@article {12522,
	title = {Appearance Modeling Using a Geometric Transform},
	journal = {Image Processing, IEEE Transactions on},
	volume = {18},
	year = {2009},
	month = {2009/04//},
	pages = {889 - 902},
	abstract = {A general transform, called the geometric transform (GeT), that models the appearance inside a closed contour is proposed. The proposed GeT is a functional of an image intensity function and a region indicator function derived from a closed contour. It can be designed to combine the shape and appearance information at different resolutions and to generate models invariant to deformation, articulation, or occlusion. By choosing appropriate functionals and region indicator functions, the GeT unifies Radon transform, trace transform, and a class of image warpings. By varying the region indicator and the types of features used for appearance modeling, five novel types of GeTs are introduced and applied to fingerprinting the appearance inside a contour. They include the GeTs based on a level set, shape matching, feature curves, and the GeT invariant to occlusion, and a multiresolution GeT (MRGeT). Applications of GeT to pedestrian identity recognition, human body part segmentation, and image synthesis are illustrated. The proposed approach produces promising results when applied to fingerprinting the appearance of a human and body parts despite the presence of nonrigid deformations and articulated motion.},
	keywords = {body, contour;feature, curve;fingerprinting;geometric, function;image, function;shape, geometry;image, identity, indicator, INTENSITY, matching;trace, method;pedestrian, modeling;closed, part, Radon, recognition;region, resolution;image, segmentation;, segmentation;image, set, synthesis;image, transform;appearance, transform;human, transform;Radon, transforms;computational, warping;level},
	isbn = {1057-7149},
	doi = {10.1109/TIP.2008.2011381},
	author = {Li,Jian and Zhou,S. K and Chellapa, Rama}
}
@conference {12649,
	title = {Appearance modeling under geometric context},
	booktitle = {Computer Vision, 2005. ICCV 2005. Tenth IEEE International Conference on},
	volume = {2},
	year = {2005},
	month = {2005/10//},
	pages = {1252 -1259 Vol. 2 - 1252 -1259 Vol. 2},
	abstract = {We propose a unified framework based on a general definition of geometric transform (GeT) for modeling appearance. GeT represents the appearance by applying designed functionals over certain geometric sets. We show that image warping, Radon transform, trace transform, etc. are special cases of our definition. Moreover, three different types of GeTs are designed to handle deformation, articulation and occlusion and applied to fingerprinting the appearance inside a contour. They include the contour-driven GeT, the feature curve based GeT and selecting functionals to model the appearance inside the convex hull of the contour. A multi-resolution representation that combines both shape and appearance information is also proposed. We apply our approach to image synthesis and object recognition. The proposed approach produces promising results when applied to fingerprinting the appearance of human and body parts despite the challenges due to articulated motion and deformations},
	keywords = {based, curve, geometry;image, GeT;convex, GeT;fingerprinting;geometric, hull;feature, information;trace, modeling;contour-driven, Radon, recognition;, recognition;shape, representation;object, synthesis;image, transform;appearance, transform;image, transform;Radon, transforms;computational, warping;multi-resolution},
	doi = {10.1109/ICCV.2005.38},
	author = {Li,Jian and Zhou,S. K and Chellapa, Rama}
}
@conference {14859,
	title = {Deformation invariant image matching},
	booktitle = {Computer Vision, 2005. ICCV 2005. Tenth IEEE International Conference on},
	volume = {2},
	year = {2005},
	month = {2005/10//},
	pages = {1466 -1473 Vol. 2 - 1466 -1473 Vol. 2},
	abstract = {We propose a novel framework to build descriptors of local intensity that are invariant to general deformations. In this framework, an image is embedded as a 2D surface in 3D space, with intensity weighted relative to distance in x-y. We show that as this weight increases, geodesic distances on the embedded surface are less affected by image deformations. In the limit, distances are deformation invariant. We use geodesic sampling to get neighborhood samples for interest points, and then use a geodesic-intensity histogram (GIH) as a deformation invariant local descriptor. In addition to its invariance, the new descriptor automatically finds its support region. This means it can safely gather information from a large neighborhood to improve discriminability. Furthermore, we propose a matching method for this descriptor that is invariant to affine lighting changes. We have tested this new descriptor on interest point matching for two data sets, one with synthetic deformation and lighting change, and another with real non-affine deformations. Our method shows promising matching results compared to several other approaches},
	keywords = {deformation, deformations;nonaffine, deformations;point, descriptor;geodesic, distances;geodesic, geometry;differential, geometry;image, histogram;image, image, invariant, local, matching;computational, matching;deformation, matching;image, morphing;, sampling;geodesic-intensity},
	doi = {10.1109/ICCV.2005.67},
	author = {Ling,Haibin and Jacobs, David W.}
}
