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Visual Computing (SS 07) - Home


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Description Description | Administration



Goal

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.

Contents

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.

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


Administration Description | Administration

Lecturers

Prof. Dr. Markus Gross
Prof. Dr. Joachim Buhmann

Locations

Course:
Di. 11-12 CAB G61
Mi. 10-12 CAB G61
Attention: No lecture on April 24 and April 25, but the exercise lessons will take place


Attention: There will be an additional lecture on April 17, 14:15-16:00, in HG F3 AND on May 03, 09:15-10:00, in HG E3

 

Exercises:
Di. 14-16 CAB H52 or
Do. 8-10 CAB G56

Credits

3V / 2U

Exam

180 min, written (English, answers allowed in german or english), notes (8 pages A4 handwritten) allowed


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