Robust Recovery of Eigenimages in the Presence of Outliers and Occlusions

Aleš Leonardis, Horst Bischof


The basic limitations of the current appearance-based matching methods using eigenimages are non-robust estimation of coefficients and inability to cope with problems related to occlusions and segmentation. In this paper we present a new approach which successfully solves these problems. The major novelty o f our approach lies in the way how the coefficients of the eigenimages are determined. Instead of computing the coefficients by a projection of the data onto the eigenimages, we extract them by a hypothesize-and-test paradigm using subsets of image points. Compeling hypotheses are then subject to a selection procedure based on the Minimum Description Length principle. The approach enables us not only to reject outliers and to deal with occlusions but also to simultaneously use multiple classes of eigenimages.


appearance-based matching, principal component analysis, robust estimation. occlusion, discrete optimization

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This work is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License.

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