@conference {13163,
	title = {Constraint Integration for Multiview Pose Estimation of Humans with Self-Occlusions},
	booktitle = {3D Data Processing, Visualization, and Transmission, Third International Symposium on},
	year = {2006},
	month = {2006/06//},
	pages = {900 - 907},
	abstract = {Detection of articulated objects such as humans is an important task in computer vision. We present a system that incorporates a variety of constraints in a unified multi- view framework to automatically detect humans in possibly crowded scenes. These constraints include the kinematic constraints, the occlusion of one part by another and the high correlation between the appearance of parts such as the two arms. The graphical structure (non-tree) obtained is optimized in a nonparametric belief propagation framework using prior based search.},
	keywords = {articulated, belief, constraint;multiview, detection;computer, detection;pose, estimation;, estimation;nonparametric, framework;self-occlusion;computer, graphics;computer, method;graphical, methods;object, object, pose, propagation, structure;kinematic, vision;correlation},
	doi = {10.1109/3DPVT.2006.45},
	author = {Gupta,A. and Mittal,A. and Davis, Larry S.}
}
@conference {12625,
	title = {Segmentation and Probabilistic Registration of Articulated Body Models},
	booktitle = {Pattern Recognition, 2006. ICPR 2006. 18th International Conference on},
	volume = {2},
	year = {2006},
	month = {2006///},
	pages = {92 - 96},
	abstract = {There are different approaches to pose estimation and registration of different body parts using voxel data. We propose a general bottom-up approach in order to segment the voxels into different body parts. The voxels are first transformed into a high dimensional space which is the eigenspace of the Laplacian of the neighbourhood graph. We exploit the properties of this transformation and fit splines to the voxels belonging to different body segments in eigenspace. The boundary of the splines is determined by examination of the error in spline fitting. We then use a probabilistic approach to register the segmented body segments by utilizing their connectivity and prior knowledge of the general structure of the subjects. We present results on real data, containing both simple and complex poses. While we use human subjects in our experiment, the method is fairly general and can be applied to voxel-based registration of any articulated or non-rigid object composed of primarily 1-D parts},
	keywords = {(mathematics);, and, approach;eigenspace;image, articulated, body, data;voxel-based, eigenfunctions;graph, estimation;probabilistic, fitting;voxel, graph;pose, models;bottom-up, registration;eigenvalues, registration;image, registration;spline, segmentation;neighbourhood, segmentation;probability;splines, theory;image},
	doi = {10.1109/ICPR.2006.1034},
	author = {Sundaresan, A. and Chellapa, Rama}
}
@conference {14845,
	title = {Using the inner-distance for classification of articulated shapes},
	booktitle = {Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on},
	volume = {2},
	year = {2005},
	month = {2005/06//},
	pages = {719 - 726 vol. 2 - 719 - 726 vol. 2},
	abstract = {We propose using the inner-distance between landmark points to build shape descriptors. The inner-distance is defined as the length of the shortest path between landmark points within the shape silhouette. We show that the inner-distance is articulation insensitive and more effective at capturing complex shapes with part structures than Euclidean distance. To demonstrate this idea, it is used to build a new shape descriptor based on shape contexts. After that, we design a dynamic programming based method for shape matching and comparison. We have tested our approach on a variety of shape databases including an articulated shape dataset, MPEG7 CE-Shape-1, Kimia silhouettes, a Swedish leaf database and a human motion silhouette dataset. In all the experiments, our method demonstrates effective performance compared with other algorithms.},
	keywords = {articulated, CE-Shape-1, classification;, database;, databases;, dataset;, descriptor;, dynamic, human, image, inner-distance;, Kimia, landmark, leaf, matching;, MOTION, MPEG7, points;, programming;, SHAPE, silhouette, silhouette;, Swedish, visual},
	doi = {10.1109/CVPR.2005.362},
	author = {Ling,H. and Jacobs, David W.}
}
