Module Number

INFO-4177
Module Title

Intelligent Systems II - Learning in Computer Vision
Lecture Type(s)

Lecture, Tutorial
ECTS 6
Work load
- Contact time
- Self study
Workload:
180 h
Class time:
60 h / 4 SWS
Self study:
120 h
Duration 1 Semester
Frequency Irregular
Language of instruction English
Type of Exam

Written exam (in case of a small number of participants: oral tests)

Content

Graphical Models; Bayesian Belief Networks; Markov Random Fields; Conditional Random Fields; Learning of Structured Variables; Bayesian Decision Theory; Loss-based Learning; Parameter Learning in Graphical Models; Structured Support Vector Machines; Exact and Approximate Inference Methods; Applications in Image Processing; Segmentation; Human Pose Estimation; Image Denoising; Stereo; Object Detection.

Objectives

Students learn how complicated statistical relationships can be represented with the help of graphical models. Concrete and current problems from the fields of image processing and image understanding are solved. Various learning methods make it possible to automatically set data-driven parameters and evaluate the performance achieved.

Allocation of credits / grading
Type of Class
Status
SWS
Credits
Type of Exam
Exam duration
Evaluation
Calculation
of Module (%)
Lecture
V
o
2
3.0
ot
25
g
100
Tutorial
Ü
o
2
3.0
Prerequisite for participation There are no specific prerequisites.
Lecturer / Other Gehler, Lensch, MPI
Literature

Vorlesungsfolien werden bereitgestellt

Last offered Sommersemester 2020
Planned for ---
Assigned Study Areas INFO-INFO, MEDI-APPL, MEDI-INFO, MEDI-MEDI, MEDI-VIS, ML-CS, ML-DIV