Becoming familiar with the basic principles in the field of computer vision and with advanced techniques of digital image processing; Becoming familiar with up-to-date methods in this field by working on several projects.

The overview of principles of modern computer vision methods. Student is able to understand the basic principles and methods used in computer vision, and can broaden their knowledge by working on a specific problem.

Visual system components: Image processing systems, computer vision signal processing, computer vision shape recognition, algorithm performance evaluation, types of tasks in computer vision. Sensors and image: radiation and illumination, optics, radiometry, sensors, geometric callibration, tridimensional vision. Signal processing and shape recognition: representation of multidimentional signals, environment operators, Movement, 3D algorithms, non-linear filter design, adaptive filtering and segmentation, morphological operators, probability models in computer vision, fuzzy signal processing, neural networks in signal processing. Computer vision projects: Object recognition using intelligent cameras, quality control in shipyards, topological maps of microstructures, fast 3D oject mapping, 3D plane reconstruction from the image sequence, movement mapping.

Lectures, computer practice, projects.