Danijel Skočaj and Horst Bischof and Aleš Leonardis (2002) A Robust PCA Algorithm for Building Representations from Panoramic Images. In: 7th European Conference on Computer Vision - ECCV 2002, May 28-3, Copenhagen, Denmark.
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.
|Item Type: ||Conference or Workshop Item (Paper)|
|Keywords: ||appearance-based modeling, object recognition, principal component analysis, robust learning|
|Language of Content: ||English|
|Institution: ||University of Ljubljana|
|Department: ||Faculty of Computer and Information Science|
|Divisions: ||Faculty of Computer and Information Science > Computer Vision Laboratory|
|Item ID: ||50|
|Date Deposited: ||17 Mar 2003|
|Last Modified: ||13 Aug 2011 00:31|
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