• Course code:63267
  • Credits:6
  • Semester: winter
  • Contents
Machine perception is a rapidly developing exciting field with a wealth of applications available as well as those still to come. This course will cover in depth the mathematics and basic techniques of computer vision which are widely used in a broad spectrum of modern applications. If you have ever wondered what kind of methods devices like Google glasses, Robotic vehicles, Panorama stitching, Photo editing software, etc., use, this course will address that curiosity and more. At the end of this course, the student is expected to have a grasp in the following topics: (i) Basic image processing techniques, (ii) Image derivatives and edges, (iii) Model fitting, (iv) Local descriptors, (v) Stereo vision, (vi) Subspace methods for recognition, (vii) Object detection, (viii) Object recognition, (ix) Basics of motion. The course is composed of (i) the lectures in which we will cover the relevant theory and (ii) exercises in which the students will implement the basic techniques and solidify the theory.
  • Study programmes
  • Distribution of hours per semester
45
hours
lectures
20
hours
laboratory work
10
hours
tutorials
  • Professor
Teaching Assistant
Room:R2.37-LUVSS