Martin-Luther-Universität Halle
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Institute of Computer Science

Pattern Recognition and Bioinformatics
Abstracts  
[1] Birgit Möller and Stefan Posch. MiCA - easy cell image analysis with normalized snakes. In International Workshop on Microscopic Image Analysis with Applications in Biology, September 2011.
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[2] Jens Keilwagen, Jan Grau, Ivan A. Paponov, Stefan Posch, Marc Strickert, and Ivo Grosse. De-novo discovery of differentially abundant transcription factor binding sites including their positional preference. PLoS Comput Biol, 7(2):e1001070, February 2011.
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<title>Author Summary</title> <p>Binding of transcription factors to promoters of genes, and subsequent enhancement or repression of transcription, is one of the main steps of transcriptional gene regulation. Direct or indirect wet-lab experiments allow the identification of approximate regions potentially bound or regulated by a transcription factor. Subsequently, de-novo motif discovery tools can be used for detecting the precise positions of binding sites. Many traditional tools focus on motifs over-represented in the target regions, which often turn out to be similarly over-represented in the entire genome. In contrast, several recent tools focus on differentially abundant motifs in target regions compared to a control set. As binding sites are often located at some preferred distance to the transcription start site, it is favorable to include this information into de-novo motif discovery. Here, we present Dispom a novel approach for learning differentially abundant motifs and their positional preferences simultaneously, which predicts binding sites with increased accuracy compared to many popular de-novo motif discovery tools. When applying Dispom to promoters of auxin-responsive genes of Arabidopsis thaliana, we find a binding motif slightly different from the canonical auxin-response element, which exhibits a strong positional preference and which is considerably more specific to auxin-responsive genes.</p>
[3] Manuela Hesse, Edith Willscher, Benjamin Schmiedel, Stefan Posch, Ralph Golbik, and Martin Staege. Sequence and expression of the chicken membrane-associated phospholipases a1 alpha (liph) and beta (lipi). Molecular Biology Reports, pages 1-9, 2011. 10.1007/s11033-011-0796-0.
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[4] Markus Glaß, Birgit Möller, Anne Zirkel, Kristin Wächter, Stefan Hüttelmaier, and Stefan Posch. Scratch assay analysis with topology-preserving level sets and texture measures. In Proc. Iberian Conference on Pattern Recognition and Image Analysis, pages 100-108, Gran Canaria, 2011.
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[5] Birgit Möller, Oliver Greß, and Stefan Posch. Knowing what happened - automatic documentation of image analysis processes. In J.L. Crowley, B.A. Draper, and M. Thonnat, editors, Proceedings of 8th International Conference on Computer Vision Systems, volume 6962 of LNCS, pages 1-10. Springer, 2011.
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Keywords: automatic documentation, meta data, XML, processing graph, image analysis
[6] Oliver Greß, Birgit Möller, Nadine Stöhr, Stefan Hüttelmaier, and Stefan Posch. Scale-adaptive wavelet-based particle detection in microscopy images. In Hans-Peter Meinzer, Thomas Martin Deserno, Heinz Handels, and Thomas Tolxdorff, editors, Bildverarbeitung für die Medizin, pages 266-270, Berlin, 2010. Springer.
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[7] Birgit Möller, Oliver Greß, Nadine Stöhr, Stefan Hüttelmaier, and Stefan Posch. Adaptive segmentation of cells and particles in fluorescent microscope images. In Proc. of International Conference on Computer Vision Theory and Applications (VISAPP '10), volume 2, pages 97-106, 2010.
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[8] Stefan Posch, Jan Grau, André Gohr, Jens Keilwagen, and Ivo Grosse. Probabilistic Approaches to Transcription Factor Binding Site Prediction, volume 674 of Methods in Molecular Biology, chapter 7. Springer Press, 2010.
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[9] Jens Keilwagen, Jan Grau, Stefan Posch, Marc Strickert, and Ivo Grosse. Unifying generative and discriminative learning principles. BMC Bioinformatics, 11(1):98, 2010.
