Course: Introduction to Image Analysis

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Course title Introduction to Image Analysis
Course code KMA/OBR
Organizational form of instruction Lecture + Exercise
Level of course Bachelor
Year of study 3
Semester Winter
Number of ECTS credits 4
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)
  • Machalová Monika, Ing.
  • Trnečková Markéta, Mgr. Ph.D.
Course content
unspecified

Learning activities and teaching methods
unspecified
Learning outcomes
Prerequisites
unspecified

Assessment methods and criteria
unspecified
Recommended literature
  • Gonzales, R. C., Woods, R. E. (2017). Digital Image Processing. Prentice Hall.
  • Hughes, J. F., van Dam, A., McGuire, M., Sklar, D. F., Foley, J. D., Feiner, S. K., Akeley, K. (2013). Computer Graphics: Principles and Practice. 3rd Edition; Pearson Education.
  • Manish Kashyap. (2025). Digital Image Processing Using Python: A comprehensive guide to the fundamentals of digital image processing. BPB Publications.
  • Solem, J. E. (2012). Programming Computer Vision with Python: Tools and Algorithms for Analyzing Images. Oreilly Media.
  • Steve Marschner, Peter Shirley. (2015). Fundamentals of Computer Graphics 4th Edition. A K Peters/CRC Press.
  • Žára, J., Beneš, B., Sochor, J., Felkel, P. (2004). Moderní počítačová grafika, 2. vyd. Brno, Computer Press.


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 Mathematics - Specialization in Industrial Mathematics (2020) Category: Mathematics courses 3 Recommended year of study:3, Recommended semester: Winter
Faculty: Faculty of Science Study plan (Version): Applied Mathematics - Specialization in Business Mathematics (2021) Category: Mathematics courses 3 Recommended year of study:3, Recommended semester: Winter
Faculty: Faculty of Science Study plan (Version): Applied Mathematics - Specialization in Data Science (2020) Category: Mathematics courses 3 Recommended year of study:3, Recommended semester: Winter