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Lecturer(s)
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Fribert Miroslav, Ing. Dr.
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Dobrovolný Martin, Ing. Ph.D.
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Mandlík Michal, Ing. Ph.D.
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Course content
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Introduction to image processing: Basic features of raster image. Sampling of 2D image. Linear image processing. Fundamental equation of linear image processing. Linear image transformations: 2D Fourier transform. Walsh-Hadamard transform. Cosine transform. Haar and Vawelet transform. Elementary images of linear image transformations. Statistical descriptions of images: Image as a random field. Statistical characteristics of the random field. Ergodicity of the random field. Karhunen-Loeve transform. Image preprocessing: Brightness scale transform. Equalization of the histogram. Geometric transform and interpolation. Filtering in the frequency space. Filtering in the space area. Nonlinear preprocessing. Image restoration problem: Simple degradations of image. Inverse filtering. Wiener restoration filter. Image segmentation: Thresholding. Region growing. Edge detection and edge operators. Image sharpening. Objects classification in images. Measurement in images: Brightness and color measurement. Measurement of feature size. Describing shape and distortion measurement. Determining location and orientation.
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Learning activities and teaching methods
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Monologic (reading, lecture, briefing), Skills training
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Learning outcomes
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The aim of the subject is the understanding of image processing fundamentals and familiarizing with technologies used in this realm (image digitalization, image transformation, image preprocessing, image restoration etc.). Further is it understanding of the objects features evaluation included in the image.
Ability of using of theoretical models in image processing applications
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Prerequisites
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Mathematics - matrix arithmetic, mathematical statistic
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Assessment methods and criteria
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Oral examination, Written examination
The subject includes the lectures and continued exercises. The exercises demonstrate the methods of image information processing using some math software (Matlab, Mathematica). The exercises are compulsory. Exam consists of two example calculation solved in the exercises and processing of two answers from theory.
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Recommended literature
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Forsyth, D. A., Ponce, J. Computer Vision - a Modern Approach. Prentice Hall, 1 .vydání, 2002. ISBN 0-13-085198-1.
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Fribert, Miroslav. Základy zpracování obrazu. Pardubice: Univerzita Pardubice, 2006. ISBN 80-7194-901-9.
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Hlaváč, Václav. Počítačové vidění. Praha: Grada, 1992. ISBN 80-85424-67-3.
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Klíma, Miloš. Zpracování obrazové informace. Praha: Vydavatelství ČVUT, 1996. ISBN 80-01-01436-3.
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Martišek, Dalibor. Matematické principy grafických systémů. Brno: Littera, 2002. ISBN 80-85763-19-2.
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Pratt, William K. Digital image processing : PIKS Inside. New York: John Wiley & Sons, 2001. ISBN 0-471-37407-5.
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Sonka, Milan. Image processing, analysis and machine vision. London: Chapman & Hall, 1993. ISBN 0-412-45570-6.
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Žára, Jiří. Moderní počítačová grafika. Praha: Computer Press, 1998. ISBN 80-7226-049-9.
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