Introduction to fundamental computer vision concepts and advanced image processing techniques. Realization and implementation of different methods and technical systems of computer vision, through independent work on projects. Pattern recognition and machine learning in computer vision. Types of tasks in computer vision. Problems of detection and estimation, tracking, recognition, optimal reasoning, segmentation, signal reconstruction and enhancement, analysis and synthesis of images. Understanding of special image acquisition systems. Design and analysis of image and video processing systems. Processing of multidimensional image signals. Features design, global and local image descriptors, optimal (learned) signal representations. Fundamentals of three-dimensional vision. Understanding of different methodologies for performance evaluation of computer vision algorithms and comparison of their characteristics. Analysis and application of different models of shallow and deep neural networks (DNN) in computer vision tasks. Introduction to programming tools that are designed for computer vision and dedicated hardware platforms. The rest of the course content can vary to some extent depending on participants’ interest. Introduction to fundamentals of photogrammetry. Fundamentals of remote sensing. Dedicated hardware platforms for real-time computer vision applications. Detection and recognition of different objects, processes and phenomena in images and video. Problem of segmentation and tracking of moving objects in video. Morphological operators. Application of variational methods in computer vision. Reconstruction and restoration of image and video. Modern techniques for solving inverse problems in computer vision.