Hauptseminar/Master Seminar:
Machine Learning Methods for Computer Vision Applications
Nassir Navab, Diana Mateus, Loren Schwarz, Olivier Pauly, Slobodan Ilic
Type: Advanced Seminar Course
Module IN8901
Type: Master Seminar
Module IN2107
Type: Seminar
Module IN0014
SWS: 2+0
ECTS: 4 Credits
Location: MI 03.13.010
Time See schedule below.
Course Language: English
Announcements
- The detailed schedule is now available.
- Please send a mail to Loren Schwarz () until May 15th with your preferred topic.
- Preliminary list of topics is now online.
- The first meeting will be on May 5 at 16:00 in our seminar room (MI 03.13.010).
- The seminar schedule is now available.
- All participants, please register for the seminar in TUMonline.
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 have to fulfill these requirements:
- Presentation: The selected paper is presented to the other participants (20 minutes presentation 10 minutes questions). Use the CAMP templates for PowerPoint CAMPmaster.pot, or Latex: CAMP-latex-template.zip.
- Written Handout: A document of maximum 8 pages is written and submitted one week after the presentation. Please download and use the llncs2e.zip Latex template.
- Attendance: Participants have to participate actively in all seminar sessions.
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.
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.
Please send a mail to Loren Schwarz () until May 15th with your preferred topic.
Medical Image Analysis
- Lee, Hofmann et al.: Learning Similarity Measure for Multi-Modal 3D Image Registration, CVPR (2009)
- Lucchi, Smith et al.a: A Fully Automated Approach to Segmentation of Irregularly Shaped Cellular Structures in EM Images, MICCAI (2010)
- Bernardis and Yu: Segmentation Subject to Stitching Constraints: Finding Many Small Structures in a Large Image, MICCAI (2010)
- (taken) Shi, Yap, Fan et al.: Construction of Multi-Region-Multi-Reference Atlases for Neonatal Brain MRI Segmentation, Neuro Image (2010)
- Wang, Suh et al.: Regression-based Label Fusion for Multi-Atlas Segmentation, CVPR (2011)
- André, Vercauteren, Buchner et al.: A smart Atlas for Endomicroscopy using Automated Video Retrieval, Medical Image Analysis (2011)
- (taken) Barbu, Suehling, Xu et al.: Automatic Detection and Segmentation of Axillary Lymph Nodes, MICCAI (2010)
- (taken) Criminisi, Shotton et al.: Regression Forests for Efficient Anatomy Detection and Localization in CT Studies, MICCAI (2010)
- Geremia, Menze et al.: Spatial Decision Forests for MS Lesion Segmentation in Multi-Channel MR Images
- Huh, Ker, Bise et al.: Automated Mitosis Detection of Stem Cell Populations in Phase-Contrast Microscopy Images, IEEE TMI (2011)
- Zheng, Barbu, Georgescu et al.: Four-Chamber Heart Modeling and Automatic Segmentation for 3D Cardiac CT Volumes using Marginal Space Learning and Steerable Features, IEEE TMI (2008)
- (taken) Zheng, Georgescu et al.: Marginal Space Learning for Efficient Detection of 2D/3D Anatomical Structures in Medical Images, IPMI (2009)
- Zhou, Georgescu et al.: Image Based Regression Using Boosting Method, ICCV (2005)
- Criminisi, Shotton and Bucciarelli: Decision Forests with Long-Range Spatial Context for Organ Localization in CT Volumes, MICCAI PMMIA (2009)
- (taken) Feulner, Zhou et al.: Fast Automatic Segmentation of the Esophagus from 3D CV Data Using a Probabilistic Model, MICCAI (2009)
- Wels, Zheng et al.: Fast and Robust 3D MRI Brain Structure Segmentation, MICCAI (2009)
Object Detection and Tracking
- (taken) Gall, Yao et al.: Hough Forests for Object Detection, Tracking, and Action Recognition, PAMI
- (taken) Felzenszwalb, Girshick et al: Object Detection with Discriminatively Trained Part Based Models. PAMI (2010)
Dimensionality Reduction
- Carreira-Perpiñán and Lu: Parametric Dimensionality Reduction by Unsupervised Regression, CVPR (2010)
Image Analysis
- (taken) Santner, Unger et al.: Interactive Texture Segmentation using Random Forests and Total Variation, BMVC (2009)
- (taken) Yang, Huang and Yang: Exploiting Self-Similarities for Single Frame Super-Resolution, ACCV (2010)
- Kim, Lee and Lee: Learning Full Pairwise Affinities for Spectral Segmentation, CVPR (2010)
Human Motion Analysis
- (taken) Shotton, Fitzgibbon et al.: Real-Time Human Pose Recognition in Parts from Single Depth Images, CVPR (2011)
- (taken) Gall, Yao and van Gool: 2D Action Recognition Serves 3D Human Pose Estimation, ECCV (2010)
- Jiang: Human Pose Estimation Using Consistent Max-Covering, ICCV (2009)
- (taken) Lu, Carreira-Perpinan and Sminchisescu: People Tracking with the Laplacian Eigenmaps Latent Variable Model, NIPS (2007)
- (taken) Sun, Bray, Thayananthan, Yuan and Torr: Regression-Based Human Motion Capture from Voxel Data, BMVC (2006)
- Darby, Li and Costen: Tracking humanpose with multiple activity models, Pattern Recognition (2010)
- Li, Fermuller et al.: Learning Shift-Invariant Sparse Representation of Actions CVPR (2010)
- Urtasun, Fleet, Hertzmann and Fua: Priors for People Tracking from Small Training Sets, ICCV (2005)
- Tian, Li and Sclaroff: Articulated Pose Estimation in a Learned Smooth Space of Feasible Solutions, CVPRW (2005)
- (taken) Liu, Stoll et al.: Markerless Motion Capture of Interacting Characters Using Multi-view Image Segmentation, CVPR (2011)
- Sigal, Black et al.: Loose-limbed People: Estimating Human Pose and Motion using Non-parametric Belief Propagation, IJCV
- Freifeld, Weiss et al.: Contour people: A parameterized model of 2D articulated human shape, CVPR (2010)
- Eichner, Marin-Jimenez et al.: Articulated Human Pose Estimation and Search in (Almost) Unconstrained Still Images, ETH Zurich (2010)
- (taken) Bourdev, Maji et al.: Detecting People Using Mutually Consistent Poselet Activations, ECCV (2010)
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.
Literature and Helpful Links
A lot of scientific publications can be found online.
The following list may help you to find some further information on your particular topic:
Some publishers:
Libraries (online and offline):
Some further hints for working with references:
- JabRef is a Java program for comfortable working with Bibtex literature databases. Handy feature: if you know the PubMed ID for an article, JabRef can import data from there (via "Web Search/Medline").
- Mendeley is a cross-platform program for organising your references.
If you find useful resources that are not already listed here, please tell us, so we can add them for others. Thanks.
