Module RO4300-KP08

Duration

2 Semester

Turnus of offer

normally each year in the winter semester

Credit points

8

Course of studies, specific fields and terms:

• Master Robotics and Autonomous Systems 2019, compulsory, Compulsory courses

Classes and lectures:

• Computer Vision (exercise, 1 SWS)
• Computer Vision (lecture, 2 SWS)
• Machine Learning (exercise, 1 SWS)
• Machine Learning (lecture, 2 SWS)

Workload:

• 90 hours in-classroom work
• 110 hours private studies
• 40 hours exam preparation

Contents of teaching:

• Representation learning, including manifold learning
• Statistical learning theory
• VC dimension and support vector machines
• Boosting
• Deep Learning
• Limits of induction and importance of data ponderation
• Introduction to human and computer vision
• Sensors, cameras, optics and projections
• Image features: edges, intrinsic dimension, Hough transform, Fourier descriptors, snakes
• Range imaging and 3-D cameras
• Motion and optical flow
• Object recognition
• Example applications

Qualification-goals/Competencies:

• Students can understand and explain various machine-learning problems.
• They can explain and apply different machine learning methods and algorithms.
• They can chose and then evaluate an appropriate method for a particular learning problem.
• They can understand and explain the limits of automatic data analysis.
• Students can understand the basics of computer vision.
• They can explain and perform camera choice and calibration.
• They can explain and apply the basic methods for feature extraction, motion estimation, and object recognition.
• They can indicate appropriate methods for different kinds of computer-vision applications.

Grading through:

• Oral examination

Responsible for this module:

• Prof. Dr.-Ing. Erhardt Barth

Teacher:

• Institute for Neuro- and Bioinformatics
• Prof. Dr.-Ing. Erhardt Barth
• Prof. Dr. rer. nat. Thomas Martinetz

Literature:

• Chris Bishop : Pattern Recognition and Machine Learning Springer ISBN 0-387-31073-8
• Vladimir Vapnik : Statistical Learning Theory Wiley-Interscience, ISBN 0471030031
• Richard Szeliski : Computer Vision: Algorithms and Applications Springer, Boston, 2011
• David Forsyth and Jean Ponce : Computer Vision: A Modern Approach Prentice Hall, 2003

Language:

• English, except in case of only German-speaking participants

Notes:

Last Updated:

02.09.2021