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BACKGROUND:The recognition of functional binding sites in genomic DNA remains one of the fundamental challenges of genome research. During the last decades, a plethora of different and well-adapted models has been developed, but only little attention has be payed to the development of different and similarly well-adapted learning principles. Only recently it was noticed that discriminative learning principles can be superior over generative ones in diverse bioinformatics applications, too.RESULTS:Here, we propose a generalization of generative and discriminative learning principles containing the maximum likelihood, maximum a-posteriori, maximum conditional likelihood, maximum supervised posterior, generative-discriminative trade-off, and penalized generative-discriminative trade-off learning principles as special cases, and we illustrate its efficacy for the recognition of vertebrate transcription factor binding sites.CONCLUSIONS:We find that the proposed learning principle helps to improve the recognition of transcription factor binding sites and enables better computational approaches for extracting as much information as possible from valuable wet-lab data. We make all implementations available in the open-source library Jstacs so that this learning principle can be easily applied to other classification problems in the field of genome and epigenome analysis.
[10] Jens Keilwagen, Jan Grau, Stefan Posch, and Ivo Grosse. Apples and oranges: avoiding different priors in Bayesian DNA sequence analysis. BMC Bioinformatics, 11(1):149, 2010.
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BACKGROUND: One of the challenges of bioinformatics remains the recognition of short signal sequences in genomic DNA such as donor or acceptor splice sites, splicing enhancers or silencers, translation initiation sites, transcription start sites, transcription factor binding sites, nucleosome binding sites, miRNA binding sites, or insulator binding sites. During the last decade, a wealth of algorithms for the recognition of such DNA sequences has been developed and compared with the goal of improving their performance and to deepen our understanding of the underlying cellular processes. Most of these algorithms are based on statistical models belonging to the family of Markov random fields such as position weight matrix models, weight array matrix models, Markov models of higher order, or moral Bayesian networks. While in many comparative studies different learning principles or different statistical models have been compared, the influence of choosing different prior distributions for the model parameters when using different learning principles has been overlooked, and possibly lead to questionable conclusions. RESULTS: With the goal of allowing direct comparisons of different learning principles for models from the family of Markov random fields based on the same a-priori information, we derive a generalization of the commonly-used product-Dirichlet prior. We find that the derived prior behaves like a Gaussian prior close to the maximum and like a Laplace prior in the far tails. In two case studies, we illustrate the utility of the derived prior for a direct comparison of different learning principles with different models for the recognition of binding sites of the transcription factor Sp1 and human donor splice sites. CONCLUSIONS: We find that comparisons of different learning principles using the same a-priori information can lead to conclusions different from those of previous studies in which the effect resulting from different priors has been neglected. We implement the derived prior is implemented in the open-source library Jstacs to enable an easy application to comparative studies of different learning principles in the field of sequence analysis.
[11] Birgit Möller, Nadine Stöhr, Stefan Hüttelmaier, and Stefan Posch. Cascaded segmentation of grained cell tissue with active contour models. In Proceedings International Conference on Pattern Recognition, pages 1481-1484, 2010.
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[12] Jan Grau, Daniel Arend, Ivo Grosse, Artemis G. Hatzigeorgiou, Jens Keilwagen, Manolis Maragkakis, Claus Weinholdt, and Stefan Posch. Predicting miRNA targets utilizing an extended profile HMM. In Dietmar Schomburg and Andreas Grote, editors, German Conference on Bioinformatics, volume P-173 of Lecture Notes in Informatics (LNI) - Proceedings, pages 81-91, Bonn, 2010. Gesellschaft für Informatik.
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[13] Birgit Möller and Stefan Posch. Robust features for 2-d electrophoresis gel image registration. Electrophoresis, 30:4137-4148, 2009.
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[14] Danny Misiak, Stefan Posch, Nadine Stöhr, Stefan Hüttelmaier, and Birgit Möller. Automatic analysis of fluorescence labeled neurites in microscope images. In IEEE Workshop on Applications of Computer Vision (WACV '09), pages 118-124, 2009. IEEE Catalog Number: CFP09082-CDR.
