PD Dr. Rolf Würtz

Institut für Neuroinformatik
Ruhr-Universität Bochum
Universitätsstraße 150
Building NB, Room NB 3/66
D-44801 Bochum
Germany
Theory of Neural Systems

Groups

  • Organic Computing
  • Image understanding
  • Neural networks
  • Visual object recognition
  • Face recognition
  • Visual memory
  • Autonomous learning of object representations
  • Integration of visual, tactile, and proprioceptive information

Grieben, R. (2013, January). Invariante Objekterkennung mittels Ähnlichkeitsranglisten. ET-IT Dept., Univ. of Bochum, Germany.
Tomforde, S., Rudolph, S., Bellman, K., & Würtz, R. P. (2016). "Self-Improving System Integration" – Preface for the SISSY′16 Workshop. In Proc. ICAC (p. 275). http://doi.org/10.1109/ICAC.2016.74
Tomforde, S., Rudolph, S., Bellman, K., & Würtz, R. P. (2016). An Organic Computing Perspective on Self-Improving System Interweaving at Runtime. In Proc. ICAC (pp. 276–284). http://doi.org/10.1109/ICAC.2016.15
Fahmy, G., Alqallaf, A., & Wurtz, R. (2015). Phase based detection of JPEG counter forensics. In 2015 IEEE International Conference on Electronics, Circuits, and Systems (ICECS) (pp. 37–40). http://doi.org/10.1109/ICECS.2015.7440243
Bodenstein, C., Tremer, M., Overhoff, J., & Würtz, R. P. (2015). A smartphone-controlled autonomous robot. In Proceedings of the 12th International Conference on Fuzzy Systems and Knowledge Discovery, Zhangjiajie, China, Aug. 15-17 (pp. 2360–2367).
Günther, M., Böhringer, S., Wieczorek, D., & Würtz, R. P. (2015). Reconstruction of Images from Gabor Graphs with Applications in Facial Image Processing. International Journal of Wavelets, Multiresolution and Information Processing, 13(4), 1550019-1–25. http://doi.org/10.1142/S0219691315500198
Kosilek, R. P., Frohner, R., Würtz, R. P., Berr, C. M., Schopohl, J., Reincke, M., & Schneider, H. J. (2015). Automatic face classification in Cushing′s syndrome and acromegaly: Review, current results and future perspectives. European Journal of Endocrinology, 173(4), M39–M44. http://doi.org/10.1530/EJE-15-0429
Balliu, B., Würtz, R. P., Horsthemke, B., Wieczorek, D., & Böhringer, S. (2014). Classification and visualization based on derived image features: application to genetic syndromes. PLOS One, 9(11), e109033. http://doi.org/10.1371/journal.pone.0109033
Bellman, K., Tomforde, S., & Würtz, R. P. (2014). "Self-Improving System Integration" – Preface for the SISSY14 Workshop. In Proc. SASO, London (p. 122). IEEE.
Bellman, K., Tomforde, S., & Würtz, R. P. (2014). Interwoven Systems: Self-improving Systems Integration. In Proc. SASO, London (pp. 123–127). IEEE.
Krause, T. U., Schrör, P. Y., & Würtz, R. P. (2014). Spiking network simulations. In B. Hammer, Martinetz, T., & Villmann, T. (Eds.), Proceedings of New Challenges in Neural Computation, Münster (pp. 14–15).
Lessmann, M., & Würtz, R. P. (2014). Learning of invariant object recognition in a hierarchical network. Neural Networks, 54, 70–84. http://doi.org/10.1016/j.neunet.2014.02.011
Lessmann, M., & Würtz, R. P. (2014). Online Learning of Invariant Object Recognition in a Hierarchical Neural Network. In S. Wermter, Weber, C., Duch, W., Honkela, T., Koprinkova-Hristova, P., Magg, S., et al. (Eds.), Proc. ICANN (pp. 427–434). Springer.
Wiskott, L., Würtz, R. P., & Westphal, G. (2014). Elastic Bunch Graph Matching. Scholarpedia, 9, 10587. http://doi.org/10.4249/scholarpedia.10587
Frohner, R., Würtz, R. P., Kosilek, R. P., & Schneider, H. J. (2013). Optimierung der Gesichtsklassifikation bei der Erkennung von Akromegalie. Austrian Journal of Clinical Endocrinology and Metabolism, 6(3), 20–24.
Kosilek, R. P., Schopohl, J., Grünke, M., Dimopoulou, C., Stalla, G. K., Lammert, A., et al. (2013). Automatic face classification of Cushing’s syndrome in women - A novel screening approach. Experimental and Clinical Endocrinology and Diabetes. http://doi.org/10.1055/s-0033-1349124
Müller, M. K., Tremer, M., Bodenstein, C., & Würtz, R. P. (2013). Learning invariant face recognition from examples. Neural Networks, 41, 137–146. http://doi.org/10.1016/j.neunet.2012.07.006
Walther, T., & Würtz, R. P. (2012). Unsupervised Learning of Face Detection Models from Unlabeled Image Streams. In A. Brömme & Busch, C. (Eds.), Proceedings of the 11th International Conference of the Biometrics Special Interest Group (Vol. P-196, pp. 221–231). Bonn: Köllen.
Müller, M. K., Tremer, M., Bodenstein, C., & Würtz, R. P. (2012). Lernen situationsunabhängiger Personenerkennung. Informatikspektrum, 35(2), 112–118. http://doi.org/10.1007/s00287-012-0598-3
Böhringer, S., van der Lijn, F., Liu, F., Sinigerova, S., Birnbaum, S., Mangold, E., et al. (2011). Genetic determination of the human facial shape: links between cleft-lips and normal variation. European Journal of Human Genetics, 19, 1192–1197. http://doi.org/10.1038/ejhg.2011.110
Schneider, H. J., Kosilek, R. P., Günther, M., Schopohl, J., Römmler, J., Stalla, G. K., et al. (2011). A novel approach for the detection of acromegaly: accuracy of diagnosis by automatic face classification. Journal of Clinical Endocrinology and Metabolism, 96(7), 2074–2080. http://doi.org/10.1210/jc.2011-0237
Walther, T., & Würtz, R. P. (2011). Learning to Look at Humans. In C. Müller-Schloer, Schmeck, H., & Ungerer, T. (Eds.), Organic Computing - a Paradigm Shift for Complex Systems (pp. 309–322). Springer. http://doi.org/10.1007/978-3-0348-0130-0_20
Donatti, G. S., Lomp, O., & Würtz, R. P. (2010). Evolutionary Optimization of Growing Neural Gas Parameters for Object Categorization and Recognition. In Proc. IJCNN (pp. 1862–1869). IEEE Computer Society, Los Alamitos, CA.
