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AAM[12] is an algorithm for matching a statistical shape model to an image with both shape and appearance variations.
SVM classifiers were trained on both shape and appearance feature vectors separately for each 36 pairwise combination of control and 8 syndromes (Table 1).
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Differing from previous approaches, the proposed model captures both the shape and appearance of the door.
To overcome these difficulties, we describe a method of producing large variations of mouth shape and gray-level appearance using a compact parametric appearance model, which represents both shape and gray-level appearance.
Recently, Niebles and Fei-Fei [4] enhance this approach by proposing a novel model characterized as a constellation of bags-of-features, which encodes both the shape and appearance of the actor.
Shapes and appearance registrations were used in a principal component analysis (PCA) to generate both the shape and appearance models for use with the AAM.
We synthesized 5 test faces at random moving along the first 15 components of both the shape and appearance models.
Active appearance models (AAM) were used to decouple shape and appearance in the digitized face images.
Wang, J. & Yuille, A. Semantic Part Segmentation using Compositional Model combing Shape and Appearance.
"Sometimes, it depends on the locale, they will change their shape and appearance".
"Shape" and "appearance", the two pillars of a deformable model, complement each other in object segmentation.
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