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On-Line, Incremental Learning of a Robust Active Shape Model

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Part of the book series:Lecture Notes in Computer Science ((LNIP,volume 4174))

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Abstract

Active Shape Models are commonly used to recognize and locate different aspects of known rigid objects. However, they require an off-line learning stage, such that the extension of an existing model requires a complete new re-training phase. Furthermore, learning is based on principal component analysis and requires perfect training data that is not corrupted by partial occlusions or imperfect segmentation. The contribution of this paper is twofold: First, we present a novel robust Active Shape Model that can handle corrupted shape data. Second, this model can be created on-line through the use of a robust incremental PCA algorithm. Thus, an already partially learned Active Shape Model can be used for segmentation of a new image in a level set framework and the result of this segmentation process can be used for an on-line update of the robust model. Our experimental results demonstrate the robustness and the flexibility of this new model, which is at the same time computationally much more efficient than previous ASMs using batch or iterated batch PCA.

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Author information

Authors and Affiliations

  1. Institute of Electrical Measurement and Measurement Signal Processing, Graz University of Technology, Kopernikusgasse 24/IV, 8010, Graz, Austria

    Michael Fussenegger & Axel Pinz

  2. Institute for Computer Graphics and Vision, Graz University of Technology, Inffeldgasse 16/II, 8010, Graz, Austria

    Peter M. Roth & Horst Bischof

Authors
  1. Michael Fussenegger

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  2. Peter M. Roth

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  3. Horst Bischof

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  4. Axel Pinz

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Editor information

Editors and Affiliations

  1. Norwegian Information Security Laboratory, Gjøvik University College, Norway

    Katrin Franke

  2. Fraunhofer FIRST (IDA), Berlin, Germany

    Klaus-Robert Müller

  3. Department of Security Technology, Fraunhofer Institute for Production Systems and Design Technology (IPK), Pascalstr. 8-9, 10587, Berlin, Germany

    Bertram Nickolay

  4. Department of Electronic Imaging Technology, Fraunhofer Institute for Information and Communication Technology, Heinrich Hertz Institute (HHI), Einsteinufer 37, 10587, Berlin, Germany

    Ralf Schäfer

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© 2006 Springer-Verlag Berlin Heidelberg

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Fussenegger, M., Roth, P.M., Bischof, H., Pinz, A. (2006). On-Line, Incremental Learning of a Robust Active Shape Model. In: Franke, K., Müller, KR., Nickolay, B., Schäfer, R. (eds) Pattern Recognition. DAGM 2006. Lecture Notes in Computer Science, vol 4174. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11861898_13

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