@conference {12483,
	title = {Fast directional chamfer matching},
	booktitle = {Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on},
	year = {2010},
	month = {2010/06//},
	pages = {1696 - 1703},
	abstract = {We study the object localization problem in images given a single hand-drawn example or a gallery of shapes as the object model. Although many shape matching algorithms have been proposed for the problem over the decades, chamfer matching remains to be the preferred method when speed and robustness are considered. In this paper, we significantly improve the accuracy of chamfer matching while reducing the computational time from linear to sublinear (shown empirically). Specifically, we incorporate edge orientation information in the matching algorithm such that the resulting cost function is piecewise smooth and the cost variation is tightly bounded. Moreover, we present a sublinear time algorithm for exact computation of the directional chamfer matching score using techniques from 3D distance transforms and directional integral images. In addition, the smooth cost function allows to bound the cost distribution of large neighborhoods and skip the bad hypotheses within. Experiments show that the proposed approach improves the speed of the original chamfer matching upto an order of 45 $\#$x00D7;, and it is much faster than many state of art techniques while the accuracy is comparable.},
	keywords = {3D, algorithm;edge, algorithms;single, chamfer, cost, detection;image, directional, distance, distribution;cost, example;smooth, function;sublinear, hand-drawn, images;edge, information;fast, integral, localization, MATCHING, matching;gallery, matching;transforms;, model;piecewise, of, orientation, problem;object, score;directional, shapes;object, smooth;shape, TIME, time;cost, transforms;computational, variation;directional},
	doi = {10.1109/CVPR.2010.5539837},
	author = {Ming-Yu Liu and Tuzel, O. and Veeraraghavan,A. and Chellapa, Rama}
}
@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}
}
