Robust Recognition Using Eigenimages (bibtex)
by Ales Leonardis, Horst Bischof, Roland Ebensberger
Abstract:
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 outliers, occlusions, and segmentation. In this paper we present a new approach which successfully solves these problems. The major novelty of 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. Competing 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.
Reference:
Robust Recognition Using Eigenimages (Ales Leonardis, Horst Bischof, Roland Ebensberger), Technical report, PRIP, TU Wien, 1997.
Bibtex Entry:
@TechReport{TR047,
  author =	 "Ales Leonardis and Horst Bischof and Roland
                  Ebensberger",
  institution =	 "PRIP, TU Wien",
  number =	 "PRIP-TR-047",
  title =	 "Robust {R}ecognition Using {E}igenimages",
  year =	 "1997",
  url =		 "ftp://ftp.prip.tuwien.ac.at/pub/publications/trs/tr47.pdf",
  abstract =	 "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 outliers,
                  occlusions, and segmentation. In this paper we
                  present a new approach which successfully solves
                  these problems. The major novelty of 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. Competing 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.",
}
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