Pau, P. Wang (eds.), World Scientific Publishing Co., pp. 207-248, (1998).
Topics 2, 3
R. Gonzales, R.Woods. Digital image processing. Prentice Hall, 2008.
N. Pal, S. Pal. A Review on Image Segmentation Techniques. Pattern Recognition,Vol. 26, No. 9, pp. 1277-1294, (1993).