TeachingWs11TDCV

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

Tracking and Detection in Computer Vision - Winter Term 2011/12

Administrative Info

Lecture by Dr. Slobodan Ilic
Exercises by: Stefan Holzer, Vasilis Belagiannis, David Tan

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 10:30 - 11:30 & 13:30 - 15:00 MI 03.13.008

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

Exercises: (Stefan Holzer, Vasilis Belagiannis): Thursday, 13:30-15:00 MI 03.13.042

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 projects and the intermediate exam

Site Content

Announcements

Results of the repetition exam can be downloaded here.

The repeat exam will take place on 29th of March at 10:00h (Seminar Room 03.13.010).

You can download the final result here (all students who failed are marked red or gray on the final grade).

Homework Results here. If there are any corrections, please e-mail to: tanda@in.tum.de

Midterm Results here.

The final exam will take place on 13.02. from 12-14:00h in 5620.01.101 (101, Interims Hörsaal 1).

The lectures start on 18th of October and the exercises the day after on 19th of October.

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 holzers 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!)

Registration

You need to register via TUMOnline!

Homeworks

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.

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. 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 18.10.2011 at 14:30 at MI 03.13.010.

Date Topic Conducted by Material/Literature
Tue, Oct 18 Introduction Dr. S. Ilic
Tue, Oct 25 Convolution, Non-linear filtering, Gaussian Filtering, Image Derivatives, Edge Detection. Dr. S. Ilic
Wed, Nov 15 Local invariant feature detectors: Harris corner, Harris Laplace/Affine, Hessian, Hessian-Laplace/Affine. Dr. S. Ilic
Tue, Nov 22 Feature descriptors: Difference of Gaussians and SIFT, Integral images and SURF, Histogram of Oriented Gradients Dr. S. Ilic
Tue, Nov 29, Dec 6 Keypoint recognition: Randomized trees, FERNS, Keypoint signatures Dr. S. Ilic
Tue, Dec 13 Haar features, Ada-boost, Viola-Jones Face Detection. Dr. S. Ilic
Tue, Dec 20 Camera models and projections, Model based tracking, Pose estimation from 2D-3D coresspondencies (DLT, P-n-P), Rotation parametrization. Dr. S. Ilic
Tue, Jan 10, 17 Non-linear optimization, Robust estimators, RANSAC. Dr. S. Ilic
Tue, Jan 24 Template tracking methods: Lucas-Kanade, Compositional Alg., Inverse Compositional, ESM, Dr. S. Ilic
Tue, Jan 31 Real-Time Detection of Texture-Less Objects S. Hinterstoisser
Tue, Feb 07 Summary Dr. S. Ilic

Exercise Schedule

Exercises start on 19.10.2011 at 13:30 at MI 03.13.008. Here you can find a MATLAB tutorial.

Date Topic Conducted by Material/Literature
Wed, Oct 19 Matlab introduction Stefan Holzer, Vasilis Belangianis, David Tan
Wed, Oct 26 Convolution, Gaussian Filtering, Image Derivatives (due to Nov 2) Stefan Holzer, Vasilis Belangianis, David Tan
Wed, Nov 2 Median and Bilateral Filtering (due to Nov 16) Stefan Holzer, Vasilis Belangianis, David Tan
Wed, Nov 16 Harris, Harris-Laplace (due to Nov 23) Stefan Holzer, Vasilis Belangianis, David Tan
Wed, Nov 23 Sift, Dlt, Ransac, Image Stiching (due to Nov 30) Stefan Holzer, Vasilis Belangianis, David Tan
Wed, Nov 30 Ferns (due to Dec 14) Stefan Holzer, Vasilis Belangianis, David Tan
Wed, Dec 7 Mid-term Stefan Holzer, Vasilis Belangianis, David Tan
Wed, Dec 14 Ada-boost classification(due to Jan 11) Stefan Holzer, Vasilis Belangianis, David Tan
Wed, Jan 11 Non-linear optimization (due to Jan 25) Stefan Holzer, Vasilis Belangianis, David Tan
Wed, Jan 18 Mid-term review Stefan Holzer, Vasilis Belangianis, David Tan
Wed, Jan 25 Pose Estimation/Refinement (due to Feb 8) Stefan Holzer, Vasilis Belangianis, David Tan  
Wed, Feb 8 Feedback and summary Stefan Holzer, Vasilis Belangianis, David Tan  
       
       


TeachingForm
Title: Tracking and Detection in Computer Vision
Professor: Dr. Slobodan Ilic
Tutors: Stefan Holzer, Vasillis Belangianis, David Tan
Type: Lecture
Information: 2+2 SWS, 5 ECTS, Wahlfach im Masterstudiengang Informatik, Wahlfach im Masterstudiengang Biomedical Computing, Wahlfach im Masterstudiengang Robotics, Cognition, Intelligence
Term: 2011WiSe
Abstract:  


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