@CONFERENCE\{IMM2004-02807,
    author       = "M. B. Stegmann and S. Forchhammer and T. F. Cootes",
    title        = "Wavelet Enhanced Appearance Modelling",
    year         = "2004",
    keywords     = "segmentation, deformable models, active appearance models, wavelet compression, bi-orthogonal wavelets",
    pages        = "1823-1832",
    booktitle    = "International Symposium on Medical Imaging 2004, San Diego, {CA,} Proc. of {SPIE} vol. 5370",
    volume       = "",
    series       = "",
    editor       = "Michael J. Fitzpatrick",
    publisher    = "SPIE",
    organization = "",
    address      = "",
    url          = "http://www2.compute.dtu.dk/pubdb/pubs/2807-full.html",
    abstract     = "Generative segmentation methods such as the Active Appearance Models (AAM) establish dense correspondences by modelling variation of shape and pixel intensities. Alas, for {3D} and high-resolution {2D} images typical in medical imaging, this approach is rendered infeasible due to excessive storage and computational requirements. This paper extends the previous work of Wolstenholme and Taylor where Haar wavelet coefficient subsets were modelled rather than pixel intensities. In addition to a detailed review of the method and a discussion of the integration into an {AAM-}framework, we demonstrate that the more recent bi-orthogonal {CDF} {9-}7 wavelet offers advantages over the traditional Haar wavelet in terms of synthesis quality and accuracy. Further, we demonstrate that the inherent frequency separation in wavelets allows for simple band-pass filtering, e.g. edge-emphasis. Experiments using Haar and {CDF} {9-}7 wavelets on face images have shown that segmentation accuracy degrades gracefully with increasing compression ratio. Further, a proposed weighting scheme emphasizing edges was shown to be significantly more accurate at compression ratio 1:{1,} than a conventional {AAM}. At higher compression ratios the scheme offered both a decrease in complexity and an increase in segmentation accuracy."
}