Günther, M., Müller, M. K., & Würtz, R. P. (2010). Two kinds of statistics for better face recognition. In T. E. Simos, Psihoyios, G., & Tsitouras, C. (Eds.), Numerical Analysis and Applied Mathematics, International Conference (pp. 1901–1904). American Institute of Physics.
Walther, T., & Würtz, R. P. (2010). Learning Generic Human Body Models. In F. J. Perales & Fisher, R. B. (Eds.), Proc. Sixth Conference on Articulated Motion and Deformable Objects (pp. 98–107). Springer.
Würtz, R. P., Bellman, K. L., Schmeck, H., & Igel, C. (2010). Editorial: Special Issue on Organic Computing. ACM Transactions on Autonomous and Adaptive Systems, 5(3), 1–3. http://doi.org/10.1145/1837909.1837910
Donatti, G. S., & Würtz, R. P. (2009). Using Growing Neural Gas Networks to represent visual object knowledge. In Proceedings of the 21st IEEE International Conference on Tools with Artificial Intelligence (pp. 54–58). IEEE Computer Society, Newark, NJ.
Günther, M., & Würtz, R. P. (2009). Face Detection and Recognition Using Maximum Likelihood Classifiers on Gabor Graphs. International Journal of Pattern Recognition and Artificial Intelligence, 23(3), 433–461. http://doi.org/10.1142/S0218001409007211
Müller, M. K., & Würtz, R. P. (2009). Learning from Examples to Generalize over Pose and Illumination. In C. Alippi, Polycarpou, M., Panayiotou, C., & Ellinas, G. (Eds.), Artificial Neural Networks - ICANN 2009 (Vol. 5769, pp. 643–652). Springer.
Walther, T., & Würtz, R. P. (2009). Unsupervised learning of human body parts from video footage. In Proceedings of ICCV workshops, Kyoto (pp. 336–343). IEEE Computer Society, Los Alamitos, CA.
Westphal, G., & Würtz, R. P. (2009). Combining Feature- and Correspondence-Based Methods for Visual Object Recognition. Neural Computation, 21(7), 1952–1989. http://doi.org/10.1162/neco.2009.12-07-675
Westphal, G., & Würtz, R. P. (2009). Combining Feature- and Correspondence-Based Methods for Visual Object Recognition. Neural Computation, 21(7), 1952–1989. http://doi.org/10.1162/neco.2009
Krüger, M., von der Malsburg, C., & Würtz, R. P. (2008). Self-organized evaluation of dynamic hand gestures for sign language recognition. In R. P. Würtz (Ed.), Organic Computing (pp. 321–342). Springer.
Lessmann, M., & Würtz, R. P. (2008). Image Segmentation by a Network of Cortical Macrocolumns with Learned Connection Weights. In M. Hinchey, Pagnonia, A., Rammig, F. J., & Schmeck, H. (Eds.), Proceedings of Biologically-Inspired Collaborative Computing (BICC), Milano, Sep. 2008 (pp. 177–186). Springer.
Vollmar, T., Maus, B., Würtz, R. P., Gillessen-Kaesbach, G., Horsthemke, B., Wieczorek, D., & Böhringer, S. (2008). Impact of geometry and viewing angle on classification accuracy of 2D based analysis of dysmorphic faces. European Journal of Medical Genetics, 51, 44–53. http://doi.org/10.1016/j.ejmg.2007.10.002
Westphal, G., von der Malsburg, C., & Würtz, R. P. (2008). Feature-driven emergence of model graphs for object recognition and categorization. In A. Kandel, Bunke, H., & Last, M. (Eds.), Applied Pattern Recognition (pp. 155–199). Springer.
Würtz, R. P. (2008). Organic Computing for Image Understanding and Robotics.
Würtz_(editor),. (2008). Organic Computing. Springer. http://doi.org/10.1007/978-3-540-77657-4
Kusnezow, W., Horn, W., & Würtz, R. P. (2007). Fast image processing with constraints by solving linear PDEs. Electronic Letters on Computer Vision and Image Analysis, 6(2), 22–35.
Müller, M. K., Heinrichs, A., Tewes, A. H. J., Schäfer, A., & , R. P. W. (2007). Similarity rank correlation for face recognition under unenrolled pose. In S. -W. Lee & Li, S. Z. (Eds.), Advances in Biometrics (pp. 67–76). Springer.
Würtz, R. P. (2007). Organic Computing for Video Analysis. In A. König, Köppen, M., Abraham, A., Igel, C., & Kasabov, N. (Eds.), Seventh International Conference on Hybrid Intelligent Systems, Kaiserslautern, Germany (pp. 6–11). IEEE Computer Society Press.
Barwiṅski, M., & Würtz, R. P. (2006). A sequence-encoding neural network for face recognition. In M. Verleysen (Ed.), Proceedings of ESANN, Bruges, Belgium (pp. 635–640). d-side publications, Evere, Belgium.
Böhringer, S., Vollmar, T., Tasse, C., Würtz, R. P., Gillessen-Kaesbach, G., Horsthemke, B., & Wieczorek, D. (2006). Syndrome identification based on 2D analysis software. European Journal of Human Genetics, 14(10), 1082–1089. http://doi.org/10.1038/sj.ejhg.5201673
Heinrichs, A., Müller, M. K., Tewes, A. H. J., & , R. P. W. (2006). Graphs with Principal Components of Gabor Wavelet Features for Improved Face Recognition. In G. Cristóbal, Javidi, B., & Vallmitjana, S. (Eds.), Information Optics: 5th International Workshop on Information Optics; WIO′06 (pp. 243–252). American Institute of Physics.
Heyden, L., Würtz, R. P., & Peters, G. (2006). Supplementing Bundle Adjustment with Evolutionary Algorithms. In Proceedings of the IET International Conference on Visual Information Engineering 2006 (VIE 2006) (pp. 533–536). Institution of Engineering and Technology.
Schmidt, P. A., Maël, E., & Würtz, R. P. (2006). A sensor for dynamic tactile information with applications in human-robot interaction and object exploration. Robotics and Autonomous Systems, 54(12), 1005–1014. http://doi.org/10.1016/j.robot.2006.05.013
Westphal, G., von der Malsburg, C., & Würtz, R. P. (2006). Feature-driven emergence of model graphs for object recognition and categorization. In K. Bellman, Hofmann, P., Müller-Schloer, C., Schmeck, H., & Würtz, R. P. (Eds.), Organic Computing – Controlled Emergence. Internationales Begegnungs- und Forschungszentrum (IBFI), Schloss Dagstuhl, Germany. Retrieved from http://drops.dagstuhl.de/opus/volltexte/2006/575/pdf/06031.WuertzRolf.Paper.575.pdf
Brinkschulte, U., Becker, J., Fey, D., Hochberger, C., Martinetz, T., Müller-Schloer, C., et al. (2005). ARCS 2005 - System Aspects in Organic and Pervasive Computing - Workshops Procedeedings, Innsbruck Austria, March 14-17. VDE Verlag, Berlin, Offenbach.
