Overview of goals and methods of image analysis and computer vision.
Introduction to biological vision and visual perception.
Basic image analysis: signal theory, filtering, image enhancement, image reconstruction, segmentation, classification, representation.
Basic computer vision: multi-scale representation, detection of contours and other features.
Perspective projection.
Illumination models. Texture. Stereo. Motion.
Object recognition.
After completing the course you will be able to:
- identify basic concepts, terminology, theories, models and methods in the field of computer vision, image analysis and image processing,
- describe known principles of human visual system,
- develop and systematically test different basic methods of computer vision, image analysis and image processing,
- experimentally evaluate different image analysis algorithms and summarize the results,
- choose appropriate image processing methods for image filtering, image restauration, image reconstruction, segmentation, classification and representation,
- describe basic methods of computer vision related to multi-scale representation, edge detection and detection of other primitives, stereo, motion and object recognition,
- build a toolbox for image processing consisting of methods for grey-level transformations, image filtering functions and methods for edge and corner detection,
- suggest a design of a computer vision system for a specific problem
in order to
- get acquainted with basic possibilities and constraints of computer vision, image processing and image analysis and therefore assess which problems can be solved in the field of robotics, medical and industrial image processing, processing of satelite images and similar,
- be able to implement, analyse and evaluate simple systems for automatic image processing and computer vision,
- have a broad knowledge base so to easily read the related literature.