FPF:UIDI006 Computer Vision and Image Anal - Course Information
UIDI006 Computer Vision and Image Analysis in Autonomous Systems
Faculty of Philosophy and Science in OpavaSummer 2014
- 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
- Autonomous Systems (programme FPF, P1801 Inf)
- 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
- 1. Mathematical description of continuous and digital image, physiological and psychological aspects of vision
- Language of instruction
- Czech
- Further Comments
- The course can also be completed outside the examination period.
- Teacher's information
- * commisional exam
- Enrolment Statistics (Summer 2014, recent)
- Permalink: https://is.slu.cz/course/fpf/summer2014/UIDI006