Image Processing and Analysis

Upon successful course completion students will be able to: – understand image formation, acquisition and representation and the role of basic preprocessing techniques, – understand the contemporary methods for image processing, enhancement and restoration, – use and apply image segmentation techniques, and techniques for representation and description of shape and texture, – understand object tracking techniques, – asses and evaluate the applicability of image processing and analysis techniques for solving practical problems, – follow and understand new developments in image processing and analysis and to asses their applicability – develop and describe evaluation criteria for image processing and analysis systems.

Digital images and their properties. Data structures for image processing and analysis, Image preprocessing: intensity transformations, geometric transformations, local preprocessing, restoration. Advanced techniques for image segmentation: intensity thresholding, region borders detection. Shape representation and description: region identification, contour based representation and shape description, region based shape representation and description, shape classes. Object recognition: knowledge representation, statistical and syntactic pattern recognition, optimization techniques in recognition. Mathematical morphology: basic principles and morphological transformations. Texture: statistical texture description, syntactic  texture description, hybrid methods, recognition methods. Motion analysis: differential methods, optical flow, points-of-interest based motion analysis.

Combined learning methods: lectures, supported by presentations and concepts visualizations, active participation of students through project works.