TeachingSs10MLCVSeminar

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

Hauptseminar/Master Seminar:
Machine Learning Methods for Computer Vision Applications

Nassir Navab, Diana Mateus, Loren Schwarz, Olivier Pauly

Type: Advanced Seminar Course Module IN8901
Type: Master Seminar Module IN2107
Type: Seminar Module IN0014
SWS: 2+0
ECTS: 4 Credits
Time/Location: Mondays (selected dates), 16:00-18:00, MI 03.13.010
Course Language: English

Announcements

  • Attention: on June 28, the seminar will take place in room MI 01.06.011 due to a collision with another event at our regular seminar room.
  • All participants, please register for the seminar in TUMonline during this or next week.

Requirements

In this Master Seminar (formerly Hauptseminar), each student is asked to select one topic from the list below. In order to successfully complete the seminar, participants are required to:

  • Submit a written handout (8 pages maximum). Please download the llncs2e.zip latex format.
  • Attend all seminar sessions and participate.

Each presentation is followed by a discussion and everyone is encouraged to actively participate. The handout must include all references used and must be written completely in your own words. Copy and paste wont be tolerated. Both handout and presentation have to be done in English.

Registration

Interested students, please take a look at the papers listed below and select one you would like to base your seminar talk on. Then please send an email to Loren Schwarz (), including your paper choice, your name, your birthday, program of study and Matrikelnummer. Registration is closed!

Presentation Topics

Please choose one of the following papers for your seminar presentation. For obtaining the full-text PDFs, please use Google and especially Google Scholar. In case you are interested in a paper but cannot find the full text, please e-mail us.

Dimensionality Reduction

  • (taken) Carreira-Perpinan and Lu: Dimensionality Reduction by Unsupervised Regression, CVPR (2008)
  • (taken) Hadsell, Chopra and Lecun: Dimensionality Reduction by Learning an Invariant Mapping, CVPR (2006)
  • Frey and Dueck: Clustering by Passing Messages Between Data Points, Science (2007)
  • Konolige, Bowman, Chen, Mihelich, Calonder, Lepetit and Fua: View-Based Maps, Proc. Rob. Sc. Sys
  • Lu, Bi, Yu, Peng, Krishnan and Zhou: Hierarchical Learning for Tubular Structure Parsing in Medical Imaging: A Study on Coronary Arteries Using 3D CV Angiography, ICCV (2009)
  • Ramanan and Baker: Local Distance Functions: A Taxonomy, New Algorithms and an Evaluation, CVPR (2009)
  • (taken) Talwalkar, Kumar and Rowley: Large-Scale Manifold Learning, CVPR (2009)

Metric Learning

  • (taken) Caetano, Mcauley, Cheng, Le and Smola: Learning Graph Matching, PAMI (2009)
  • (taken) Nguyen and de la Torre: Metric Learning for Image Alignment, IJCV (2009)

Image Processing

  • (taken) Duchenne, Audibert, Keriven, Ponce and Ségonne: Segmentation by Transduction, CVPR (2008)
  • (taken) Glasner, Bagon and Irani: Super-Resolution from a Single Image, ICCV (2009)
  • (taken) Takeda, Farsiu and Milanfar: Kernel Regression for Image Processing and Reconstruction, IEEE TIP (2007)

Object Recognition

  • (taken) Calonder, Lepetit and Fua: Keypoint Signatures for Fast Learning and Recognition, ECCV (2008)
  • Chin, Wang and Suter: Robust Fitting of Multiple Structures: The Statistical Learning Approach, ICCV (2009)
  • Jurie and Triggs: Creating Efficient Codebooks for Visual Recognition, ICCV (2005)
  • (taken) Leistner, Saffari, Santner and Bischof: Semi-Supervised Random Forests, ICCV (2009)
  • (taken) Özuysal, Calonder, Lepetit and Fua: Fast Keypoint Recognition using Random Ferns, PAMI (2009)

Object Tracking

  • (taken) Jurie and Dhome: Hyperplane Approximation for Template Matching, PAMI (2002)
  • Lepetit and Fua: Keypoint Recognition using Randomized Trees, PAMI (2006)
  • Kokkinos and Yuille: Unsupervised Learning of Object Deformation Models, ICCV (2007)
  • Reiterer, Lehmann, Miljanovic, Ali, Paar, Egly, Eiter and Kahmen: A 3D Optical Deformation Measurement System Supported by Knowledge-Based and Learning Techniques, JAG (2009)
  • (taken) Salzmann, Urtasun and Fua: Local Deformation Models for Monocular 3D Shape Recovery, CVPR (2008)

