Startseite
Martin Haker
erstellt von Thomas Klähn
—
zuletzt verändert:
11.03.2010 17:00
Publications
Martin Haker
| [1] | Martin Böhme, Martin Haker, Thomas Martinetz, and Erhardt Barth. Shading constraint improves accuracy of time-of-flight measurements. Computer Vision and Image Understanding, 114:1329-1335, 2010. [ bib | .pdf ] |
| [2] | Martin Böhme, Martin Haker, Thomas Martinetz, and Erhardt Barth. Head tracking with combined face and nose detection. In Proceedings of the IEEE International Symposium on Signals, Circuits & Systems (ISSCS), Iasi, Romania, 2009. (to appear). [ bib | .pdf ] |
| [3] | Martin Böhme, Martin Haker, Kolja Riemer, Thomas Martinetz, and Erhardt Barth. Face detection using a time-of-flight camera. In Dynamic 3D Imaging - Workshop in Conjunction with DAGM, volume 5742 of Lecture Notes in Computer Science, pages 167-176, 2009. http://www.springerlink.com/content/023881623j67r336/. [ bib | .pdf ] |
| [4] | Martin Haker, Martin Böhme, Thomas Martinetz, and Erhardt Barth. Deictic gestures with a time-of-flight camera. In Stefan Kopp and Ipke Wachsmuth, editors, Gesture in Embodied Communication and Human-Computer Interaction - International Gesture Workshop GW 2009, volume 5934 of LNAI, pages 110-121. Springer, 2009. http://www.springerlink.com/content/uw12051512tk5261/. [ bib | .pdf ] |
| [5] | Martin Haker, Martin Böhme, Thomas Martinetz, and Erhardt Barth. Self-organizing maps for pose estimation with a time-of-flight camera. In Dynamic 3D Imaging - Workshop in Conjunction with DAGM, volume 5742 of Lecture Notes in Computer Science, pages 142-153, 2009. http://www.springerlink.com/content/006305183070t383/. [ bib | .pdf ] |
| [6] | Martin Haker, Thomas Martinetz, and Erhardt Barth. Multimodal sparse features for object detection. In Artificial Neural Networks - ICANN 2009, 19th International Conference, Limassol, Cyprus, September 14-17, 2009, Proceedings, volume 5769 of Lecture Notes in Computer Science, pages 923-932. Springer, 2009. http://www.springerlink.com/content/574230078m0228wh/. [ bib | .pdf ] |
| [7] | Martin Böhme, Martin Haker, Thomas Martinetz, and Erhardt Barth. A facial feature tracker for human-computer interaction based on 3D Time-of-Flight cameras. International Journal of Intelligent Systems Technologies and Applications, 5(3/4):264-273, 2008. [ bib | .pdf ] |
| [8] | Martin Böhme, Martin Haker, Thomas Martinetz, and Erhardt Barth. Shading constraint improves accuracy of time-of-flight measurements. In CVPR 2008 Workshop on Time-of-Flight-based Computer Vision (TOF-CV), 2008. [ bib | .pdf ] |
| [9] | Christoph S. Garbe, Kai Krajsek, Pavel Pavlov, Björn Andres, Matthias Mühlich, Ingo Stuke, Cicero Mota, Martin Böhme, Martin Haker, Tobias Schuchert, Hanno Scharr, Til Aach, Erhardt Barth, Rudolf Mester, and Bernd Jähne. Nonlinear analysis of multi-dimensional signals: local adaptive estimation of complex motion and orientation patterns. In Rainer Dahlhaus, Jürgen Kurths, Peter Maaß, and Jens Timmer, editors, Mathematical Methods in Time Series Analysis and Digital Image Processing, Understanding Complex Systems, pages 231-288. Springer, 2008. [ bib ] |
| [10] | Martin Haker, Martin Böhme, Thomas Martinetz, and Erhardt Barth. Scale-invariant range features for time-of-flight camera applications. In CVPR 2008 Workshop on Time-of-Flight-based Computer Vision (TOF-CV), 2008. [ bib | .pdf ] |
| [11] | Martin Haker, Martin Böhme, Thomas Martinetz, and Erhardt Barth. Geometric invariants for facial feature tracking with 3D TOF cameras. In Proceedings of the IEEE International Symposium on Signals, Circuits & Systems (ISSCS), volume 1, pages 109-112, Iasi, Romania, 2007. [ bib | .pdf ] |
| [12] | Martin Haker. Locally Linear Manifold Classifier. Diploma thesis, Universität zu Lübeck, 2006. [ bib ] |
| [13] | M. Haker, C. Mota, and E. Barth. Multiple Motions in the Projective Plane. In 7th Tübingen Perception Conference, page 177, Kirchentellinsfurt, 2004. Knirsch Verlag. [ bib | .pdf ] |
| [14] | M. Haker, A. Meyer, D. Polani, and T. Martinetz. A Method for Incorporation of New Evidence to Improve World State Estimation. In Proceedings of the RoboCup 2001 Symposium, Seattle, pages 356-361, 2001. [ bib | .pdf ] |
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