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[15] Ivo Grosse, Steffen Neumann, Stefan Posch, Falk Schreiber, and Peter F. Stadler, editors. German Conference on Bioinformatics 2009, volume 157 of LNI. GI, 2009.
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[16] Birgit Möller, Thomas Plötz, and Gernot Fink. Calibration-free camera hand-over for fast and reliable person tracking in multi-camera setups. In Proc. of Int. Conf. on Pattern Recognition (ICPR '08), Tampa, Florida, USA, December 2008. to appear.
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[17] Armagan Elibol, Birgit Möller, and Rafael Garcia. Perspectives of auto-correcting lens distortions in mosaic-based underwater navigation. In Proc. of 23rd IEEE Int. Symposium on Computer and Information Sciences (ISCIS '08), pages 1-6, Istanbul, Turkey, October 2008.
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[18] Birgit Möller, Oliver Gress, and Stefan Posch. A comparative study of robust feature detectors for 2d electrophoresis gel image registration. In Proc. of German Conference on Bioinformatics, LNI P-136, pages 138-147, Dresden, Germany, September 2008.
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[19] Jörg Wensch, Alf Gerisch, and Stefan Posch. Optimised coupling of hierarchies in image registration. Image and Vision Computing, 26(7), 2008.
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[20] Birgit Möller and Stefan Posch. An integrated analysis concept for errors in image registration. Int. Journal on Pattern Recognition and Image Analysis (PRIA), 18(2):201-206, 2008.
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[21] Birgit Möller and Stefan Posch. An iconic scene memory approach for mobile robots interacting with humans. Technical Report 2007-03, Institute of Computer Science, Martin-Luther-University Halle-Wittenberg, Germany, December 2007.
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[22] Birgit Möller and Stefan Posch. Identifying lens distortions in image registration by learning from examples. In Proc. of British Machine Vision Conference (BMVC '07), pages I:152-161, University of Warwick, Coventry, UK, September 2007.
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[23] Birgit Möller and Stefan Posch. An integrated analysis concept for errors in image registration. In Proc. of 7th Open German/Russian Workshop on Pattern Recognition and Image Understanding (OGRW '07), Ettlingen, Germany, August 2007.
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[24] Birgit Möller and Stefan Posch. Automatic analysis of lens distortions in image registration. In Proc. of Int. Conf. on Computer Vision Systems (ICVS '07), Workshop on Camera Calibration Methods for Computer Vision Systems (CCMVS '07), Bielefeld, Germany, March 2007.
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[25] Birgit Möller, Rafael Garcia, and Stefan Posch. Towards objective quality assessment of image registration results. In Proc. of International Conference on Computer Vision Theory and Applications (VISAPP '07), pages 233-240, Barcelona, Spain, March 2007.
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[26] Lennart Opitz, Alexander Schliep, and Stefan Posch. Joint analysis of in-situ hybridization and gene expression data. In Reinhold Decker and Hans J. Lenz, editors, Advances in Data Analysis: Proceedings of the 30th Annual Conference of the Gesellschaft für Klassifikation e.V.,, pages 577-584. Springer, 2007.
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[27] Stefan Posch, Jan Grau, Andre Gohr, Irad Ben-Gal, Alexander Kel, and Ivo Grosse. Recognition of cis-regulatory elements with VOMBAT. Journal of Bioinformatics and Computational Biology, 5(02B):561-577, 2007.
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[28] Jan Grau, Jens Keilwagen, Alexander Kel, Ivo Grosse, and Stefan Posch. Supervised posteriors for dna-motif classification. In German Conference on Bioinformtics, pages 123-134, 2007.
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[29] Jan Grau, Jens Keilwagen, Ivo Grosse, and Stefan Posch. On the relevance of model orders to discriminative learning of markov models. In Alexander Hinneburg, editor, LWA: Lernen - Wissen - Adaption, pages 61-66, 2007.