Tewes, A., Würtz, R. P., & von der Malsburg, C. (2005). A flexible object model for recognising and synthesising facial expressions. In T. Kanade, Ratha, N., & Jain, A. (Eds.), Proceedings of the International Conference on Audio- and Video-based Biometric Person Authentication (pp. 81–90). Springer.
Würtz, R. P. (2005). Organic Computing methods for face recognition. it - Information Technology, 47(4), 207–211. http://doi.org/10.1524/itit.2005.47.4.207
Krüger, M., Schäfer, A., Tewes, A., & Würtz, R. P. (2004). Communicating agents architecture with applications in multimodal human computer interaction. In P. Dadam & Reichert, M. (Eds.), Informatik 2004 (Vol. 2, pp. 641–645). Gesellschaft für Informatik.
Messer, K., Kittler, J., Sadeghi, M., Hamouz, M., Kostin, A., Cardinaux, F., et al. (2004). Face Authentication Test on the BANCA Database. In Proceedings of ICPR 2004, Cambridge (Vol. 4, pp. 523–532).
Müller-Schloer, C., von der Malsburg, C., & Würtz, R. P. (2004). Aktuelles Schlagwort: Organic Computing. Informatik Spektrum, 27(4), 332–336. http://doi.org/DOI 10.1007/s00287-004-0409-6
Westphal, G., & Würtz, R. P. (2004). Fast object and pose recognition through minimum entropy coding. In 17th International Conference on Pattern Recognition (ICPR 2004), Cambridge (Vol. 3, pp. 53–56). IEEE Press. http://doi.org/10.1109/ICPR.2004.1334467
Wundrich, I. J., von der Malsburg, C., & Würtz, R. P. (2004). Image Representation by Complex Cell Responses. Neural Computation, 16(12), 2563–2575. http://doi.org/10.1162/0899766042321760
Würtz, R. P. (2004). Organic Computing for face and object recognition. In P. Dadam & Reichert, M. (Eds.), Informatik 2004 (Vol. 2, pp. 636–640). Gesellschaft für Informatik.
Loos, H. S., Wieczorek, D., Würtz, R. P., von der Malsburg, C., & Horsthemke, B. (2003). Computer-based recognition of dysmorphic faces. European Journal of Human Genetics, 11, 555–560. http://doi.org/10.1038/sj.ejhg.5200997
Lourens, T., & Würtz, R. P. (2003). Extraction and Matching of Symbolic Contour Graphs. International Journal of Pattern Recognition and Artificial Intelligence, 17(7), 1279–1302. http://doi.org/10.1142/S0218001403002848
Prodöhl, C., Würtz, R. P., & von der Malsburg, C. (2003). Learning the Gestalt Rule of Collinearity from Object Motion. Neural Computation, 15(8), 1865–1896. http://doi.org/10.1162/08997660360675071
Schmidt, P., Maël, E., & Würtz, R. P. (2003). European Patent Number: EP 1 142 118 B1 Tastsensor. Europäisches Patentamt, München.
Schmidt, P., Maël, E., & Würtz, R. P. (2003). US Patent Number: 6 593 756 Tactile sensor. United States Patent and Trademark Office.
Würtz, R. P. (2003). Conference Report: 4th Workshop on Dynamic Perception. KI - Künstliche Intelligenz, 17(2), 38.
Würtz, R. P., & Lappe_(eds.), M. (2002). Dynamic Perception. infix Verlag/IOS press, Berlin, Amsterdam.
Heinrichs, A., Eckes, C., Würtz, R. P., & von der Malsburg, C. (2002). Statistical Learning of the Detection of Faces in Natural Images. In R. P. Würtz & Lappe, M. (Eds.), Dynamic Perception (pp. 265–270). infix Verlag/IOS press.
Lücke, J., von der Malsburg, C., & Würtz, R. P. (2002). Macrocolumns as decision units. In J. R. Dorronsoro (Ed.), Artificial Neural Networks - ICANN 2002, Madrid (Vol. 2415, pp. 57–62). Springer.
Wieghardt, J., Würtz, R. P., & von der Malsburg, C. (2002). Gabor-based Feature Point Tracking with Automatically Learned Constraints. In R. P. Würtz & Lappe, M. (Eds.), Dynamic Perception (pp. 121–126). infix Verlag/IOS press.
Wieghardt, J., Würtz, R. P., & von der Malsburg, C. (2002). Learning the Topology of Object Views. In A. Heyden, Sparr, G., Nielsen, M., & Johansen, P. (Eds.), Computer Vision - ECCV 2002 (p. IV-747-IV-760). Springer.
Wundrich, I. J., von der Malsburg, C., & Würtz, R. P. (2002). Image Reconstruction from Gabor Magnitudes. In H. H. Bülthoff, Lee, S. -W., Poggio, T. A., & Wallraven, C. (Eds.), Biologically Motivated Computer Vision (pp. 117–126). Springer.
Würtz, R. P. (2002). Face Recognition, Neurophysiology, and Neural technology. In M. A. Arbib (Ed.), The Handbook of Brain Theory and Neural Networks (2.nd ed., pp. 434–437). MIT Press.
Würtz, R. P. (2002). Technik und Leistungsfähigkeit automatischer Gesichtserkennung. FIfF-Kommunikation, 19(1), 27–30.
Würtz, R. P. (2002). Vision and Touch for Grasping. In G. D. Hager, Christensen, H. I., Bunke, H., & Klein, R. (Eds.), Sensor Based Intelligent Robots (pp. 74–86). Springer.
König, P., Kayser, C., Bonin, V., & Würtz, R. P. (2001). Efficient evaluation of serial sections by iterative Gabor matching. Journal of Neuroscience Methods, 111(2), 141–150. http://doi.org/10.1016/S0165-0270(01)00439-3
Würtz, R. P. (2001). Gabor phase space molecules for image understanding. In I. Wundrich (Ed.), The Mathematical, Computational and Biological Study of Vision (p. 23).
Würtz, R. P. (2000). Gossiping Nets. Artificial Intelligence, 119(1-2), 295–299.
Würtz, R. P., & Lourens, T. (2000). Corner detection in color images through a multiscale combination of end-stopped cortical cells. Image and Vision Computing, 18(6-7), 531–541.
