UINA357 Computer Vision

Faculty of Philosophy and Science in Opava
Summer 2018
Extent and Intensity
2/2/0. 6 credit(s). Type of Completion: zk (examination).
Teacher(s)
doc. Ing. Petr Čermák, Ph.D. (lecturer)
doc. Ing. Petr Čermák, Ph.D. (seminar tutor)
Guaranteed by
doc. Ing. Petr Čermák, Ph.D.
Institute of Computer Science – Faculty of Philosophy and Science in Opava
Prerequisites (in Czech)
UINA327 Image Analysis and Recognition
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
Computer Vision is a course which is closely related with robotics and image analysis. The course is focused on successful analysis of input image followed by desired reaction. During the course are discussed practical applications of the mostly used computer vision algorithms in C#, C++ and also in MATLAB with Image Processing Toolbox.
Syllabus
  • 1. Image pre-processing. Practical implementation of the following algorithms:
    a. conversion color 24bit RGB image into 8bit grayscale (C#), different approaches
    b. convolution filters demos, e.g. edge detection, edge enhancing, blur, sharpening, relief and other filters
    2. Segmentation and feature recognition. Practical implementation:
    a. Quadtree decomposition (C#)
    b. Global thresholding (C#)
    c. Line finding (C#)
    3. Reverse stereoprojection, camera model, two cameras case, absolute and relative calibration and reconstruction
    4. Analysis of time-variant images by Discrete Kalman Filter
    5. Objects tracking in images from moving camera
    6. Motion detection, image substraction methods
    7. Environment model estimation
    8. Optical flow. Practical implementation of the following algorithms:
    a. Lucas-Kanade algorithm to motion detection (MATLAB)
    9. Interest point detection, SURF/SIFT method comparison (C++)
Literature
    required literature
  • Haralick Shapiro. Computer vision. New York. info
  • SOJKA, E. Zpracování digitálního obrazu. 2000. ISBN 80-7078-746-5. info
  • HARALICK, R. M. , SHAPIRO, L. G. Computer and Robot Vision. New York, 1992. info
    recommended literature
  • Čermák, P., Blahuta, J., Martinu, J. Počítačové vidění. Opava, 2013. URL info
  • 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
  • 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)
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 computer vision 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 Summer 2019, Summer 2020, Summer 2021, Summer 2022, Summer 2023, Summer 2024, Summer 2025.
  • Enrolment Statistics (Summer 2018, recent)
  • Permalink: https://is.slu.cz/course/fpf/summer2018/UINA357