Human Recognition

  • (taken) Duchenne, Laptev, Sivic, Bach and Ponce: Automatic Annotation of Human Actions in Video, ICCV (2009)
  • (taken) Guillaumin, Verbeek and Schmid: Is that you? Learning Approaches for Face Identification, CVPR (2009)
  • Turaga, Chellappa, Subrahmanian and Udrea: Machine Recognition of Human Activities: A Survey, IEEE TCSVT (2008)

Human Tracking

  • Agarwal and Triggs: Recovering 3D Human Pose from Monocular Images, PAMI (2006)
  • (taken) Jaeggli, Koller-Meier and Van Gool: Learning Generative Models for Multi-Activity Body Pose Estimation, IJCV (2009)
  • (taken) Poppe: Vision-based Human Motion Analysis: An Overview, CVIU (2007)
  • (taken) Grochow, Martin, Hertzmann and Popovic: Style-Based Inverse Kinematics, ACM TOG (2004)
  • Lu, Carreira-Perpinan and Sminchisescu: People Tracking with the Laplacian Eigenmaps Latent Variable Model, NIPS (2007)
  • Sun, Bray, Thayananthan, Yuan and Torr: Regression-Based Human Motion Capture from Voxel Data, BMVC (2006)
  • Tian, Li and Sclaroff: Articulated Pose Estimation in a Learned Smooth Space of Feasible Solutions, CVPRW (2005)
  • Urtasun, Fleet, Hertzmann and Fua: Priors for People Tracking from Small Training Sets, ICCV (2005)

Schedule

The seminar consists of a preliminary meeting, an introductory meeting and seminar sessions. Each of the sessions has two student presentations. All seminar participants are expected to be present at every session and to actively participate in the discussion.

Date Time Topic By Supervision Material
11.02.10 12:00 Preliminary Meeting Tutors   Introduction
26.04.10 16:00 First Meeting Tutors   Instructions and Dates
21.06.10 16:00 Dimensionality Reduction by Learning an Invariant Mapping Huang Xiao Diana Mateus Paper
  16:30 Dimensionality Reduction by Unsupervised Regression Han Xiao Diana Mateus Paper
  17:00 Large-Scale Manifold Learning Azat Yanbulatov Diana Mateus Paper
28.06.10 16:00 Super-Resolution from a Single Image Wangxin Liu Diana Mateus Paper
  16:30 Metric Learning for Image Alignment Jakob Engel Olivier Pauly Paper
  17:00 Segmentation by Transduction Vladimir Golkov Diana Mateus Paper
05.07.10 16:00 Kernel Regression for Image Processing and Reconstruction Martin Raiber Olivier Pauly Paper
  16:30 Hyperplane Approximation for Template Matching Carlos Camino Olivier Pauly Paper
12.07.10 16:00 Keypoint Signatures for Fast Learning and Recognition Mohammadreza Babaee Loren Schwarz Paper
  16:30 Fast Keypoint Recognition using Random Ferns Benoit Diotte Olivier Pauly Paper
  17:00 Semi-Supervised Random Forests Jennifer Reinelt Olivier Pauly Paper
19.07.10 16:00 Learning Generative Models for Multi- Activity Body Pose Estimation Bernhard Fürst Loren Schwarz Paper
  16:30 Is that you? Metric Learning Approaches for Face Identification Valeriy Khakhutskyy Olivier Pauly Paper
26.07.10 16:00 Style-Based Inverse Kinematics Andreas Schaumeier Loren Schwarz Paper
  17:00 Local Deformation Models for Monocular 3D Shape Recovery Tolga Birdal Loren Schwarz Paper


TeachingForm
Title: Machine Learning Methods for Computer Vision Applications
Professor: Nassir Navab
Tutors: Diana Mateus; Loren Schwarz; Olivier Pauly
Type: Hauptseminar
Information: Hauptseminar, SWS: 2, ECTS: 4
Term: 2010SoSe
Abstract:  


Edit | Attach | Refresh | Diffs | More | Revision r1.40 - 09 Aug 2010 - 14:00 - LorenSchwarz

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