Becker, M., Kefalea, E., Maël, E., von der Malsburg, C., Pagel, M., Triesch, J., et al. (1999). GripSee: A Gesture-controlled Robot for Object Perception and Manipulation. Autonomous Robots, 6(2), 203–221. http://doi.org/10.1023/A:1008839628783
Becker, M., Kefalea, E., Maël, E., von der Malsburg, C., Pagel, M., Triesch, J., et al. (1999). GripSee: A Gesture-controlled Robot for Object Perception and Manipulation. Autonomous Robots, 6(2), 203–221. http://doi.org/10.1023/A:1008839628783
Kefalea, E., Maël, E., & Würtz, R. P. (1999). An Integrated Object Representation for Recognition and Grasping. In L. C. Jain (Ed.), Proceedings of the Third International Conference on Knowledge-based Intelligent Information Engineering Systems, Adelaide, Australia (pp. 423–426). IEEE Press.
Würtz, R. P. (1999). Neural networks as a model for visual perception: what is lacking? Cognitive Systems, 5(2), 103–112.
Würtz, R. P., Konen, W., & Behrmann, K. -O. (1999). On the performance of neuronal matching algorithms. Neural Networks, 12(1), 127–134. http://doi.org/10.1016/S0893-6080(98)00112-9
Lourens, T., & Würtz, R. P. (1998). Object Recognition by matching symbolic edge graphs. In R. Chin & Pong, T. -C. (Eds.), Computer Vision – ACCV′98 (Vol. 1352, p. II-193 - II-200). Springer Verlag. http://doi.org/10.1016/S0262-8856(99)00061-X
Würtz, R. P. (1998). Biologically Inspired Methods for Model Matching and Object Recognition. In Y. Sawada (Ed.), Proceedings of the 2nd R.I.E.C. International Symposium on Design and Architecture of Information Processing Systems Based on the Brain Information Principles, in Sendai, Japan, March 16-18, 1998 (pp. 101–106).
McKenna, S. J., Gong, S., Würtz, R. P., Tanner, J., & Banin, D. (1997). Tracking Facial Feature Points with Gabor Wavelets and Shape Models. In J. Bigün, Chollet, G., & Borgefors, G. (Eds.), Proceedings of the First International Conference on Audio- and Video-based Biometric Person Authentication (Vol. 1206, pp. 35–42). Springer.
Würtz, R. P. (1997). Context dependent feature groups, a proposal for object representation. Behavioral and Brain Sciences, 20(4), 702–703. http://doi.org/10.1017/S0140525X97441602
Würtz, R. P. (1997). Neuronal theories and technical systems for face recognition. In M. Verleysen (Ed.), Proceedings of the Fifth European Symposium On Artificial Neural Networks, Bruges (Belgium), 16-18 April 1997 (pp. 73–78). D facto, Brussels.
Würtz, R. P. (1997). Object recognition robust under translations, deformations and changes in background. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(7), 769–775. http://doi.org/10.1109/34.598234
Würtz, R. P., & Lourens, T. (1997). Corner detection in color images by multiscale combination of end-stopped cortical cells. In W. Gerstner, Germond, A., Hasler, M., & Nicoud, J. -D. (Eds.), Artificial Neural Networks - ICANN ′97 (Vol. 1327, pp. 901–906). Springer Verlag.
Würtz, R. P., Konen, W., & Behrmann, K. -O. (1996). How fast can neuronal algorithms match patterns? In C. von der Malsburg, Vorbrüggen, J. C., von Seelen, W., & Sendhoff, B. (Eds.), Artificial Neural Networks - ICANN 96 (Vol. 1112, pp. 145–150). Springer Verlag.
Würtz, R. P. (1995). Building visual correspondence maps – from neural dynamics to a face recognition system. In R. Moreno-Díaz & Mira-Mira, J. (Eds.), Brain Processes, Theories and Models (pp. 420–429). MIT Press.
Konen, W., Vorbrüggen, J. C., & Würtz, R. P. (1995). Patent Nummer 4406020: Verfahren zur automatisierten Erkennung von Objekten. Deutsches Patentamt, München.
Würtz, R. P. (1995). Multilayer Dynamic Link Networks for Establishing Image Point Correspondences and Visual Object Recognition (p. 155). Thun, Frankfurt am Main: Verlag Harri Deutsch.
Würtz, R. P., & von der Malsburg, C. (1994). Image Point Correspondences from a Wavelet Representation and a Hierarchical Dynamic Link Network. In T. Smithers & Moreno, A. (Eds.), The Role of Dynamics and Representation in Adaptive Behavior and Cognition (pp. 200–202).
Lades, M., Vorbrüggen, J. C., Buhmann, J., Lange, J., von der Malsburg, C., Würtz, R. P., & Konen, W. (1993). Distortion Invariant Object Recognition in the Dynamic Link Architecture. IEEE Transactions on Computers, 42(3), 300–311. http://doi.org/10.1109/12.210173
Würtz, R. P. (1992). Gesichtserkennung mit dynamischen neuronalen Netzen. Spektrum der Wissenschaft, 18–22.
Buhmann, J., Lange, J., von der Malsburg, C., Vorbrüggen, J. C., & Würtz, R. P. (1992). Object Recognition with Gabor Functions in the Dynamic Link Architecture – Parallel Implementation on a Transputer Network. In B. Kosko (Ed.), Neural Networks for Signal Processing (pp. 121–159). Prentice Hall, Englewood Cliffs, NJ.
Doursat, R., Konen, W., Lades, M., von der Malsburg, C., Vorbrüggen, J., Wiskott, L., & Würtz, R. (1992). Neural Mechanisms of Elastic Pattern Matching. In M. van der Meer (Ed.), Statusseminar des BMFT: Neuroinformatik (pp. 71–82). Projektträger Informationstechnik/DLR, Berlin.
Lades, M., Vorbrüggen, J. C., & Würtz, R. P. (1991). Recognizing Faces with a Transputer farm. In T. S. Durrani, Sandham, W. A., Soraghan, J. J., & Forbes, S. M. (Eds.), Applications of Transputers~3 (pp. 148–153). IOS Press; Amsterdam, Oxford, Washington, Tokio.
von der Malsburg, C., Würtz, R. P., & Vorbrüggen, J. C. (1991). Bilderkennung mit dynamischen Neuronennetzen. In W. Brauer & Hernández, D. (Eds.), Verteilte Künstliche Intelligenz und kooperatives Arbeiten (pp. 519–529). Springer.
Würtz, R. P., Vorbrüggen, J. C., von der Malsburg, C., & Lange, J. (1991). A Transputer-based Neural Object Recognition System. In H. Burkhardt, Neuvo, Y., & Simon, J. C. (Eds.), From Pixels to Features II - Parallelism in Image Processing (pp. 275–294). North Holland, Amsterdam.
