Palacký University Olomouc
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for academic year 2024/2025
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Course: Digital Image Processing

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Course title Digital Image Processing
Course code KEF/DZO
Organizational form of instruction Lecture
Level of course Master
Year of study not specified
Semester Winter
Number of ECTS credits 2
Language of instruction Czech
Status of course Compulsory-optional
Form of instruction Face-to-face
Work placements This is not an internship
Recommended optional programme components None
Lecturer(s)
  • Bartoněk Luděk, doc. Ing. Ph.D.
Course content
1. Relationship of digital image processing to other related disciplines , overview , types, distribution. 2. Video signals. A/D - Tv signals, types of color formats, CCD detectors (1D-2D sto-ry ), scanner, grabber. 3.Digitální image. Image matrix, image files , image analyzer DIPS . 4. Processing image files. Descriptive statistics ? histogram , cum . Histogram The basic operation of the image matrix (Add , Subtract , Difference , Multiply , Divide , Lin . Combination, then ... If probabilistic image operations , logic operations with images (Lu - kaszewiczova logic) , blending images , filters , convolution matrix . 5.Spektrální image processing . Discrete basis functions , general disktrétní Fourier transform ( DFT), Fast Fourier Transform (FFT) to reduce the time , frequency , algorithm , program , inverse DFT. 2D FFT functions in the analysis of the image matrix. 6. Methods measurement and recognition of objects in the image. a) Analysis of the geometry and the physical shape of the object ( DIPS ) . Coordinate measuring . Measuring distances and angles. Geometric transformations of scale. Analysis of geometric shapes (area, center of gravity, moments (main , central), Legendre ellipse , elongation , dispersion , ex - tension , perimeter, shape factor , orientation . b) Feature Recognition . Discriminant function , the criterion of minimum distances of the minimum error parameter estimation method, the cluster analysis . Structural methods, choice of primitives , a description of formal languages ??, grammars , automata, syntactic analysis . c ) Neural Networks . Applications practical demonstration.

Learning activities and teaching methods
Lecture
Learning outcomes
In this course, the students become acquainted with usages of computers in measurements and analysis of image information acquired from one-dimensional and two-dimensional CCD sensors. The first part of the course is devoted to a study of relation of digital image processing to other related disciplines, to digitalization of analog signals and to description of typical CCD sensors. The second part of the course is then focused on processing of image matrix and its geometrical, statistical and spectral evaluations. The attention is also concentrated on methods of recognition (symptomatic and structural) and analysis of geometrical shapes in the image.
The course focuses on the acquisition of knowledge in the field of digital image processing and description of typical CCD sensors. Image processing in the form of a matrix geometric, statistical and spectral evaluation. Methods of detection and analysis of geometric shapes in the image.
Prerequisites
Knowing the basics of programming and an interest in the issue.

Assessment methods and criteria
Mark

Knowledge within the scope of the course topics (examination)
Recommended literature
  • Dobeš, Michal. (2008). Zpracování obrazu a algoritmy v C#. BEN.
  • Gonzales RC et al. (2004). Digitální zpracování obrazu pomocí MATLAB. Prentice Hall.
  • Hlaváč, V., Šonka, M. (1992). Počítačové vidění. Grada a.s. Praha.
  • Kotek, Z., Mařík, V. (1993). Metody rozpoznávání a jejich aplikace.. Academia Praha.
  • K.R. Castleman. (1996). Digital Image Processing. Prentice-Hall.
  • Pratt WK. (2001). Digitální zpracování obrazu (3rd ed). John Wiley, New York.
  • Sojka, Eduard. (2000). Digitální zpracování a analýza obrazů. VŠB - Technická univerzita.
  • Šonka, Hlaváč, Boyle. (1998). Image Processing, Analysis and Machine Vision. PWS.


Study plans that include the course
Faculty Study plan (Version) Category of Branch/Specialization Recommended year of study Recommended semester
Faculty: Faculty of Science Study plan (Version): Applied Computer Science - Specialization in Computer Systems and Technologies (2024) Category: Informatics courses 2 Recommended year of study:2, Recommended semester: Winter
Faculty: Faculty of Science Study plan (Version): Applied Computer Science - Specialization in Software Development (2024) Category: Informatics courses 2 Recommended year of study:2, Recommended semester: Winter
Palacký University Olomouc, date of update: 30.04.2025 23:58. Data created for academic year 2024/2025