The courses offered by the VIPS lab for the Laurea degree in Computer Science of the University of Verona are the following:

Computational Geometry

The course consists in an introduction to projective geometry, steered towards the applications to vision and drawing problems, via a hybrid appoach: analytic (via coordinates and matrices), and synthetic.
The Laboratory module aims at illustrating the foundations of computational geometry, describing its main problems and algorithms.
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Computer Graphics

This course aims at providing the student with the tools needed to master the algorithms and computational methods upon which many interactive computer graphics applications are based. The focus is on understanding the theory (geometry, radiometry) and the computational issues (algorithms, programming) that lie behind computer generated images.
The Laboratory module introduces to OpenGL programming.


Computer Vision

The course aims at providing the student with the theoretical and practical tools to tackle the problem of recovering the 3D structure of a scene starting from its 2D projections, the images. The focus is on the geometry of the problem. The methods will be discussed in sufficient detail to allow the student to implement them on a computer. The Laboratory module consists in MATLAB implementation of the algorithm described in the theory module with case study drawn from the real world.
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Human Computer Interaction and Multimedia

The course supply with the theorical and technical basis for images creation and processing, as well as their usage in visual interface context. It can be divided in three sections: first section (human-computer interaction) describes the fundamentals in terms of human factor, projecting theories, interaction styles, access peripherals and windows systems, with applications regarding hypertext, hypermedia, world wide web and VRML language. Second section (image processing) describes image creation techniques and most significant filtering and low-processing algorithms. Third section (computer graphics) describes mathematical fundamentals for graphic object visualization, including virtual reality oriented examples.


Image Processing

The course means to supply with image processing basis theory and fundamental methodologies. Starting from image acquiring, filtering and analysis tecniques are described, aiming to quality improving, image recontruction, information extraction and images trasmission. Phisics and mathematical knowlegde is required, while systems theory basic notions are recommended.


Pattern Recognition

The course means to provide the theoretical basis of the main methods for the analysis and the authomatic recognition of patterns. Pattern recognition is used in diverse fields and applications, such as image processing, computer vision, artificial intelligence, bioinformatics, robotics, speech recognition, large-scale learning, database and more. During the course, probabilistic models and statistical analysis is presented with particular focus on the automatic learning of patterns for recognition and classification.
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Link Course Page (Patter Recognition for Bioinformatics)


Sound Processing

The class gives the basic theoretical fundamentals and some methods for the generation and processing of sound. Sound processing is important for designing multimedia systems. The auditory channel is always open and able to discriminate or integrate acoustic events. Vice versa, the visual system is suited for detecting and organizing spatial information. This gives importance to audio-visual communication in contemporary computer science. After a short introduction to psycho-acoustics, the main techniques for analyzing, processing, and synthesizing sounds by computer are presented. The course gives the tools and fundamental techniques for understanding and manipulating sounds for the purpose of creating interfaces, compressing and transmitting information, analyzing scenes, etc. The Laboratory module is an experimental complement of the theoretical lectures on sound generation and processing.



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Last revision: Jan. 2012