@CONFERENCE\{IMM2004-03134,
    author       = "S. Darkner and R. Larsen and M. B. Stegmann and B. K. Ersb{\o}ll",
    title        = "Wedgelet Enhanced Appearance Models",
    year         = "2004",
    month        = "jul",
    keywords     = "appearance model, wedgelet, image segmentation, quadtree,",
    booktitle    = "2nd International Workshop on Generative Model Based Vision ({GMBV} 2004), Washington, D. C., July, 2nd",
    volume       = "",
    series       = "",
    editor       = "Arthur Pece, Rasmus Larsen, and Alan Yuille",
    publisher    = "IEEE",
    organization = "",
    address      = "New Jersey, {US}",
    url          = "http://www2.compute.dtu.dk/pubdb/pubs/3134-full.html",
    abstract     = "Statistical region-based segmentation methods such as the Active
Appearance Model (AAM) are used for establishing dense
correspondences in images based on learning the variation in shape
and pixel intensities in a training set. For low resolution {2D} 
images correspondences can be recovered reliably in real-time.
However, as resolution increases this becomes infeasible due to
excessive storage and computational requirements. In this paper we
propose to reduce the textural components by modelling the
coefficients of a wedgelet based regression tree instead of the
original pixel intensities. The wedgelet regression trees employed
are based on triangular domains and estimated using cross
validation. The wedgelet regression trees are functional
descriptions of the intensity information and serve to 1) reduce
noise and 2) produce a compact textural description. The wedgelet
enhanced appearance model is applied to a case study of human
faces. Compression rates of the texture information of 1:40 is
obtained without sacrificing segmentation accuracy noticably, even
at compression rates of 1:150 fair segmentation is achieved."
}