Tracking and Detection in Computer Vision - Winter Term 2009/10
Administrative Info
Lecture by Dr. Slobodan Ilic
Exercises by: Stefan Hinterstoisser, Stefan Holzer
Type: Lecture Module IN2210
Programs: Informatics (Bachelor, Master)
Biomedical Computing (Master)
Robotics, Cognition, Intelligence(Master)
SWS: 2+2
ECTS: 5 Credits
Course Language: English
|
Time, Location & Requirements
Monday 10:00 - 11:30 MI 03.13.010
Exercises: Wednesday 10:00 - 11:30 MI 03.13.008
Consulting hours:
Lectures (Slobodan Ilic): Tuesday, 10:00-11:00h MI 03.13.040
Exercises: (Stefan Hinterstoisser and Stefan Holzer):
Requirements:
- The classes and exam are in English
- For the intermediate exam, nothing but your pens and your calculators are allowed
- For the final exam, nothing but your pens, your calculators and one DIN A4 page (handwritten, front and back side) with notes are allowed
- The final exam contains 100 points, you need to have 50 points to pass it
- Up to 40 bonus points can be earned from the homework and the intermediate exam
|
Site Content
|
Announcements
Repeated Exam: Thu, 22.04.2010, 10:00-12:00, MI 03.13.010. Please send us an e-mail if you would like to take the repeated exam by 15.04.2010!
For the repeated exam we allow using of one A4 paper filled by hand.
Results for the final exams with grades: GRADES.
Consulting hours:
Lectures (Slobodan Ilic): Tuesday, 10:00-11:00h MI 03.13.040
Exercises: (Stefan Hinterstoisser and Stefan Holzer): Thursday, 10:00-12:00 MI 03.13.042
Access to the computerroom
Every student needs to have access to the computer in the computer room (03.13.008) before the exercises start! If you do not have access yet please sent us an email (send it to hinterst at in.tum.de ) in the following format:
username,name,family_name,tum_email_address
Registration
Neither the class nor the exercises require any registration. However, you need to be registered to the final exam via TUMOnline!
Homeworks
Students have exactly one week to solve the homework (Solutions will be checked in the following exercise session)
|
Overview
Computer Vision, as a relatively young research area, has emerged as a key discipline in computer science.
This is not only evident by a growing high competitive research community with the high impact
factor in computer science, but also by the emerging of numerous Vision companies turning the research
ideas into the numerous commercial applications. Besides well know studies of the 3D geometry and
camera models, object tracking and detection in the images and videos become one of the principal research
directions of the modern Computer Vision. The main objective of this course is to provide students
with gradual introduction to the modern tracking, detection and recognition techniques developed
in last 5 years. The course will provide in depth knowledge of image features, their detection and description,
matching techniques, key-point recognition, basic and advanced tracking algorithms based on image
features and image intensities, basics of structure from motion and bundle adjustment, synchronous
localization and mapping algorithm, object detection and pose estimation using classification approaches
and object recognition and image retrieval using bag of words approaches. In the end student will have
thorough description of the most important tracking, detection and recognition techniques. They should
be able to understand and implement those solutions and apply them in the reasonably complex problems.
The concepts described in this course will be accompanied with the brief explanation of the necessary
mathematical tools. The participants of this course will be given the necessary basis to follow vast
and growing Computer Vision literature and use the acquired knowledge to solve new practical problems.
Who should attend this course?
Bachelor students at their last years and all Master students that are interested to learn about the modern and well established concepts and algorithms
related to tracking, detection and recognition of Computer Vision or desire to use those techniques
in their research.
Prerequisites: Most of the knowledge required should be part of the normal background in Computer
Science and undergraduate/graduate Mathematics and Geometry.
Textbooks
For some basic concepts the textbook "Computer Vision: a modern approach" by David Forsyth and
Jean Ponce, can be used. However, since the majority of the topics is state-of-the-art, students will be
referred to the relevant publications provided before the course and can use the provided lecture notes.
Lecture Schedule and Content
Lectures start on 19.10.2009 at 10am at
MI 03.13.010.
Exercise Schedule
Exercises start on 11.11.2009 (due to FVV) at 10am at
MI 03.13.008. Here you can find a
MATLAB tutorial.