eTRIMS - E-Training for Interpreting Images of Man-Made Scenes
 
eTRIMS
     
Dataset:

Benchmark data set of eTRIMS Image Database

Mar 31, 2009. Official release with 60 annotated images
Journal Publications:
  • Čech, J. & Šára, R.
    Languages for Constrained Binary Segmentation Based on Maximum A Posteriori Probability Labeling International Journal of Imaging Systems and Technology, John Wiley & Sons, Inc., 2009, Vol. 19(2), pp. 69-79
    [pdf]
  • Čech, J., Matas, J. & Perdoch, M.
    Efficient Sequential Correspondence Selection by Cosegmentation IEEE Transactions on PAMI, In Press. DOI 10.1109/TPAMI.2009.176.
    [pdf]
  • Drbohlav, O. & Leonardis, A.
    Towards correct and informative evaluation methodology for texture classification under varying viewpoint and illumination Computer Vision and Image Understanding, 2009
  • Heesch, D. & Petrou, M.
    Markov random fields with asymmetric interactions for modelling spatial context in structured scenes Journal of Signal Processing Systems, Springer, 2009, Vol. 10.1007/s11265-009-0349-0
    [pdf]
  • Jahangiri, M. & Petrou, M.
    Investigative Mood Visual Attention Model Computer Vision and Image Understanding, Elsevier, (Under Review)
  • Šochman, J. & Matas, J.
    Learning Fast Emulators of Binary Decision Processes International Journal of Computer Vision, 2009, Vol. 83, pp. 149-163
    [pdf]
  • Wenzel, S., Drauschke, M. & Förstner, W.
    Detection of Repeated Structures in Facade Images Pattern Recognition and Image Analysis, 2008, Vol. 18(3), pp. 406-411
    [pdf]
  • Wenzel, S., Drauschke, M. & Förstner, W.
    Detection and Description of Repeated Structures in Rectified Facade Images Photogrammetrie, Fernerkundung, Geoinformation PFG, 2007, Vol. 7, pp. 481-490
    [pdf]
Conferences Publications:
  • Bochko, V.A. & Petrou, M.
    Recognition of structural parts of buildings using support vector machines Pattern Recognition and Information Processing, PRIP2007 2007
    [pdf]
  • Čech, J. & Šára, R.
    Windowpane Detection based on Maximum Aposteriori Probability Labeling Barneva, R. P. & Brimkov, V. (ed.) Image Analysis - From Theory to Applications, Proceedings of the 12th International Workshop on Combinatorial Image Analysis (IWCIA'08) Research Publishing Services, 2008, pp. 3-11
    [pdf]
  • Čech, J. & Šára, R.
    Efficient Sampling of Disparity Space for Fast and Accurate Matching BenCOS 2007: CVPR Workshop Towards Benchmarking Automated Calibration, Orientation and Surface Reconstruction from Images Omnipress, 2007
    [pdf]
  • Čech, J., Matas, J. & Perdoch, M.
    Efficient Sequential Correspondence Selection by Cosegmentation CVPR 2008: Proceedings of the 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition 2008, pp. 1020-1027
    [pdf]
  • Drauschke, M.
    An Irregular Pyramid for Multi-Scale Analysis of Objects and their Parts GbR'09 2009, pp. 293-303
    [pdf]
  • Drauschke, M. & Förstner, W.
    Comparison of Adaboost and ADTboost for Feature Subset Selection PRIS'08 2008, pp. 113-122
    [pdf]
  • Drauschke, M. & Förstner, W.
    Selecting Appropriate Features for Detecting Buildings and Building Parts 21st ISPRS Congress 2008, pp. 447-452
    [pdf]
  • Grabner, H., Šochman, J., Bischof, H. & Matas, J.
    Training Sequential On-line Boosting Classifier for Visual Tracking Borgefors, G. & Flynn, P. (ed.) ICPR 2008: Proceedings of the 19th International Conference on Pattern Recognition Omnipress, 2008, pp. 4
    [pdf]
  • Hartz, J.
    Learning Probabilistic Structure Graphs for Classification and Detection of Object Structures to appear in: Proc. of the of the International Conference on Machine Learning and Applications 2009
    [pdf]
  • Hartz, J., Hotz, L., Neumann, B. & Terzić, K.
