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This course provides an in-depth introduction to the core concepts of computer graphics, computer vision, and machine learning. The course forms a basis for the specialization track Visual Computing of the CS master program at ETH.
We will cover a broad spectrum of fundamental concepts of computer graphics, computer vision, image processing, and machine learning.
Course topics will include: Graphics pipeline, perception and color models, camera models, transformations and projection, projections, lighting, shading, global illumination, texturing, sampling theorem, Fourier transforms, convolution, linear filtering, diffusion, nonlinear filtering, edge detection, shape from X, stochastic image models, Bayes decision theory and classification, support vector machines, dimensionality reduction, clustering, Bayes nets.
In theoretical and practical homework assignments students will learn to apply and implement the presented concepts and algorithms.
A scriptum will be handed out for a part of the course. Copies of the slides will be available for download. We will also provide a detailed list of references and textbooks.
Markus Gross: Computer Graphics, scriptum, 1994-2005
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