Introduction to learning-based computer vision; statistical learning background; image processing and filtering primer; convolutional neural networks (CNNs), network layers, computer vision data sets and competitions; computer vision problems, in particular, image classification, detection and recognition, semantic segmentation, image generation, multi-view problems and tracking.
SyllabusIntroduction to learning-based computer vision; statistical learning background; image processing and filtering primer; convolutional neural networks (CNNs), network layers, computer vision data sets and competitions; computer vision problems, in particular, image classification, detection and recognition, semantic segmentation, image generation, multi-view problems and tracking.
SyllabusPresentation of the major programming paradigms: object-oriented, imperative, logic, functional. Related programming languages, their essential properties and typical applications. Programming in imperative, logic and functional languages. Influence of programming paradigms on problem solving and program design strategies. An overview of other paradigms, such as constraint-based, rule-based and event-driven programming.
Differences between C++ and Java programming. C++ data types. Pointers and memory management. Object oriented programming in C++. File and stream I/O. Preprocessor macros. Templates and the Standard Template Library. Numerical computation in C++. Interfacing with hardware. Engineering applications.
Interactive computer graphics. Display data structures and procedures. Graphics pipeline. Geometric transformations. Viewing in three dimensions. Illumination and color models. Object modelling in 2D and 3D.
Basic concepts. Virtual worlds. Hardware and software support. World modeling. Geometric modeling. Light modeling. Kinematic and dynamic models. Other physical modeling modalities. Multi-sensor data fusion. Anthropomorphic avatars. Animation: modeling languages, scripts, real-time computer architectures. Virtual environment interfaces. Case studies.
Principles and advanced techniques in rendering and modelling. Research field overview. Splines, subdivision surfaces and hierarchical surface representations. Physics of light transport, rendering equation and Bidirectional Reflectance Distribution Function. Classical ray tracing, radiosity, global illumination and modern hybrid methods. Plenoptic function and image-based rendering.