Würtz, R. P., Vorbrüggen, J. C., & von der Malsburg, C. (1990). A Transputer System for the Recognition of Human Faces by Labeled Graph Matching. In R. Eckmiller, Hartmann, G., & Hauske, G. (Eds.), Parallel Processing in Neural Systems and Computers (pp. 37–41). North Holland, Amsterdam.
Tomforde, S., Rudolph, S., Bellman, K., & Würtz, R. P. (2016). "Self-Improving System Integration" – Preface for the SISSY′16 Workshop. In Proc. ICAC (p. 275). http://doi.org/10.1109/ICAC.2016.74
Tomforde, S., Rudolph, S., Bellman, K., & Würtz, R. P. (2016). An Organic Computing Perspective on Self-Improving System Interweaving at Runtime. In Proc. ICAC (pp. 276–284). http://doi.org/10.1109/ICAC.2016.15
Fahmy, G., Alqallaf, A., & Wurtz, R. (2015). Phase based detection of JPEG counter forensics. In 2015 IEEE International Conference on Electronics, Circuits, and Systems (ICECS) (pp. 37–40). http://doi.org/10.1109/ICECS.2015.7440243
Bodenstein, C., Tremer, M., Overhoff, J., & Würtz, R. P. (2015). A smartphone-controlled autonomous robot. In Proceedings of the 12th International Conference on Fuzzy Systems and Knowledge Discovery, Zhangjiajie, China, Aug. 15-17 (pp. 2360–2367).
Günther, M., Böhringer, S., Wieczorek, D., & Würtz, R. P. (2015). Reconstruction of Images from Gabor Graphs with Applications in Facial Image Processing. International Journal of Wavelets, Multiresolution and Information Processing, 13(4), 1550019-1–25. http://doi.org/10.1142/S0219691315500198
Kosilek, R. P., Frohner, R., Würtz, R. P., Berr, C. M., Schopohl, J., Reincke, M., & Schneider, H. J. (2015). Automatic face classification in Cushing′s syndrome and acromegaly: Review, current results and future perspectives. European Journal of Endocrinology, 173(4), M39–M44. http://doi.org/10.1530/EJE-15-0429
Balliu, B., Würtz, R. P., Horsthemke, B., Wieczorek, D., & Böhringer, S. (2014). Classification and visualization based on derived image features: application to genetic syndromes. PLOS One, 9(11), e109033. http://doi.org/10.1371/journal.pone.0109033
Bellman, K., Tomforde, S., & Würtz, R. P. (2014). "Self-Improving System Integration" – Preface for the SISSY14 Workshop. In Proc. SASO, London (p. 122). IEEE.
Bellman, K., Tomforde, S., & Würtz, R. P. (2014). Interwoven Systems: Self-improving Systems Integration. In Proc. SASO, London (pp. 123–127). IEEE.
Krause, T. U., Schrör, P. Y., & Würtz, R. P. (2014). Spiking network simulations. In B. Hammer, Martinetz, T., & Villmann, T. (Eds.), Proceedings of New Challenges in Neural Computation, Münster (pp. 14–15).
Lessmann, M., & Würtz, R. P. (2014). Learning of invariant object recognition in a hierarchical network. Neural Networks, 54, 70–84. http://doi.org/10.1016/j.neunet.2014.02.011
Lessmann, M., & Würtz, R. P. (2014). Online Learning of Invariant Object Recognition in a Hierarchical Neural Network. In S. Wermter, Weber, C., Duch, W., Honkela, T., Koprinkova-Hristova, P., Magg, S., et al. (Eds.), Proc. ICANN (pp. 427–434). Springer.
Frohner, R., Würtz, R. P., Kosilek, R. P., & Schneider, H. J. (2013). Optimierung der Gesichtsklassifikation bei der Erkennung von Akromegalie. Austrian Journal of Clinical Endocrinology and Metabolism, 6(3), 20–24.
Kosilek, R. P., Schopohl, J., Grünke, M., Dimopoulou, C., Stalla, G. K., Lammert, A., et al. (2013). Automatic face classification of Cushing’s syndrome in women - A novel screening approach. Experimental and Clinical Endocrinology and Diabetes. http://doi.org/10.1055/s-0033-1349124
Müller, M. K., Tremer, M., Bodenstein, C., & Würtz, R. P. (2013). Learning invariant face recognition from examples. Neural Networks, 41, 137–146. http://doi.org/10.1016/j.neunet.2012.07.006
Walther, T., & Würtz, R. P. (2012). Unsupervised Learning of Face Detection Models from Unlabeled Image Streams. In A. Brömme & Busch, C. (Eds.), Proceedings of the 11th International Conference of the Biometrics Special Interest Group (Vol. P-196, pp. 221–231). Bonn: Köllen.
Müller, M. K., Tremer, M., Bodenstein, C., & Würtz, R. P. (2012). Lernen situationsunabhängiger Personenerkennung. Informatikspektrum, 35(2), 112–118. http://doi.org/10.1007/s00287-012-0598-3
Böhringer, S., van der Lijn, F., Liu, F., Sinigerova, S., Birnbaum, S., Mangold, E., et al. (2011). Genetic determination of the human facial shape: links between cleft-lips and normal variation. European Journal of Human Genetics, 19, 1192–1197. http://doi.org/10.1038/ejhg.2011.110
Schneider, H. J., Kosilek, R. P., Günther, M., Schopohl, J., Römmler, J., Stalla, G. K., et al. (2011). A novel approach for the detection of acromegaly: accuracy of diagnosis by automatic face classification. Journal of Clinical Endocrinology and Metabolism, 96(7), 2074–2080. http://doi.org/10.1210/jc.2011-0237
Walther, T., & Würtz, R. P. (2011). Learning to Look at Humans. In C. Müller-Schloer, Schmeck, H., & Ungerer, T. (Eds.), Organic Computing - a Paradigm Shift for Complex Systems (pp. 309–322). Springer. http://doi.org/10.1007/978-3-0348-0130-0_20
Donatti, G. S., Lomp, O., & Würtz, R. P. (2010). Evolutionary Optimization of Growing Neural Gas Parameters for Object Categorization and Recognition. In Proc. IJCNN (pp. 1862–1869). IEEE Computer Society, Los Alamitos, CA.
Günther, M., Müller, M. K., & Würtz, R. P. (2010). Two kinds of statistics for better face recognition. In T. E. Simos, Psihoyios, G., & Tsitouras, C. (Eds.), Numerical Analysis and Applied Mathematics, International Conference (pp. 1901–1904). American Institute of Physics.
