@MASTERSTHESIS\{IMM2000-0124,
    author       = "M. B. Stegmann",
    title        = "Active Appearance Models: Theory, Extensions and Cases",
    year         = "2000",
    month        = "aug",
    edition      = "2",
    pages        = "262",
    school       = "Informatics and Mathematical Modelling, Technical University of Denmark, {DTU}",
    address      = "Richard Petersens Plads, Building 321, {DK-}2800 Kgs. Lyngby",
    type         = "",
    url          = "http://www.imm.dtu.dk/~aam/main/",
    abstract     = "This thesis presents a general approach towards image segmentation using the learning-based deformable model Active Appearance Model (AAM) proposed by Cootes et al. The primary advantage of AAMs is that a priori knowledge is learned through observation of both shape and texture variation in a training set. From this, a compact object class description is derived, which can be used to rapidly search images for new object instances. 
A thorough treatment and discussion of the theory behind AAMs is given, followed by several extensions to the basic {AAM,} which constitutes the major contribution of this thesis. Extensions include automatic initialization and unification of finite element models and AAMs. All of these have been implemented in a structured and fast C++ framework; the {AAM-API}.
Finally, case studies based on radiographs of metacarpals, cardiovascular magnetic resonance images and perspective images of pork carcass are presented. Herein the performance of the basic {AAM} and the developed extensions are assessment using leave-one-out evaluation.
It is concluded that AAMs -- as a data-driven and fully automated method -- successfully can perform object segmentation in challenging and very different image modalities with very high accuracy. In two of three cases subpixel accuracy were obtained w.r.t. object segmentation.",
    isbn_issn    = "IMM-EKS-2000-25"
}