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[30] Jens Keilwagen, Jan Grau, Stefan Posch, and Ivo Grosse. Recognition of splice sites using maximum conditional likelihood. In Alexander Hinneburg, editor, LWA: Lernen - Wissen - Adaption, pages 67-72, 2007.
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[31] Birgit Möller and Stefan Posch. A space- and time-efficient mosaic-based iconic memory for interactive systems. In Proc. of International Conference on Computer Vision Theory and Applications (VISAPP '06), pages 413-421, Setúbal, Portugal, February 2006.
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[32] Jan Grau, Irad Ben-Gal, Andre Gohr, Alexander Kel, Olga Kel-Margoulis, Stefan Posch, and Ivo Grosse. Prediction of eukaryotic transcription factor binding sites using variable order markov models. In GCB, pages 5-6, 2006.
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[33] Jan Grau, Irad Ben-Gal, Ivo Grosse, and Stefan Posch. Vombat: A web-server for predicting transcription factor binding sites using variable order markov models and variable order bayesian trees. In GCB, Short paper, 2006.
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[34] Yvonne Pöschl, Christoph Böttcher, Stephan Clemens, Dierk Scheel, Stefan Posch, and Steffen Neumann. Analysis of metabolite relations in lcms data using bayesian networks. In GCB, Short paper, pages 17-18, 2006.
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[35] Jan Grau, Irad Ben-Gal, Stefan Posch, and Ivo Grosse. VOMBAT: Prediction of transcription factor binding sites using variable order bayesian trees. Nucleic Acids Research, 34(suppl_2):W529-533, 2006.
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[36] Gregor Erz and Stefan Posch. Root detection by hierarchical seed expansion. In Proceedings of Eurocon 2005, pages 963-966, Belgrade, November 2005. IEEE Computer Society Press.
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[37] Birgit Möller, Stefan Posch, Axel Haasch, Jannik Fritsch, and Gerhard Sagerer. Interactive object learning for robot companions using mosaic images. In Proc. IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, pages 371-376, Edmonton, Alberta, Canada, August 2005.
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[38] Birgit Möller and Stefan Posch. A mosaic-based visual memory with applications to active scene exploration. In Proc. of Mirage, Computer Vision / Computer Graphics Collaboration Techniques and Applications, pages 117-125, INRIA Rocquencourt, France, March 2005.
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[39] Birgit Möller and Stefan Posch. Visual scene memory based on multi-mosaics. In Dietrich Paulus and Detlev Droege, editors, Mixed-reality as a challenge to image understanding and artificial intelligence, pages 27-32, Koblenz, 2005. Universität Koblenz-Landau, Institut für Informatik.
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[40] Gregor Erz, Maik Veste, Heiko Anlauf, Siegmar-Walter Breckle, and Stefan Posch. A region and contour based technique for automatic detection of of tomatoe roots in minirhizotron images. Journal of Applied Botany and Food Quality, 79:83-88, 2005.
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[41] I. Ben-Gal, A. Shani, A. Gohr, J. Grau, S. Arviv, A. Shmilovici, S. Posch, and I. Grosse. Identification of transcription factor binding sites with variable-order bayesian networks. Bioinformatics, 21:2657-2666, 2005.
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[42] Irad Ben-Gal, Sigal Arviv, Andre Gohr, Jan Grau an d Stefan Posch, Armin Shmilovici, and Ivo Grosse. Computational identification of transcription factor binding si tes with variable-order markov models. In ISMB/ECCB, Poster Abstract, 2004.
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[43] Andreas Stephanik, Steffen Neumann, Lothar Altschmied, David L. Müller, Stefan Posch, and Ivo Grosse. SMArrT: Smarrt: An integrated workflow for array an integrate d workflow for array analysis. In GCB, Poster Abstract, 2004.