Walther, T., & Würtz, R. P. (2010). Learning Generic Human Body Models. In F. J. Perales & Fisher, R. B. (Eds.), Proc. Sixth Conference on Articulated Motion and Deformable Objects (pp. 98–107). Springer.
Würtz, R. P., Bellman, K. L., Schmeck, H., & Igel, C. (2010). Editorial: Special Issue on Organic Computing. ACM Transactions on Autonomous and Adaptive Systems, 5(3), 1–3. http://doi.org/10.1145/1837909.1837910
Donatti, G. S., & Würtz, R. P. (2009). Using Growing Neural Gas Networks to represent visual object knowledge. In Proceedings of the 21st IEEE International Conference on Tools with Artificial Intelligence (pp. 54–58). IEEE Computer Society, Newark, NJ.
Günther, M., & Würtz, R. P. (2009). Face Detection and Recognition Using Maximum Likelihood Classifiers on Gabor Graphs. International Journal of Pattern Recognition and Artificial Intelligence, 23(3), 433–461. http://doi.org/10.1142/S0218001409007211
Müller, M. K., & Würtz, R. P. (2009). Learning from Examples to Generalize over Pose and Illumination. In C. Alippi, Polycarpou, M., Panayiotou, C., & Ellinas, G. (Eds.), Artificial Neural Networks - ICANN 2009 (Vol. 5769, pp. 643–652). Springer.
Walther, T., & Würtz, R. P. (2009). Unsupervised learning of human body parts from video footage. In Proceedings of ICCV workshops, Kyoto (pp. 336–343). IEEE Computer Society, Los Alamitos, CA.
Westphal, G., & Würtz, R. P. (2009). Combining Feature- and Correspondence-Based Methods for Visual Object Recognition. Neural Computation, 21(7), 1952–1989. http://doi.org/10.1162/neco.2009.12-07-675
Westphal, G., & Würtz, R. P. (2009). Combining Feature- and Correspondence-Based Methods for Visual Object Recognition. Neural Computation, 21(7), 1952–1989. http://doi.org/10.1162/neco.2009
Krüger, M., von der Malsburg, C., & Würtz, R. P. (2008). Self-organized evaluation of dynamic hand gestures for sign language recognition. In R. P. Würtz (Ed.), Organic Computing (pp. 321–342). Springer.
Lessmann, M., & Würtz, R. P. (2008). Image Segmentation by a Network of Cortical Macrocolumns with Learned Connection Weights. In M. Hinchey, Pagnonia, A., Rammig, F. J., & Schmeck, H. (Eds.), Proceedings of Biologically-Inspired Collaborative Computing (BICC), Milano, Sep. 2008 (pp. 177–186). Springer.
Vollmar, T., Maus, B., Würtz, R. P., Gillessen-Kaesbach, G., Horsthemke, B., Wieczorek, D., & Böhringer, S. (2008). Impact of geometry and viewing angle on classification accuracy of 2D based analysis of dysmorphic faces. European Journal of Medical Genetics, 51, 44–53. http://doi.org/10.1016/j.ejmg.2007.10.002
Westphal, G., von der Malsburg, C., & Würtz, R. P. (2008). Feature-driven emergence of model graphs for object recognition and categorization. In A. Kandel, Bunke, H., & Last, M. (Eds.), Applied Pattern Recognition (pp. 155–199). Springer.
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Kusnezow, W., Horn, W., & Würtz, R. P. (2007). Fast image processing with constraints by solving linear PDEs. Electronic Letters on Computer Vision and Image Analysis, 6(2), 22–35.
Müller, M. K., Heinrichs, A., Tewes, A. H. J., Schäfer, A., & , R. P. W. (2007). Similarity rank correlation for face recognition under unenrolled pose. In S. -W. Lee & Li, S. Z. (Eds.), Advances in Biometrics (pp. 67–76). Springer.
Würtz, R. P. (2007). Organic Computing for Video Analysis. In A. König, Köppen, M., Abraham, A., Igel, C., & Kasabov, N. (Eds.), Seventh International Conference on Hybrid Intelligent Systems, Kaiserslautern, Germany (pp. 6–11). IEEE Computer Society Press.
Barwiṅski, M., & Würtz, R. P. (2006). A sequence-encoding neural network for face recognition. In M. Verleysen (Ed.), Proceedings of ESANN, Bruges, Belgium (pp. 635–640). d-side publications, Evere, Belgium.
Böhringer, S., Vollmar, T., Tasse, C., Würtz, R. P., Gillessen-Kaesbach, G., Horsthemke, B., & Wieczorek, D. (2006). Syndrome identification based on 2D analysis software. European Journal of Human Genetics, 14(10), 1082–1089. http://doi.org/10.1038/sj.ejhg.5201673
Heinrichs, A., Müller, M. K., Tewes, A. H. J., & , R. P. W. (2006). Graphs with Principal Components of Gabor Wavelet Features for Improved Face Recognition. In G. Cristóbal, Javidi, B., & Vallmitjana, S. (Eds.), Information Optics: 5th International Workshop on Information Optics; WIO′06 (pp. 243–252). American Institute of Physics.
Heyden, L., Würtz, R. P., & Peters, G. (2006). Supplementing Bundle Adjustment with Evolutionary Algorithms. In Proceedings of the IET International Conference on Visual Information Engineering 2006 (VIE 2006) (pp. 533–536). Institution of Engineering and Technology.
Schmidt, P. A., Maël, E., & Würtz, R. P. (2006). A sensor for dynamic tactile information with applications in human-robot interaction and object exploration. Robotics and Autonomous Systems, 54(12), 1005–1014. http://doi.org/10.1016/j.robot.2006.05.013
Westphal, G., von der Malsburg, C., & Würtz, R. P. (2006). Feature-driven emergence of model graphs for object recognition and categorization. In K. Bellman, Hofmann, P., Müller-Schloer, C., Schmeck, H., & Würtz, R. P. (Eds.), Organic Computing – Controlled Emergence. Internationales Begegnungs- und Forschungszentrum (IBFI), Schloss Dagstuhl, Germany. Retrieved from http://drops.dagstuhl.de/opus/volltexte/2006/575/pdf/06031.WuertzRolf.Paper.575.pdf
Brinkschulte, U., Becker, J., Fey, D., Hochberger, C., Martinetz, T., Müller-Schloer, C., et al. (2005). ARCS 2005 - System Aspects in Organic and Pervasive Computing - Workshops Procedeedings, Innsbruck Austria, March 14-17. VDE Verlag, Berlin, Offenbach.
