Course: Image Analysis and Processing

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Course title Image Analysis and Processing
Course code KMI/AZO
Organizational form of instruction Lecture + Exercise
Level of course Master
Year of study not specified
Semester Summer
Number of ECTS credits 5
Language of instruction Czech
Status of course Compulsory-optional, Optional
Form of instruction Face-to-face
Work placements This is not an internship
Recommended optional programme components None
Lecturer(s)
  • Bartl Eduard, RNDr. Ph.D.
  • Trnečková Markéta, Mgr.
Course content
1. Image reconstruction - noise models: Gaussian noise, uniform noise - estimation of noise parameters - spacial domain filtering - frequency domain filtering - estimation the degradation function - Inverse filtering - Wiener filering - constrained least squares filtering 2. Wavelet transform - preliminaries: image pyramids, subband coding, Haar transform - discrete Wavelet transform - continuous Wavelet transform - fast Wavelet transform - Wavelet compression (JPEG 2000) 3. Mathematical morphology - dilation and erosion operators - closing and opening operators - applications: boundary extraction, convex hull, skeleton, ... - extension to grayscale images, fuzzy morfologie 4. Segmentation - point, line and edge detection - edge linking - tresholding - region-based segmentation 5. Image representation and description - representation: chain codes, polygonal approximations, signatures, ... - boundary descriptors: shape numbers, Fourier descriptors, statistical moments, ... - regional descriptors: topological descriptors, moments of two-dimensional functions, ... - use of principal components

Learning activities and teaching methods
Lecture, Demonstration
Learning outcomes
The students become familiar with basic concepts of image analysis and processing.
2. Comprehension Recognize principles of image analysis reconstruction and recognition.
Prerequisites
unspecified

Assessment methods and criteria
Oral exam, Written exam

Active participation in class. Completion of assigned homeworks. Passing the oral (or written) exam.
Recommended literature
  • Gonzales, R. C., Woods, R. E. (2002). Digital Image Processing. Prentice Hall.
  • Gonzales, R. C., Woods, R. E. (2004). Digital Image Processing Using Matlab. Prentice Hall.
  • Pratt, K. W. (2001). Digital image processing: PIKS inside. New York, Chichester, Weinhe, John Wiley and Sons.
  • Sojka, E. (2000). Digitální zpracování a analýza obrazů. VŠB-TU Ostrava.
  • Sonka M., Hlavac, V., Boyle R. (2008). Image Processing, Analysis and Machine Vision. Toronto.


Study plans that include the course
Faculty Study plan (Version) Branch of study Category Recommended year of study Recommended semester
Faculty of Science Applied Computer Science (1) Informatics courses 1 Summer
Faculty of Science Teaching Training in Computer Science for Secondary Schools (1) Pedagogy, teacher training and social care 1 Summer