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[44] Birgit Möller, Denis Williams, and Stefan Posch. Towards a mosaic-based visual representation of large scenes. Int. Journal on Pattern Recognition and Image Analysis (PRIA), Spec. Issue, 14(2):262-266, 2004.
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[45] Gregor Erz and Stefan Posch. A region based seed detection for root detection in minirhizotron images. In B. Michaelis and G. Krell, editors, Pattern Recognition, Proc. of 25th DAGM Symposium, LNCS 2781, pages 482-489, Magdeburg, Germany, September 2003. Springer.
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[46] Birgit Möller, Denis Williams, and Stefan Posch. Robust image sequence mosaicing. In B. Michaelis and G. Krell, editors, Pattern Recognition, Proc. of 25th DAGM Symposium, LNCS 2781, pages 386-293, Magdeburg, Germany, September 2003. Springer.
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[47] Birgit Möller, Denis Williams, and Stefan Posch. Towards a mosaic-based visual representation of large scenes. In Proc. of 6th Open German-Russian Workshop (IAPR), pages 108-111, Katun Village, Altai Region, Russian Federation, 25.-30. August 2003.
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[48] Jörg Weitzenberg and Stefan Posch. Using Hidden-Markov-Models to analyze Concentrations from Biosensor Curves. In Proc. Int. Conference Signal Processing, Pattern Recognition, & Applications, pages 98-103, Rhodes, 2003. IASTED.
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[49] Denis Williams, Birgit Möller, and Stefan Posch. Integrated system for a visual memory based on mosaics. In H.R. Arabnia and Youngsong Mun, editors, Proc. of International Conference on Imaging Science, Systems, and Technology (CISST'03), pages II: 633-639, Las Vegas, USA, 23.-26.June 2003. CSREA Press.
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[50] Birgit Möller and Stefan Posch. Analysis of object interactions in dynamic scenes. In L. van Gool, editor, Pattern Recognition. 24. DAGM-Symposium, LNCS 2449, pages 361-369, Zurich, Switzerland, September 2002. Springer.
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[51] Jörg Weitzenberg, Stefan Posch, and Manfred Rost. Analysis of amperometric biosensor curves using hidden-markov-modells. In Luc Van Gool, editor, Pattern Recognition. 24. DAGM-Symposium, pages 182-189, 2002.
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[52] Birgit Möller and Stefan Posch. Detection and tracking of moving objects for mosaic image generation. In B. Radig and S. Florczyk, editors, Pattern Recognition, Proc. of 23rd DAGM Symposium, LNCS 2191, pages 208-215, Munich, Germany, September 2001. Springer.
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[53] J. Weitzenberg, S. Posch, C. Bauer, M. Rost, and B. Gründig. Analysis of amperometric biosensor data using fuzzy logic and discrete hidden-markov-models. In H.R. Tränkler, editor, SENSOR 2001, pages Volume II: 493-498, 2001.
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[54] Daniel Schlüter, Sven Wachsmuth, Gerhard Sagerer, and Stefan Posch. Towards an integrated framework for contour-based grouping and object recognition using markov random fields. In Proc. International Conference on Image Processing, volume II, pages 100-103. IEEE, 2000.
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[55] Daniel Schlüter, Franz Kummert, Gerhard Sagerer, and Stefan Posch. Integration of regions and contours for object recognition. In Proc. International Conference on Pattern Recognition, volume 1, pages 944-947. IEEE, 2000.
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[56] Denis Williams and Stefan Posch. Stereozuordnung von hierachischen Konturgruppen mit Markov Random Fields. In Gerald Sommer, Norbert Krüger, and Christian Perwass, editors, Mustererkennug 2000. Proceedings 22. DAGM-Symposium, Informatik Aktuell, pages 7-16. Springer, 2000.
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[57] Jörg Weitzenberg, Stefan Posch, and Manfred Rost. Diskrete Hidden Markov Modelle zur Analyse von Meßkurven amperometrischer Biosensoren. In Gerald Sommer, Norbert Krüger, and Christian Perwass, editors, Mustererkennug 2000. Proceedings 22. DAGM-Symposium, Informatik Aktuell, pages 317-324. Springer, 2000.
