ETH Zurich - D-INFK - IVC - Teaching - Master Program

Master Program

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The digital processing of visual information has become a core topic in modern CS and IT. Visual Computing (VisComp) builds upon foundations from Computer Science and applied Mathematics and has a wide range of applications. Methodologically, VisComp is routed in computer graphics, algorithmic geometry, image processing and computer vision as well as machine learning. Given the importance and international standing of VisComp we believe that our students should have an opportunity to acquire fundamental knowledge via a new major.

Aims and Scope Motivation | Aims and Scope | More Information

The proposed major will acquaint students with core knowledge in visual information processing and learning. We will cover the most important algorithmic and systems oriented foundations and form a basis for more specialized courses in computer graphics, digital image processing and machine learning. Topics will include:

a) Digital signal representation, sampling theorem
b) Grey value, color and vector quantization
c) Color spaces
d) Transformations and projections
e) Projective geometry, homogeneous coordinates
f) Camera models
g) Calibration
h) Lighting and shading
i) Texture mapping
j) Scan conversion
k) Graphics APIs and graphics hardware
Computer Vision
a) Digital image processing: image restoration
b) Image data formats:
  • Laplace-Gauss pyramid
  • wavelets
  • nonlinear diffusion
c) Edge detection
d) Image segmentation
e) Optical flow
f) Stereo vision
g) Shape from X
h) 2d & 3d Form models
i) Statistic image models, MRFs
j) Object recognition
k) Texture
Advanced Rendering
a) Global illumination
  • ray tracing
  • radiosity
  • stochastic methods
b) Rendering of point models, surfels
c) Image-based rendering
d) Level-of-detail rendering
e) Hierarchical texturing
3D Modeling and Geometry Processing
a) Parametric curves, Bézier curves, B-splines, NURBS
b) Tensor product surfaces, triangle meshes
c) Mesh fairing
d) Subdivision methods
e) Point models
f) Convex hulls, Delaunay triangulations
g) Spatial search structures
h) Physics-based modeling
Physically-based Simulation
a) Mass-spring models
b) Particle systems
c) Rigid body dynamics
d) Finite element method
e) Collision detection
f) Fluid simulation
g) Applied PDEs
Scientific Visualization
a) Contouring and isosurfaces
b) Direct volume rendering
c) Streamline integration
d) Texture advection
e) Flow topology
f) Feature extraction
g) Tensor field visualization
h) Information visualization
Supervised Learning
a) Bayesian decision theory
b) Classifier design:
  • knn classifiers
  • perceptron learning
  • support vector machines
  • LDA
  • neural networks
  • boosting
c) Graphical models:
  • belief propagation
  • variational methods
  • hidden Markov models
d) Regression
e) Statistical learning theory (COLT)
Unsupervised Learning
a) Custering and mixture models
b) Markov random fields.
c) reinforcement learning
d) maximum entropy inference
e) Neuroinformatics:
  • organization principles of neural systems
  • dynamics of neurons and neural assemblies
  • synaptic plasticity
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