My general interests are pattern recognition, machine learning and computer vision. My current work at Siemens Corporate Research is concerned with image analysis in safety, security and healthcare domains. In my Ph.D. work, I have been researching within machine learning and pattern recognition in the on-line handwriting recognition system frog on hand.
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Wei Li, Gianluca Paladini, Leo Grady, Timo Kohlberger, Vivek Kumar Singh, and Claus Bahlmann. Luggage Visualization and Virtual Unpacking. Workshop at SIGGRAPH Asia, Singapore, 2012.
Leo Grady, Timo Kohlberger, Vivek Singh, Chris Alvino, and Claus Bahlmann. Automatic Segmentation of Unknown Objects, with Application to Baggage Security. European Conference on Computer Vision (ECCV), Florence, Italy, 2012.
Timo Kohlberger, Vivek Singh, Chris Alvino, Claus Bahlmann, and Leo Grady. Evaluating Segmentation Error Without Ground Truth. Medical Image Computing and Computer Aided Intervention (MICCAI), Nice, France, 2012.
Touﬁq Parag, Claus Bahlmann, Vinay Shet, and Maneesh Singh. A Grammar for Hierarchical Object Descriptions in Logic Programs. CVPR Workshop on Perceptual Organization in Computer Vision (CVPRW POCV), Providence, RI, 2012.
Jie Ni, Maneesh Singh and Claus Bahlmann. Fast Radial Symmetry Detection Under Affine Transformations. Computer Vision and Pattern Recognition (CVPR), Providence, RI, 2012.
Claus Bahlmann, Amar Patel, Jeffrey Johnson, Jie Ni, Andrei Chekkoury, Parmeshwar Khurd, Ali Kamen, Leo Grady, Elizabeth Krupinski, Anna Graham, and Ronald Weinstein. Automated Detection of Diagnostically Relevant Regions in H﹠E Stained Digital Pathology Slides. SPIE Medical Imaging (SPIEMI 2012), San Diego, CA, February 2012.
Andrei Chekkoury, Parmeshwar Khurd, Jie Ni, Claus Bahlmann, Ali Kamen, Amar Patel, Leo Grady, Elizabeth Krupinski, Jeffrey Johnson, Anna Graham, and Ronald Weinstein. Automated Malignancy Detection in Breast Histopathological Images. SPIE Medical Imaging (SPIEMI 2012), San Diego, CA, February 2012.
Vinay D. Shet, Maneesh Singh, Claus Bahlmann, Visvanathan Ramesh, Jan Neumann and Larry Davis. Predicate Logic based Image Grammars for Complex Pattern Recognition. International Journal of Computer Vision (IJCV), Special Issue on Stochastic Image Grammars, 2011.
Parmeshwar Khurd, Claus Bahlmann, Peter Maday, Ali Kamen, Summer Gibbs-Strauss, Beth Genega, John Frangioni. Computer-aided Gleason grading of prostate cancer histopathological images using texton forests. IEEE International Symposium on Biomedical Imaging (ISBI 2010).
Paul Sajda, Lucas C. Parra, Christoforos Christoforou, Barbara Hanna, Claus Bahlmann, Maneesh Singh, Jun Wang, Eric Pohlmeyer, Jacek Dmochowski, and Shih-Fu Chang. In a blink of an eye and a switch of a transistor: Cortically-coupled computer vision. In vol. 98, no. 3, pp. 462-478, 2010 Proc. of the IEEE, vol. 98, no. 3, pp. 462-478, 2010.
Alexander Zouhar, Sajjad Baloch, Sergei Azernikov, Claus Bahlmann, Gozde Unal, Tong Fang, Siegfried Fuchs. Freeform Shape Clustering for Customized Design Automation. The 2009 IEEE International Workshop on 3-D Digital Imaging and Modeling (In conjunction with ICCV 2009; 3DIM 09), Kyoto, Japan, Oct 2009.
Vinay Shet, Maneesh Singh, Claus Bahlmann, and Visvanathan Ramesh. Predicate Logic based Image Grammars for Complex Pattern Recognition. In First International Workshop on Stochastic Image Grammars (In conjunction with CVPR 2009; SIG-09), Miami, FL, June 2009.
Claus Bahlmann, Martin Pellkofer, Jan Giebel, and Gregory Baratoff. Multi-Modal Speed Limit Assistants: Combining Camera and GPS Maps. In IEEE Intelligent Vehicles Symposium (IV 2008), Eindhoven, The Netherlands, June 2008.
Christoph G. Keller, Christoph Sprunk, Claus Bahlmann, Jan Giebel, and Gregory Baratoff. Real-Time Recognition of U.S. Speed Signs. In IEEE Intelligent Vehicles Symposium (IV 2008), Eindhoven, The Netherlands, June 2008, Award winner "Best Student Paper".