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[58] Steffen Neumann, Stefan Posch, and Gerhard Sagerer. Towards evaluation of docking hypotheses using elasic matching. In Computer Sciene and Biology: Proceedings of the German Conference on Bioinformatics, page 220, October 1999.
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[59] Stefan Posch. Perzeptives Gruppieren und Bildanalyse. Deutscher Universitäts Verlag, 1999.
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[60] Christian Bauckhage, Gernot A. Fink, Franz Kummert, Stefan Posch, Gerhard Sagerer, and Daniel Schlüter. Towards and image understanding architecture for a situated artificial communicator. In Bernd Radig, Heinrich Niemann, Yuri Zhuravlev, Igor Gourevitch, and Ivan Laptev, editors, Pattern Recognition and Image Understanding, pages 203-210, Sankt Augustin, 1999. infix.
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[61] Friedrich Ackermann, Grit Herrmann, Stefan Posch, and Gerhard Sagerer. Estimation and filtering of potential protein-protein docking positions. Bioinformatics, 14(2):196-205, 1998.
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[62] Stefan Posch and Helge Ritter, editors. Dynamische Perzeption, Proceedings on Artificial Intelligence. infix, 1998.
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[63] Daniel Schlüter and Stefan Posch. Combining contour and region information for perceptual grouping. In P. Levi, R.-J. Ahlers, F. May, and M. Schanz, editors, Mustererkennug 1998. Proceedings 20. DAGM-Symposium, Informatik Aktuell, pages 393-401. Springer, 1998.
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[64] Stefan Posch and Daniel Schlüter. Perceptual grouping using markov random fields and cue integration of contour and region information. Technical Report 98/10, SFB 360, Universität Bielefeld, 1998.
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[65] A. Maßmann, S. Posch, G. Sagerer, and D. Schlüter. Using markov random fields for contour-based grouping. In Proc. International Conference on Image Processing, volume II, pages 207-210. IEEE, 1997.
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[66] Thorsten Bomberg and Stefan Posch. Regionensegmentierung von Farbbildfolgen. In E. Paulus and F.M.Wahl, editors, Mustererkennug 1997. Proceedings 19. DAGM-Symposium, Informatik Aktuell, pages 63-70. Springer, 1997.
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[67] Grit Herrmann, Franz Kummert, Stefan Posch, and Gerhard Sagerer. Kontrollstrategie für ein wissensbasiertes System zur Makromolekularen Erkennung. In E. Paulus and F.M.Wahl, editors, Mustererkennug 1997. Proceedings 19. DAGM-Symposium, Informatik Aktuell, pages 602-609. Springer, 1997.
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[68] F. Ackermann, A. Maßmann, S. Posch, G. Sagerer, and D. Schlüter. Perceptual grouping of contour segments using markov random fields. International Journal of Pattern Recognition and Image Analysis, 7(1):11-17, 1997.
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[69] A. Maßmann, S. Posch, and D. Schlüter. Perzeptives Gruppieren von Ko- und Kurvilinearitäten mittels Markov Random Fields. In B. Jähne, P. Geißler, H. Haußecker, and F. Hering, editors, Mustererkennug 1996. Proceedings 18. DAGM-Symposium, Informatik Aktuell, pages 235-242. Springer, 1996.
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[70] F. Ackermann, G. Herrmann, S. Posch, and G. Sagerer. Evaluierung eines Protein-Dockingsystems durch Leave-One-Out-Test. In B. Jähne, P. Geißler, H. Haußecker, and F. Hering, editors, Mustererkennug 1996. Proceedings 18. DAGM-Symposium, Informatik Aktuell, pages 130-137. Springer, 1996.
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[71] R. Biermann, F. Ackermann, and S. Posch. Robustness of geometrical shape descriptors for molecular surfaces. In R. Hofestädt, Löffler M., Lengauer T., and Schomburg D., editors, Computer Science and Biology: Proceedings of the German Conference on Bioinformatics (GCB96), pages 171-173. Universität Leipzig, 1996.
