UIDI006 Computer Vision and Image Analysis in Autonomous Systems

Faculty of Philosophy and Science in Opava
Summer 2016
Extent and Intensity
0/0. 0 credit(s). Type of Completion: dzk.
Guaranteed by
doc. Ing. Petr Čermák, Ph.D.
Institute of Computer Science – Faculty of Philosophy and Science in Opava
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 main goal of this doctoral course is to acquaint students with methods of image analysis and computer vision to practical development of intelligent systems.
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
    11. Image interpretation, declarative and procedural models and their comparison
    12. Reverse stereoprojection, camera model, two cameras case, absolute and relative calibration and reconstruction
    13. Analysis of time-variant images by Discrete Kalman Filter, application of Kalman filtering
    14. Objects tracking in images from moving camera, alpha and beta filter
    15. Motion detection, image substraction methods
    16. Ways to implementation image processing methods and analysis in DSP, FPGA and distributed systems
Teaching methods
Interactive lecture
Lecture with a video analysis
Assessment methods
Exam
Language of instruction
Czech
Further comments (probably available only in Czech)
The course can also be completed outside the examination period.
Teacher's information
* commisional exam
The course is also listed under the following terms Winter 2006, Summer 2007, Winter 2007, Summer 2008, Winter 2008, Summer 2009, Winter 2009, Summer 2010, Winter 2010, Summer 2011, Winter 2011, Summer 2012, Winter 2012, Summer 2013, Winter 2013, Summer 2014, Winter 2014, Summer 2015, Winter 2015, Winter 2016, Summer 2017, Winter 2017, Summer 2018, Winter 2018, Summer 2019, Winter 2019, Summer 2020, Winter 2020, Summer 2021, Winter 2021, Summer 2022.
  • Enrolment Statistics (Summer 2016, recent)
  • Permalink: https://is.slu.cz/course/fpf/summer2016/UIDI006