Tewes, A., Würtz, R. P., & von der Malsburg, C. (2005). A flexible object model for recognising and synthesising facial expressions. In T. Kanade, Ratha, N., & Jain, A. (Eds.), Proceedings of the International Conference on Audio- and Video-based Biometric Person Authentication (pp. 81–90). Springer.
Würtz, R. P. (2005). Organic Computing methods for face recognition. it - Information Technology, 47(4), 207–211. http://doi.org/10.1524/itit.2005.47.4.207
Krüger, M., Schäfer, A., Tewes, A., & Würtz, R. P. (2004). Communicating agents architecture with applications in multimodal human computer interaction. In P. Dadam & Reichert, M. (Eds.), Informatik 2004 (Vol. 2, pp. 641–645). Gesellschaft für Informatik.
Messer, K., Kittler, J., Sadeghi, M., Hamouz, M., Kostin, A., Cardinaux, F., et al. (2004). Face Authentication Test on the BANCA Database. In Proceedings of ICPR 2004, Cambridge (Vol. 4, pp. 523–532).
Müller-Schloer, C., von der Malsburg, C., & Würtz, R. P. (2004). Aktuelles Schlagwort: Organic Computing. Informatik Spektrum, 27(4), 332–336. http://doi.org/DOI 10.1007/s00287-004-0409-6
Westphal, G., & Würtz, R. P. (2004). Fast object and pose recognition through minimum entropy coding. In 17th International Conference on Pattern Recognition (ICPR 2004), Cambridge (Vol. 3, pp. 53–56). IEEE Press. http://doi.org/10.1109/ICPR.2004.1334467
Wundrich, I. J., von der Malsburg, C., & Würtz, R. P. (2004). Image Representation by Complex Cell Responses. Neural Computation, 16(12), 2563–2575. http://doi.org/10.1162/0899766042321760
Würtz, R. P. (2004). Organic Computing for face and object recognition. In P. Dadam & Reichert, M. (Eds.), Informatik 2004 (Vol. 2, pp. 636–640). Gesellschaft für Informatik.
Loos, H. S., Wieczorek, D., Würtz, R. P., von der Malsburg, C., & Horsthemke, B. (2003). Computer-based recognition of dysmorphic faces. European Journal of Human Genetics, 11, 555–560. http://doi.org/10.1038/sj.ejhg.5200997
Lourens, T., & Würtz, R. P. (2003). Extraction and Matching of Symbolic Contour Graphs. International Journal of Pattern Recognition and Artificial Intelligence, 17(7), 1279–1302. http://doi.org/10.1142/S0218001403002848
Prodöhl, C., Würtz, R. P., & von der Malsburg, C. (2003). Learning the Gestalt Rule of Collinearity from Object Motion. Neural Computation, 15(8), 1865–1896. http://doi.org/10.1162/08997660360675071
Schmidt, P., Maël, E., & Würtz, R. P. (2003). European Patent Number: EP 1 142 118 B1 Tastsensor. Europäisches Patentamt, München.
Schmidt, P., Maël, E., & Würtz, R. P. (2003). US Patent Number: 6 593 756 Tactile sensor. United States Patent and Trademark Office.
Würtz, R. P. (2003). Conference Report: 4th Workshop on Dynamic Perception. KI - Künstliche Intelligenz, 17(2), 38.
Würtz, R. P., & Lappe_(eds.), M. (2002). Dynamic Perception. infix Verlag/IOS press, Berlin, Amsterdam.
Heinrichs, A., Eckes, C., Würtz, R. P., & von der Malsburg, C. (2002). Statistical Learning of the Detection of Faces in Natural Images. In R. P. Würtz & Lappe, M. (Eds.), Dynamic Perception (pp. 265–270). infix Verlag/IOS press.
Lücke, J., von der Malsburg, C., & Würtz, R. P. (2002). Macrocolumns as decision units. In J. R. Dorronsoro (Ed.), Artificial Neural Networks - ICANN 2002, Madrid (Vol. 2415, pp. 57–62). Springer.
Wieghardt, J., Würtz, R. P., & von der Malsburg, C. (2002). Gabor-based Feature Point Tracking with Automatically Learned Constraints. In R. P. Würtz & Lappe, M. (Eds.), Dynamic Perception (pp. 121–126). infix Verlag/IOS press.
Wieghardt, J., Würtz, R. P., & von der Malsburg, C. (2002). Learning the Topology of Object Views. In A. Heyden, Sparr, G., Nielsen, M., & Johansen, P. (Eds.), Computer Vision - ECCV 2002 (p. IV-747-IV-760). Springer.
Wundrich, I. J., von der Malsburg, C., & Würtz, R. P. (2002). Image Reconstruction from Gabor Magnitudes. In H. H. Bülthoff, Lee, S. -W., Poggio, T. A., & Wallraven, C. (Eds.), Biologically Motivated Computer Vision (pp. 117–126). Springer.
Würtz, R. P. (2002). Face Recognition, Neurophysiology, and Neural technology. In M. A. Arbib (Ed.), The Handbook of Brain Theory and Neural Networks (2.nd ed., pp. 434–437). MIT Press.
Würtz, R. P. (2002). Technik und Leistungsfähigkeit automatischer Gesichtserkennung. FIfF-Kommunikation, 19(1), 27–30.
Würtz, R. P. (2002). Vision and Touch for Grasping. In G. D. Hager, Christensen, H. I., Bunke, H., & Klein, R. (Eds.), Sensor Based Intelligent Robots (pp. 74–86). Springer.
König, P., Kayser, C., Bonin, V., & Würtz, R. P. (2001). Efficient evaluation of serial sections by iterative Gabor matching. Journal of Neuroscience Methods, 111(2), 141–150. http://doi.org/10.1016/S0165-0270(01)00439-3
Würtz, R. P. (2001). Gabor phase space molecules for image understanding. In I. Wundrich (Ed.), The Mathematical, Computational and Biological Study of Vision (p. 23).
Würtz, R. P. (2000). Gossiping Nets. Artificial Intelligence, 119(1-2), 295–299.
Würtz, R. P., & Lourens, T. (2000). Corner detection in color images through a multiscale combination of end-stopped cortical cells. Image and Vision Computing, 18(6-7), 531–541.
Becker, M., Kefalea, E., Maël, E., von der Malsburg, C., Pagel, M., Triesch, J., et al. (1999). GripSee: A Gesture-controlled Robot for Object Perception and Manipulation. Autonomous Robots, 6(2), 203–221. http://doi.org/10.1023/A:1008839628783
Becker, M., Kefalea, E., Maël, E., von der Malsburg, C., Pagel, M., Triesch, J., et al. (1999). GripSee: A Gesture-controlled Robot for Object Perception and Manipulation. Autonomous Robots, 6(2), 203–221. http://doi.org/10.1023/A:1008839628783
Kefalea, E., Maël, E., & Würtz, R. P. (1999). An Integrated Object Representation for Recognition and Grasping. In L. C. Jain (Ed.), Proceedings of the Third International Conference on Knowledge-based Intelligent Information Engineering Systems, Adelaide, Australia (pp. 423–426). IEEE Press.
