Chair for Computer Aided Medical Procedures & Augmented Reality
Lehrstuhl für Informatikanwendungen in der Medizin & Augmented Reality

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Tracking and Detection in Computer Vision - Winter Term 2010/11

Administrative Info

Lecture by Dr. Slobodan Ilic?
Exercises by: Stefan Hinterstoisser, Stefan Holzer, Jürgen Sotke

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

Tuesday 14:30 - 16:00 MI 03.13.010

Exercises: Wednesday 13:30 - 15:00 MI 03.13.008

Consulting hours:
Lectures (Slobodan Ilic): Thursday, 10:00-11:00h MI 03.13.040

Exercises: (Stefan Hinterstoisser, Stefan Holzer, and Jürgen Sotke): Thursday, 13:30-15:00 MI 03.13.042


  • 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 projects and the intermediate exam

Site Content


Repetition exam : Thursday , 14.04.2011, 10:00-12:00h, Room: 03.13.040(seminar room where lectures took place).(BRING YOUR SCIENTIFIC CALCULATORS!!!). For the repetition exam we allow using of A4 paper filled by hand with the material from the course.

Final results

Within the scope of the Computer Vision 2 lecture Martin Groher and Hauke Heibel will give an introduction to MATLAB on Thursday, 28th from 13:00 till 14:30 in room MI 03.13.010.

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 (of the rechnerhalle),name,family_name,tum_email_address (that is the ...@in.tum.de email address - NO MYTUM addresses!)


You need to register via TUMOnline!


Depending on the complexity of the homework projects, students will from one to several weeks to solve the problems. Solutions will be checked in the following exercise session. Exact dates for every homework project will be announced on the website and said at the exercises.


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.


For some basic concepts the textbook "Computer Vision: a modern approach" by David Forsyth and Jean Ponce, can be used. Also majority of topics can be found in a new book of Rick Szeliski from Microsoft Research available also on-line. However, since the majority of the topics is state-of-the-art, students will be also 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 14:30 at MI 03.13.010.

Date Topic Conducted by Material/Literature
Tue, Oct 19 Introduction Dr. S. Ilic
Tue, Oct 26 Filtering and edge detection: Convolution; Gaussians; Image derivatives; Canny edge detector Dr. S. Ilic
Tue, Nov 2 Local invariant feature detectors: Harris corner detector; Scale space Dr. S. Ilic
Tue, Nov 9 Local invariant feature detectors: Harris Laplace/Affine;EBR(edge based regions); Blob detectors; Hessian; Hessian-Laplace/Affine; Region detectors: MSER(Maximal Stable Extremal Regions), IBR (Image Based Regions); Dr. S. Ilic
Tue, Nov 16 Feature descriptors: Difference of Gaussians and SIFT; Integral images and SURF, Histogram of Oriented Gradients Dr. S. Ilic
Tue, Nov 23 Keypoint recognition: Randomized trees; FERNS; Keypoint signatures Dr. S. Ilic
Tue, Nov 30 Template Detection; Dominant Orientation Templates (DOT) Stefan Hinterstoisser
Tue, Dec 7 Haar features; Ada-boost; Viola-Jones Face Detection Dr. S. Ilic
Tue, Dec 14 Camera models and projections; Model based tracking; Pose estimation from 2D-3D coresspondencies (P-n-P, POSIT, Soft POSIT, Blind P-n-P); Rotation parametrization Dr. S. Ilic
Tue, Dec 21 Non-linear optimization; Robust estimators; RANSAC; Dr. S. Ilic
Tue, Jan 11 Template tracking methods: Lucas-Kanade,Compositional Alg., Inverse Compositional, ESM, Learning a linear predictor, Active Shape and Active Apperiance Models Dr. S. Ilic
Tue, Jan 18 Simulateneous Localization and Mapping(SLAM) - I Dr. S. Ilic
Tue, Jan 25 Simulateneous Localization and Mapping(SLAM) - II Dr. S. Ilic  
Tue, Feb 1 Parallel Tracking and Mapping(PTAM) Dr. S. Ilic  
Tue, Feb 8 Summary and questions Dr. S. Ilic

Exercise Schedule

Exercises start on 27.10.2009 at 13:30 at MI 03.13.008. Here you can find a MATLAB tutorial.

Date Topic Conducted by Material/Literature
Wed, Oct 27 Convolution, Gaussian Filtering and Image Derivatives (due to Nov. 3) Stefan Hinterstoisser, Stefan Holzer, Jürgen Sotke
Wed, Nov 3 Median and Bilateral Filtering (due to Nov. 10) Stefan Hinterstoisser, Stefan Holzer, Jürgen Sotke
Wed, Nov 10 Harris, Harris-Laplace (due to Nov. 17) Stefan Hinterstoisser, Stefan Holzer, Jürgen Sotke
Wed, Nov 17 Sift, Dlt, Ransac, Image Stiching (due to Dec. 1) Stefan Hinterstoisser
Wed, Nov 24 No exercises due to the internal event    
Wed, Dec 1 Ferns (due to Dec 22) Stefan Hinterstoisser
Wed, Dec 8 Lecture: DOT Stefan Hinterstoisser
Wed, Dec 15 Mid-term exam (until camera models) Stefan Hinterstoisser
Wed, Dec 22 correction of the Ferns (for those of you who are not able to attend this date - you can also show the homework on the 12.1.2011) Stefan Hinterstoisser  
Wed, Jan 12 Pose Estimation/Refinement (due to Jan 19) Stefan Hinterstoisser, Stefan Holzer, Jürgen Sotke
Wed, Jan 19 Hyperplane Template Matching (due to Feb 2) Stefan Hinterstoisser, Stefan Holzer, Jürgen Sotke
Wed, Jan 26   Stefan Hinterstoisser, Stefan Holzer, Jürgen Sotke

Title: Tracking and Detection in Computer Vision
Professor: Slobodan Ilic
Type: Lecture
Information: 2+2 SWS, 5 ECTS, Wahlfach im Masterstudiengang Informatik, Wahlfach im Masterstudiengang Biomedical Computing, Wahlfach im Masterstudiengang Robotics, Cognition, Intelligence
Term: 2010WiSe

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