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Module Descriptor School of Computer Science and Statistics

Module CodeCS7GV1
Module NameComputer Vision
Module Short Title
ECTS5
Semester TaughtHT
Contact Hours

2 lecture hours per week

Module PersonnelProfessor Aljosa Smolic
Learning Outcomes

On successful completion of this module, students will be able to:

  • Review and asses advanced computer vision technologies
  • Present and discuss state-of-the-art computer vision algorithms
  • Implement, test, evaluate and report exemplary computer vision solutions
Learning Aims

This course is an advanced master class in computer vision. It does not intend to teach fundamentals, but focuses on latest research. Guest lecturers will present leading edge research from various hot areas of computer vision. Students will get direct exposure to high class scientists and their research. In their own work, students will select each a recent paper and present it to the class. Further, they will be asked to execute small projects to explore selected state-of-the-art computer vison technology.

Module Content

Specific topics addressed in this module may include:

  • Deep learning
  • Segmentation, keying, matting
  • Visual saliency and attention modelling
  • High dynamic range imaging
  • Light field technologies
  • Augmented, virtual, mixed reality
  • Free viewpoint video
  • 3D reconstruction
  • Visual effects
  • Colour transfer
  • Optical flow
  • Object detection and recognition
Recommended Reading List

Fundamentals of computer vision in any form, e.g.

Computer Vision: Algorithms and Applications, Richard Szeliski, September 3, 2010 draft, 2010 Springer

http://szeliski.org/Book/drafts/SzeliskiBook_20100903_draft.pdf

Multiple View Geometry in Computer Vision

Second Edition

Richard Hartley and Andrew Zisserman,

Cambridge University Press, March 2004.

http://www.robots.ox.ac.uk/~vgg/hzbook/

Module Prerequisites
Assessment Details

100% coursework:

50% Seminar presentation

50% Project

 

Assessment in the Supplemental session will be based on 100% coursework.

Module Website
Academic Year of Data