Würtz, R. P. (1999). Neural networks as a model for visual perception: what is lacking? Cognitive Systems, 5(2), 103–112.
Würtz, R. P., Konen, W., & Behrmann, K. -O. (1999). On the performance of neuronal matching algorithms. Neural Networks, 12(1), 127–134. http://doi.org/10.1016/S0893-6080(98)00112-9
Lourens, T., & Würtz, R. P. (1998). Object Recognition by matching symbolic edge graphs. In R. Chin & Pong, T. -C. (Eds.), Computer Vision – ACCV′98 (Vol. 1352, p. II-193 - II-200). Springer Verlag. http://doi.org/10.1016/S0262-8856(99)00061-X
Würtz, R. P. (1998). Biologically Inspired Methods for Model Matching and Object Recognition. In Y. Sawada (Ed.), Proceedings of the 2nd R.I.E.C. International Symposium on Design and Architecture of Information Processing Systems Based on the Brain Information Principles, in Sendai, Japan, March 16-18, 1998 (pp. 101–106).
McKenna, S. J., Gong, S., Würtz, R. P., Tanner, J., & Banin, D. (1997). Tracking Facial Feature Points with Gabor Wavelets and Shape Models. In J. Bigün, Chollet, G., & Borgefors, G. (Eds.), Proceedings of the First International Conference on Audio- and Video-based Biometric Person Authentication (Vol. 1206, pp. 35–42). Springer.
Würtz, R. P. (1997). Context dependent feature groups, a proposal for object representation. Behavioral and Brain Sciences, 20(4), 702–703. http://doi.org/10.1017/S0140525X97441602
Würtz, R. P. (1997). Neuronal theories and technical systems for face recognition. In M. Verleysen (Ed.), Proceedings of the Fifth European Symposium On Artificial Neural Networks, Bruges (Belgium), 16-18 April 1997 (pp. 73–78). D facto, Brussels.
Würtz, R. P. (1997). Object recognition robust under translations, deformations and changes in background. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(7), 769–775. http://doi.org/10.1109/34.598234
Würtz, R. P., & Lourens, T. (1997). Corner detection in color images by multiscale combination of end-stopped cortical cells. In W. Gerstner, Germond, A., Hasler, M., & Nicoud, J. -D. (Eds.), Artificial Neural Networks - ICANN ′97 (Vol. 1327, pp. 901–906). Springer Verlag.
Würtz, R. P., Konen, W., & Behrmann, K. -O. (1996). How fast can neuronal algorithms match patterns? In C. von der Malsburg, Vorbrüggen, J. C., von Seelen, W., & Sendhoff, B. (Eds.), Artificial Neural Networks - ICANN 96 (Vol. 1112, pp. 145–150). Springer Verlag.
Würtz, R. P. (1995). Building visual correspondence maps – from neural dynamics to a face recognition system. In R. Moreno-Díaz & Mira-Mira, J. (Eds.), Brain Processes, Theories and Models (pp. 420–429). MIT Press.
Konen, W., Vorbrüggen, J. C., & Würtz, R. P. (1995). Patent Nummer 4406020: Verfahren zur automatisierten Erkennung von Objekten. Deutsches Patentamt, München.
Würtz, R. P. (1995). Multilayer Dynamic Link Networks for Establishing Image Point Correspondences and Visual Object Recognition (p. 155). Thun, Frankfurt am Main: Verlag Harri Deutsch.
Würtz, R. P., & von der Malsburg, C. (1994). Image Point Correspondences from a Wavelet Representation and a Hierarchical Dynamic Link Network. In T. Smithers & Moreno, A. (Eds.), The Role of Dynamics and Representation in Adaptive Behavior and Cognition (pp. 200–202).
Lades, M., Vorbrüggen, J. C., Buhmann, J., Lange, J., von der Malsburg, C., Würtz, R. P., & Konen, W. (1993). Distortion Invariant Object Recognition in the Dynamic Link Architecture. IEEE Transactions on Computers, 42(3), 300–311. http://doi.org/10.1109/12.210173
Würtz, R. P. (1992). Gesichtserkennung mit dynamischen neuronalen Netzen. Spektrum der Wissenschaft, 18–22.
Buhmann, J., Lange, J., von der Malsburg, C., Vorbrüggen, J. C., & Würtz, R. P. (1992). Object Recognition with Gabor Functions in the Dynamic Link Architecture – Parallel Implementation on a Transputer Network. In B. Kosko (Ed.), Neural Networks for Signal Processing (pp. 121–159). Prentice Hall, Englewood Cliffs, NJ.
Doursat, R., Konen, W., Lades, M., von der Malsburg, C., Vorbrüggen, J., Wiskott, L., & Würtz, R. (1992). Neural Mechanisms of Elastic Pattern Matching. In M. van der Meer (Ed.), Statusseminar des BMFT: Neuroinformatik (pp. 71–82). Projektträger Informationstechnik/DLR, Berlin.
Lades, M., Vorbrüggen, J. C., & Würtz, R. P. (1991). Recognizing Faces with a Transputer farm. In T. S. Durrani, Sandham, W. A., Soraghan, J. J., & Forbes, S. M. (Eds.), Applications of Transputers~3 (pp. 148–153). IOS Press; Amsterdam, Oxford, Washington, Tokio.
von der Malsburg, C., Würtz, R. P., & Vorbrüggen, J. C. (1991). Bilderkennung mit dynamischen Neuronennetzen. In W. Brauer & Hernández, D. (Eds.), Verteilte Künstliche Intelligenz und kooperatives Arbeiten (pp. 519–529). Springer.
Würtz, R. P., Vorbrüggen, J. C., von der Malsburg, C., & Lange, J. (1991). A Transputer-based Neural Object Recognition System. In H. Burkhardt, Neuvo, Y., & Simon, J. C. (Eds.), From Pixels to Features II - Parallelism in Image Processing (pp. 275–294). North Holland, Amsterdam.
Würtz, R. P., Vorbrüggen, J. C., & von der Malsburg, C. (1990). A Transputer System for the Recognition of Human Faces by Labeled Graph Matching. In R. Eckmiller, Hartmann, G., & Hauske, G. (Eds.), Parallel Processing in Neural Systems and Computers (pp. 37–41). North Holland, Amsterdam.