Computer Graphics Laboratory

Goal

This course provides an in-depth introduction to the core concepts of computer graphics, image processing, multimedia, computer vision and machine learning. The course forms a basis for the specialization track Visual Computing of the CS master program at ETH.

Subjects

As a prerequisite to the specialization track Visual Computing, this course covers topics from computer graphics, computer vision, and machine learning. More information on these three fields can be found on the respective group websites:

• Computer Graphics: Computer Graphics Lab
• Computer Vision: Computer Vision and Geometry Group
• Machine Learning: Pattern Analysis and Machine Learning Group

Contents

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, image representations, convolution, linear filtering, diffusion, nonlinear filtering, edge detection, optical flow, image and video compression, Bayes decision theory and classification.

In theoretical and practical homework assignments students will learn to apply and implement the presented concepts and algorithms.

Script

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.

Literature

Markus Gross: Computer Graphics, scriptum, 1994-2005