    Automatic Incremental Model Learning for Scene Interpretation Proc. of the Fourth IASTED International Conference on Computational Intelligence 2009
    [pdf]
  • Hartz, J. & Neumann, B.
    Learning a Knowledge Base of Ontological Concepts for High-Level Scene Interpretation IEEE Proc. International Conference on Machine Learning and Applications, Cincinnati (Ohio, USA), December 2007
    [pdf]
  • Heesch, D. & Petrou, M.
    Learning Markovian dependencies from annotated images IEEE Int'l Symposium Machine Learning in Signal Processing 2007
    [pdf]
  • Heesch, D. & Petrou, M.
    Non-Gibbsian Markov random fields for contextual object recognition British Machine Vision Conference (BMVC) 2007, Vol. 2, pp. 930-939
    [pdf]
  • Heesch, D., Tan, R. & Petrou, M.
    Context first Proc Int'l Workshop on Structural and Syntactic Pattern Recognition 2008
    [pdf]
  • Hotz, L.
    Modeling, Representing, and Configuring Restricted Part-Whole Relations Tihonen, J. (ed.) Configuration Workshop, 2008 2008
  • Hotz, L., Neumann, B. & Terzić, K.
    High-level expectations for Low-level image processing KI 2008: Advances in Artificial Intelligence 2008, Vol. 5243, pp. 87-94
    [PDF]
  • Jahangiri, M. & Petrou, M.
    An Attention Model for Extracting Regions that Merit Identification IEEE International Conference on Image Processing (ICIP), Cairo, Egypt 2009
    [pdf]
  • Jahangiri, M. & Petrou, M.
    Fully Bottom-Up Blob Extraction in Building Facades 9th International Conference on Pattern Recognition and Image Analysis: New Information Technologies, Nizhni Novgord, Russia, 2008
    [pdf]
  • Korč, F. & Förstner, W.
    Approximate Parameter Learning in Conditional Random Fields: An Empirical Investigation Rigoll, G. (ed.). Pattern Recognition. Springer, DAGM 2008(5096), pp. 11-20
    [pdf]
  • Korč, F. & Förstner, W.
    Interpreting Terrestrial Images of Urban Scenes Using Discriminative Random Fields Proc. of the 21st Congress of the International Society for Photogrammetry and Remote Sensing (ISPRS) 2008
    [pdf]
  • Korč, F. & Förstner, W.
    Finding Optimal Non-Overlapping Subset of Extracted Image Objects Proc. of the 12th International Workshop on Combinatorial Image Analysis (IWCIA) 2008
    [pdf]
  • Kreutzmann, A., Terzić, K. & Neumann, B.
    Context-aware Classification for Incremental Scene Interpretation to appear in: Proc. Workshop on Use of Context in Vision Processing 2009
    [pdf]
  • Matas, J. & Šochman, J.
    Wald's Sequential Analysis for Time-constrained Vision Problems Hutchinson, S. (ed.) IEEE International Conference on Robotics and Automation, Workshops and Tutorials 2007, pp. 10
    [pdf]
  • Petrou, M.
    Learning in Computer Vision: Some Thoughts Rueda, L., Mery, D. & Kittler, J. (ed.) Progress in Pattern Recognition, Image Analysis and Applications, 12th Iberoamericann Congress on Pattern Recognition, CIARP Springer, 2007, Vol. 4756, pp. 1-12
    [pdf]
  • Petrou, M. & Xu, M.
    The Tower of Knowledge Scheme for Learning in Computer Vision Proceedings of the International Conference on Digital Image Computing: Techniques and Applications, DICTA 2007, 3-5 IEEE Computer Society, 2007, pp. 85-91
    [pdf]
  • Šára, R.
    Robust Correspondence Recognition for Computer Vision Rizzi, A. & Vichi, M. (ed.) COMPSTAT 2006: Proceedings in Computational Statistics of 17th ERS-IASC Symposium Physica-Verlag, 2006, pp. 119-131
    [pdf]
  • Šára, R. & Matousek, M.
    FAIR: Towards A New Feature for Affinely-Invariant Recognition ICPR 2006: Proceedings of the 18th International Conference on Pattern Recognition IEEE Computer Society, 2006, Vol. 2, pp. 412-416
    [pdf]
  • Šochman, J. & Matas, J.