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[72] T. Klein, F. Ackermann, and S. Posch. viwish: A visualisation server for protein modelling and docking. Gene-COMBIS, 183:51-58, 1996.
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[73] G. Herrmann, Möller A., and S. Posch. Statistical segmentation of molecular surfaces. In R. Hofestädt, Löffler M., Lengauer T., and Schomburg D., editors, Computer Science and Biology: Proceedings of the German Conference on Bioinformatics (GCB96), pages 218-220. Universität Leipzig, 1996.
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[74] R. Meier, F. Ackermann, G. Herrmann, S. Posch, and G. Sagerer. Segmentation of molecular surfaces based on their convex hull. In Proc. International Conference on Image Processing, volume III, pages 552-555. IEEE, October 1995.
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[75] C. Schillo, F. Ackermann, G. Herrmann, S. Posch, and G. Sagerer. Statistical classification and segmentation of biomolecular surfaces. In Proc. International Conference on Image Processing, volume III, pages 560-563. IEEE, October 1995.
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[76] F. Ackermann, G. Herrmann, F. Kummert, S. Posch, G. Sagerer, and D. Schomburg. Protein docking combining symbolic descriptions of molecular surfaces and grid-based scoring functions. In The Third Int. Conference on Intelligent Systems for Molecular Biology, pages 3-11, July 1995.
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[77] M. Jankowski, S.-W. Breckle, S. Posch, G. Sagerer, and M. Veste. Automatische Detektion von Wurzelsystemen in Minirhizotron-Bildern. In G. Sagerer, S. Posch, and F. Kummert, editors, Mustererkennug 1995. Proceedings 17. DAGM-Symposium, Informatik Aktuell, pages 176-185. Springer, 1995.
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[78] G. Socher, T. Merz, and S. Posch. Ellipsenbasierte 3-d Rekonstruktion. In G. Sagerer, S. Posch, and F. Kummert, editors, Mustererkennug 1995. Proceedings 17. DAGM-Symposium, Informatik Aktuell, pages 252-259. Springer, 1995.
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[79] A. Maßmann and S. Posch. Bereiche perzeptiver Aufmerksamkeit für konturbasierte Gruppierung. In G. Sagerer, S. Posch, and F. Kummert, editors, Mustererkennug 1995. Proceedings 17. DAGM-Symposium, Informatik Aktuell, pages 602-609. Springer, 1995.
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[80] G. Socher, T. Merz, and S. Posch. 3-d reconstruction and camera calibration from images with known objects. In Proc. 6th British Machine Vision Converence, pages 167-176, 1995.
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[81] F. Ackermann, G. Herrmann, S. Posch, and G. Sagerer. 3d-segmentation and vectorvalued scoring functions for symbolic docking of proteins. In D. Schomburg and U. Lessel, editors, Bioinformatics: From Nucleic Acids and Proteins to the Cell Metabolism, number 18 in GBF Monograhs, pages 105-124. VCH Publishers, 1995.
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[82] A. Maßmann and S. Posch. Mask-oriented grouping operations in a contour-based approach. In Proc. 2nd Asian Conference on Computer Vision, volume 3, pages 58-61, Singapore, 1995. IEEE.
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[83] R. Moratz, G. Heidemann, S. Posch, H. Ritter, and G. Sagerer. Representing procedural knowledge for semantic networks using neural nets. Proc. 9th Scandinavian Conference on Image Processing, pages 819-828, 1995.
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[84] G. Sagerer, S. Posch, and F. Kummert, editors. Mustererkennug 1995. Proceedings 17. DAGM-Symposium, Informatik Aktuell. Springer, 1995.
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[85] R. Moratz, S. Posch, and G. Sagerer. Controlling multiple neural nets with semantic networks. Proceedings 16. DAGM-Symposium, pages 288-295, 1994.
