The course objective is to introduce the principal building blocks of a robot vision system and the associated fundamental problems, to introduce the main concepts and techniques used to solve those problems, to enable students to create robot vision systems and implement problem solutions, and to enable students to understand the basic methodology discussed in the robot vision literature. The main topics are visual perception (light, human vision, cameras, illumination, image quality parameters, sampling and quantization, visualization, image formats and standards), digital image processing and restoration (smoothing and sharpening, statistical and morphological filtering, image resampling, geometrical transformations and registrations), robust recognition of 2D objects (keypoints, lines, circles, and template detectors, 3D model alignment to 2D images, basics of unsupervised and supervised object detection, solution in case of partial object occlusion in the image), calibration of imaging systems (distortions of real optical systems, accuracy and precision, spatial homogeneity, temporal stability, self-calibration), 3D object reconstruction (review of techniques for depth perception from 2D images, concepts of systems and methods for stereo vision, structured light and photometric stereo), visual navigation (concepts of methods for image-based object tracking and motion analysis, pose estimation, and object localization and environment mapping from 2D images), and applications of robot vision (visual quality control, product sorting, object and obstacle detection, modelling of object shape and appearance, motion trajectory planning). Theoretical foundations, concepts and exemplar use cases are given during lectures, while practical skills are gained through lab works, weekly labwork assignements and an individual seminar work.
