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Lecturer(s)
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Dobrovolný Martin, 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 of objects features 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), Dialogic (discussion, interview, brainstorming)
- Participation in classes
- 52 hours per semester
- Home preparation for classes
- 30 hours per semester
- Preparation for an exam
- 30 hours per semester
- Contact teaching
- 8 hours per semester
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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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DOBEŠ, Michal. Zpracování obrazu a algoritmy v C#. Praha: BEN ? technická literatura, 2008. ISBN 978-80-7300-233-6.
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Fribert, Miroslav. Základy zpracování obrazu. Pardubice: Univerzita Pardubice, 2006. ISBN 80-7194-901-9.
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Fribert, Miroslav. Základy zpracování obrazu. Pardubice: Univerzita Pardubice, 2013. ISBN 978-80-7395-534-2.
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HLAVÁČ. Zpracování signálů a obrazů. ČVUT, 2006. ISBN 978-80-01-04442-1.
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KLÍMA, Miloš. Zpracování obrazové informace. ISBN 80-010-1436-2.
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Klíma, Miloš. Zpracování obrazové informace. Praha: Vydavatelství ČVUT, 1996. ISBN 80-01-01436-3.
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KUMAR, N. Suresh, Arun Kumar SANGAIAH, M. ARUN a S. ANAND. Advanced Image Processing Techniques and Applications. IGI Global. Advances in Computational Intelligence and Robotics, 2017. ISBN 9781522520535.
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