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[86] Friedrich Ackermann, Grit Porczynski, Stefan Posch, and Gerhard Sagerer. Hierarchische Modellierung und Bewertung von Proteindockingpositionen. In Proc. Bioinformatik - Computereinsatz in der Biowissenschaft, pages 19-29, Jena, 1994. Insitut für Molekulare Biotechnologie.
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[87] A. Drees, F. Kummert, E. Littmann, S. Posch, H. Ritter, and G. Sagerer. A hybrid system to detect hand orientation in stereo images. In E.S. Gelsema and L.N. Kanal, editors, Pattern Recognition in Practice IV: Multiple Paradigms, Comparative Studies and Hybrid Systems, pages 551-562. Elsevier, Amsterdam, 1994.
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[88] Thomas Fuhr, Franz Kummert, Stefan Posch, and Gerhard Sagerer. An approach for qualitatively predicting relations from relations. In Erik Sandewall and Carl Gustaf Jansson, editors, Proc. Scandinavian Conference on Artifical Intelligence, pages 38-49, Amsterdam, 1993. IOS Press.
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[89] F. Kummert, E. Littmann, A. Meyering, S. Posch, H. Ritter, and G. Sagerer. A hybrid approach to signal interpretation using neural and semantic networks. In S. Pöppel and H. Handels, editors, Mustererkennug 1993. Proceedings 15. DAGM-Symposium, pages 245-252. Springer Verlag, 1993.
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[90] F. Kummert, E. Littmann, A. Meyering, S. Posch, H. Ritter, and G. Sagerer. Recognition of 3d-hand orientation from monocular color images by neural semantic networks. International Journal of Pattern Recognition and Image Analysis, 3(3):311-316, 1993.
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[91] Stefan Posch. Stereozuordnung mit geraden Liniensegmenten und Polygonen. Proceedings 14. DAGM-Symposium, pages 385-391, 1992.
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[92] Stefan Posch. Detecting skewed symmetries. Proc. 11. Int. Conf. on Pattern Recognition (ICPR), pages 602-606, 1992.
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[93] Stefan Posch. Detecting skewed symmetries. Technical report, Tech. Report TR-91-058, International Computer Science Institute, October 1991.
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[94] Stefan Posch. Automatische Tiefenbestimmung aus Grauwertstereobildern. Deutscher Universitäts Verlag, Wiesbaden, 1990.
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[95] Stefan Posch. Parallele Implementierung eines hierarchischen linienbasierten Stereoverfahrens. In Bernd Mohr, editor, Grundlagen verteilter und paralleler Systeme. Teil II, pages 101-114. Bericht 90/1 des SFB 182, Universität Erlangen-Nürnberg, Institut für mathematische Maschinen und Datenverarbeitung, Erlangen, 1990.
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[96] Stefan Posch. Parallele Implementierung eines hierarchischen linienbasierten Stereoverfahrens. In R. Großkopf, editor, Proceedings 12. DAGM-Symposium, Informatik-Fachberichte 254, pages 356-363, Berlin, 1990. Springer.
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[97] H. Niemann, A. Brietzmann, U. Ehrlich, S. Posch, P. Regel, G. Sagerer, R. Salzbrunn, and G. Schukat-Talamazzini. A knowledge based speech understandig system. International Journal of Pattern Recognition and Artificial Intelligence, 2(2):321-350, 1988.
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[98] Stefan Posch. Hierarchische linienbasierte Tiefenbestimmung in einem Stereobild. In Wolfgang Hoeppner, editor, Proc. 12. German Workshop on Artificial Intelligence, pages 275-285, Berlin, Sep. 1988. Springer.
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[99] Astrid Brietzmann and Stefan Posch. Suchstrategien zur syntaktischen Analyse in der automatischen Spracherkennung. In G. Hartmann, editor, Proceedings 8. DAGM-Symposium, Informatik Fachberichte 125, pages 139-143, Berlin, 1986. Springer.
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Author Prof. Dr.-Ing. Stefan Posch  last update: 18-Nov-11