UINA327 Image Analysis and Recognition

Faculty of Philosophy and Science in Opava
Winter 2024
Extent and Intensity
2/2/0. 6 credit(s). Type of Completion: zk (examination).
Teacher(s)
Ing. Jiří Blahuta, Ph.D. (lecturer)
RNDr. Jiří Martinů, Ph.D. (seminar tutor)
Guaranteed by
Ing. Jiří Blahuta, Ph.D.
Institute of Computer Science – Faculty of Philosophy and Science in Opava
Timetable
Mon 11:25–13:00 LEI
  • Timetable of Seminar Groups:
UINA327/A: each even Friday 13:05–16:20 LEI, J. Martinů
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
Course objectives
The course called Image Analysis and Recognition is focused on digital image processing, objects classification of the digital image and understanding of the digital images. The first part of the course is devoted to ways of digital image acquisition and its digital representation. Principal chapters are focused on all phases of image processing - from pre-processing, filtration to segmentation followed by understanding by means of classifiers with feature-based analysis of the segmented image data.
Learning outcomes
After completing the course, the student will know how to preprocess an image, what a histogram is, why a filter is used, when to use segmentation, analyze an image over time, use a discrete Kalman filter. How to analyze and track movement with a camera or multiple cameras, use optical flow. Know what is roi area.
Syllabus
  • 1. Mathematical description of continuous and digital image, physiological and psychological aspects of vision
    2. Mathematical model of gray-scale and color vision, image types and devices for their acquisition
    3. Linear operators, image adding and convolution, cyclic convolution, boundary conditions
    4. Sampling, aliasing, antialias filtering, image reconstruction, examples of reconstruction filters
    5. Numerical noise filtering, image sharpening and edge sharpening, fuzzy filtration, histogram and its equalization
    6. Image segmentation by binary thresholding and adaptive thresholding, boundary tracking, segmentation by Region Growing and Region Splitting and Merging, segmentation by pattern matching, segmentation by fuzzy rule-based system
    7. Mathematical and fuzzy mathematical morphology, homotopic tree, skeletonization, dilation, erosion, opening
    8. Hough transform, line approximation and approximation of circle
    9. Features detection, global and local features, features from pixel intensity, boundary description, Euler´s number, texture-based features, polygonal representation
    10. Regions and their description, regions indexing, scalar description, moments, features evaluation, independency towards image transforms
    11. Pattern recognition, statistical feature-based methods, classifiers settings, clustering, recognition by etalons, neuro and fuzzy-neuro classifiers
Literature
    required literature
  • SOJKA, E. Zpracování digitálního obrazu. 2000. ISBN 80-7078-746-5. info
    recommended literature
  • SZELISKI, Richard. Computer Vision: Algorithms and Applications. Berlin, 2010. info
  • DOUGHERTY, G. Digital Image Processing for Medical Applications. Oxford, 2009. ISBN 978-0521860857. info
  • ŽÁRA, Jiří, SOCHOR, Jiří, FELKEL, Petr, BENEŠ, Bedřich. Moderní počítačová grafika. Brno, 2005. ISBN 978-80-2510-454-5. info
  • SCHLESINGER, M.I., HLAVÁČ, V. Deset přednášek z teorie statistického a strukturního rozpoznávání. Praha, 1999. info
  • ŠONKA, M., HLAVÁČ, V., BOYLE, R. Image Processing, Analysis and Machine Vision. Boston, 1998. info
  • HARALICK, R. M. , SHAPIRO, L. G. Computer and Robot Vision. New York, 1992. info
  • PRATT, W. K. Digital Image Processing, Second Edition. New York, 1991. info
Teaching methods
Interactive lecture
Lecture with a video analysis
Assessment methods
Exam
Language of instruction
English
Further comments (probably available only in Czech)
Study Materials
The course can also be completed outside the examination period.
Teacher's information
* 75% attendance in the lecture and exercises, active participation
* written test in the extent of the given literature and the content of seminars - success rate 50% of 30 points
* implementation of selected methods of image analysis on selected robot, success rate 50% of 30 points from programming and 10 points from documentation
* 40 points exam, sucess rate 50%
The course is also listed under the following terms Winter 2017, Winter 2018, Winter 2019, Winter 2020, Winter 2021, Winter 2022, Winter 2023.
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