A Robust PCA Algorithm for Building Representations from Panoramic Images

  • Danijel Skočaj
  • Horst Bischof
  • Aleš Leonardis
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2353)


Appearance-based modeling of objects and scenes using PCA has been successfully applied in many recognition tasks. Robust methods which have made the recognition stage less susceptible to outliers, occlusions, and varying illumination have further enlarged the domain of applicability. However, much less research has been done in achieving robustness in the learning stage. In this paper, we propose a novel robust PCA method for obtaining a consistent subspace representation in the presence of outlying pixels in the training images. The method is based on the EM algorithm for estimation of principal subspaces in the presence of missing data. By treating the outlying points as missing pixels, we arrive at a robust PCA representation. We demonstrate experimentally that the proposed method is efficient. In addition, we apply the method to a set of panoramic images to build a representation that enables surveillance and view-based mobile robot localization.


Principal Axis Principle Component Analysis Training Image Reconstruction Error Expectation Maximization Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Danijel Skočaj
    • 1
  • Horst Bischof
    • 2
  • Aleš Leonardis
    • 1
  1. 1.Faculty of Computer and Information ScienceUniversity of LjubljanaSlovenia
  2. 2.Inst. for Computer Graphics and VisionGraz University of TechnologyAustria

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