    Learning A Fast Emulator of a Binary Decision Process Yagi, Y., Kang, S. B., Kweon, I. S. & Zha, H. (ed.) ACCV Springer, 2007, Vol. II, pp. 236-245
    [pdf]
  • Terzić, K., Hotz, L. & Neumann, B.
    Division of Work During Behaviour Recognition - The SCENIC Approach Schuldt, A. (ed.) Workshop on Behaviour Monitoring and Interpretation (BMI-07, KI-07) 2007, pp. 144-159
    [pdf]
  • Terzić, K., Hotz, L. & Šochman, J.
    Interpreting Structures in Man-Made Scenes: Combining Low-Level and High-Level Structure Sources To appear: International Conference on Agents and Artificial Intelligence 2010
  • Terzić, K. & Neumann, B.
    Integrating Context Priors into a Decision Tree Classification Scheme International Conference on Machine Vision, Image Processing, and Pattern Analysis, to appear 2009
  • Wenzel, S., Drauschke, M. & Förstner, W.
    Detection of repeated structures in facade images (accepted/in print) Proceedings of the OGRW-7-2007, 7th Open German/Russian Workshop on Pattern Recognition and Image Understanding. August 20-23, 2007. Ettlingen, Germany 2007
    [pdf]
  • Wenzel, S. & Förstner, W.
    Semi-supervised incremental learning of hierarchical appearance models 21st Congress of the International Society for Photogrammetry and Remote Sensing (ISPRS) 2008, pp. 399-404 Part B3b-2
    [pdf]
  • Wenzel, S. & Hotz, L.
    The Role of Sequences for Incremental Learning To appear: Second International Conference on Agents and Artificial Intelligence, ICAART2010 2010
  • Xu, M. & Petrou, M.
    Learning Logic Rules for Scene Interpretation Based on Markov Logic Networks Asian Conference on Computer Vision (ACCV), Xi' an, China 2009
Other Publications:
  • Šochman, J.
    Learning for Sequential Classification PhD-Thesis, Czech Technical University in Prague, 2009, pp. 75
    [pdf]
  • Drauschke, M.
    Description of Stable Regions IPM Technical Report, Department of Photogrammetry, University of Bonn, 2008(TR-IGG-P-2008-03)
    [pdf]
  • Drauschke, M.
    Feature Subset Selection with Adaboost and ADTboost Technical Report, Department of Photogrammetry, University of Bonn, 2008(TR-IGG-P-2008-04)
    [pdf]
  • Drauschke, M.
    Multi-class ADTboost Technical Report, Department of Photogrammetry, University of Bonn, 2008
    [pdf]
  • Hotz, L.
    Frame-based Knowledge Representation for Configuration, Analysis, and Diagnoses of technical Systems (in German) PhD-Thesis, University of Hamburg, Infix, 2009, Vol. 325
  • Hotz, L., Neumann, B., Terzić, K. & Šochman, J.
    Feedback between Low-Level and High-Level Image Processing Technical Report, Department of Informatics, University of Hamburg, 2007(FBI-B-278/07)
    [pdf]
  • Korč, F. & Förstner, W.
    eTRIMS Image Database for Interpreting Images of Man-Made Scenes Technical Report, Dept. of Photogrammetry, University of Bonn, 2009(TR-IGG-P-2009-01)
    [pdf]
  • Matas, J. & Šochman, J.
    Wald's Sequential Analysis for Time-constrained Vision Problems Kragic, D. & Kyrki, V. (ed.) Unifying Perspectives in Computational and Robot Vision Chapter 5 Springer, 2008, Vol. 8, pp. 57-77
  • Moeller, R. & Neumann, B.
    Ontology-Based Reasoning Techniques for Multimedia Interpretation and Retrieval In: Y. Kompatsiaris, P. Hobson (Eds.): Semantic Multimedia and Ontologies: Theory and Applications Springer 2008, pp. 55-98
    [pdf]
  • Neumann, B.
    Bayesian Compositional Hierarchies - A Probabilistic Structure for Scene Interpretation Technical Report, Universität Hamburg, Department Informatik, Arbeitsbereich Kognitive Systeme, 2008(FBI-HH-B-282/08)
    [pdf]
  • Terzić, K. & Neumann, B.
    Decision Trees for Probabilistic Top-down and Bottom-up Integration Technical Report, Universität Hamburg, 2009(FBI-HH-B-288/09)
    [pdf]
04.01.2010