Contents: In this course, we teach the fundamentals of Computer Vision.

 

Time and Location

  • Wednesday, 10:15 to 11:45, Room: B 6, Bauteil E-F, A211
  • Thursday, 12:00 to 13:30, Room: B 6, A204, starting February 22nd

Instructor

Final mark

  • exam

Participation 

  • The course is open to students of the Master Business Informatics and Mannheim Master in Data Science (MMDS). 
  • The course is restricted to 30 participants.
  • Places are assigned on first come/first serve basis.
  • Students register for the course by email to me.

Requirements

  • Basics in linear algebra are beneficial for the lecture

Outline

Week

Topic

14.02.2018 Introduction and general remarks
21.02.2018 Basics of Image Processing
28.02.2018 Object Identification
07.03.2018 Bag of Visual Words
14.03.2018 Part-Based Model
21.03.2018 Deep Learning Introduction
11.04.2018 Convolutional Neural Networks
18.04.2018 Object Detection
25.04.2018 Semantic Segmentation
02.05.2018 From Proposal Prediction to Instance Segmentation
09.05.2018 Recurrent Neural Networks
16.05.2018 Adversarial Networks, Optical Flow Estimation
23.05.2018 Unsupervised Deep Learning
30.05.2018 Q&A

 Literature 

  • R. Szeliski: Computer Vision Algorithms and Applications, Springer, 2010. ISBN: 978-1-84882-934-3. (Online available: http://szeliski.org/Book/)

Software

  • Matlab
  • Python