Claus Bahlmann, Ying Zhu, Visvanathan Ramesh, Martin Pellkofer, and Thorsten Koehler. A System for Traffic Sign Detection, Tracking, and Recognition Using Color, Shape, and Motion Information. In IEEE Intelligent Vehicles Symposium (IV 2005), Las Vegas, NV, June 2005.
Claus Bahlmann. Advanced Sequence Classification Techniques Applied to Online Handwriting Recognition. Ph. D. thesis, Faculty of Applied Sciences, University of Freiburg, Shaker-Verlag, ISBN 3-8322-4535-9, 2005, Honored "Mit Auszeichnung" (with highest honors, summa cum laude) and the "Wolfgang-Gentner-Nachwuchsförderpreis".
Bernard Haasdonk and Claus Bahlmann. Learning with Distance Substitution Kernels. In 26th Pattern Recognition Symposium of the German Association for Pattern Recognition (DAGM 2004), Tübingen, Germany, August 2004.
Claus Bahlmann and Hans Burkhardt. The Writer Independent Online Handwriting Recognition System frog on hand and Cluster Generative Statistical Dynamic Time Warping. In IEEE Trans. Pattern Anal. and Mach. Intell. (TPAMI), volume 26, number 3, pages 299--310, March 2004.
Claus Bahlmann, Bernard Haasdonk and Hans Burkhardt. On-line Handwriting Recognition using Support Vector Machines - A kernel approach. In Int. Workshop on Frontiers in Handwriting Recognition (IWFHR) 2002, Niagara-on-the-Lake, Canada, August 2002, Award winner "Best Paper Presentation".
Claus Bahlmann and Hans Burkhardt. Measuring HMM Similarity with the Bayes Probability of Error and its Application to Online Handwriting Recognition. In Int. Conf. on Document Anal. and Recognition (ICDAR) 2001, Seattle, WA, September 2001.
Claus Bahlmann, Ying Zhu, Dorin Comaniciu, Thorsten Koehler, and Martin Pellkofer. Method for combining boosted classifiers for efficient multi-class object detection. Issued Aug 3rd 2010.
Claus Bahlmann, Xianlin Li, and Kazunori Okada. System and method for local pulmonary structure classification for computer-aided nodule detection. Issued Jul 27th 2010.
Claus Bahlmann, Ying Zhu, Visvanathan Ramesh, Martin Pellkofer, and Thorsten Koehler. Method for traffic sign detection. Issued Dec 16th 2008.
Dirk Bockhorn. Bestimmung und Untersuchung von Signifikanzgewichtungen für die Erkennung von handgeschriebenen Buchstaben. M.S. thesis, Computer Science Department, Albert Ludwigs University Freiburg, 2000 [in German].
Rudolph Triebel. Automatische Erkennung von handgeschriebenen Worten mithilfe des Level-building Algorithmus. B.S. thesis, Computer Science Department, Albert Ludwigs University Freiburg, 2000 [in German].
Awarded the "Wolfgang-Gentner-Nachwuchsförderpreis" 2005 for the Ph. D. dissertation
Ph. D. work entitled Advanced Sequence Classification Techniques Applied to Online Handwriting Recognition honored "mit Auszeichnung" (equivalent to "with highest honors", "summa cum laude") in 2005.
"Best Paper Presentation" at Int. Workshop on Frontiers in Handwriting Recognition (IWFHR) 2002, Niagara-on-the-Lake, Canada, August 2002, for the contribution On-line Handwriting Recognition using Support Vector Machines - A kernel approach.
Co-Author "Best Student Paper" at IEEE Intelligent Vehicles Symposium (IV 2008), Eindhoven, The Netherlands, June 2008, for the contribution Real-Time Recognition of U.S. Speed Signs.
"frog on hand" is the the freiburg recognition of on-line handwriting. Our goal is the design of a writer independent on-line handwriting recognition system.
In this work, a system including pre-processing, feature selection and two complementary classification approaches has been developed. Best results on the international UNIPEN benchmark have been achieved.
A special focus in this work is the classification. The classification approaches are based on two complementary methods: the so-called CSDTW (cluster generative statistical dynamic time warping) technique and support vector machines (SVMs).
A real-world application of our research is an implementation of recognition system on a Linux Compaq iPAQ.
Artificial Neural Networks for Automated Quality Control of Textile Seams - M.S. thesis
In this project I was working on a system for an automated, vision based quality control for textile seams. The developed system consists of
The performance of the system has been evaluated by the classification of seam specimens, which are used in industrial textile manufacturing for the setting of the sewing machine parameters. The results have shown that even with few, but well-fashioned features good classification results can be obtained. The classification rate amounts to 80% correct classifications, the rest differs from the correct grade only by one (on a scale of five). We have shown that this result is not worse than the error of human experts, which can be measured by the disagreement among a set of different expert judgments.
Time needed for classification is about one second on a 130 MHz PC, which is much less than textile experts need for classification (30 seconds).