@article {13125,
	title = {Constraint Integration for Efficient Multiview Pose Estimation with Self-Occlusions},
	journal = {Pattern Analysis and Machine Intelligence, IEEE Transactions on},
	volume = {30},
	year = {2008},
	month = {2008/03//},
	pages = {493 - 506},
	abstract = {Automatic initialization and tracking of human pose is an important task in visual surveillance. We present a part-based approach that incorporates a variety of constraints in a unified framework. These constraints include the kinematic constraints between parts that are physically connected to each other, the occlusion of one part by another, and the high correlation between the appearance of certain parts, such as the arms. The location probability distribution of each part is determined by evaluating appropriate likelihood measures. The graphical (nontree) structure representing the interdependencies between parts is utilized to "connect" such part distributions via nonparametric belief propagation. Methods are also developed to perform this optimization efficiently in the large space of pose configurations.},
	keywords = {Automated;Posture;Reproducibility of Results;Sensitivity and Specificity;Video Recording;Whole Body Imaging;, automatic initialization;constraint integration;graphical structure;human pose tracking;kinematic constraint;likelihood measure;location probability distribution;multiview pose estimation;nonparametric belief propagation;optimization;pose configuration;se, Computer-Assisted;Imaging, Three-Dimensional;Pattern Recognition},
	isbn = {0162-8828},
	doi = {10.1109/TPAMI.2007.1173},
	author = {Gupta,A. and Mittal,A. and Davis, Larry S.}
}
@article {14892,
	title = {Shape Classification Using the Inner-Distance},
	journal = {Pattern Analysis and Machine Intelligence, IEEE Transactions on},
	volume = {29},
	year = {2007},
	month = {2007/02//},
	pages = {286 - 299},
	abstract = {Part structure and articulation are of fundamental importance in computer and human vision. We propose using the inner-distance to build shape descriptors that are robust to articulation and capture part structure. The inner-distance is defined as the length of the shortest path between landmark points within the shape silhouette. We show that it is articulation insensitive and more effective at capturing part structures than the Euclidean distance. This suggests that the inner-distance can be used as a replacement for the Euclidean distance to build more accurate descriptors for complex shapes, especially for those with articulated parts. In addition, texture information along the shortest path can be used to further improve shape classification. With this idea, we propose three approaches to using the inner-distance. The first method combines the inner-distance and multidimensional scaling (MDS) to build articulation invariant signatures for articulated shapes. The second method uses the inner-distance to build a new shape descriptor based on shape contexts. The third one extends the second one by considering the texture information along shortest paths. The proposed approaches have been tested on a variety of shape databases, including an articulated shape data set, MPEG7 CE-Shape-1, Kimia silhouettes, the ETH-80 data set, two leaf data sets, and a human motion silhouette data set. In all the experiments, our methods demonstrate effective performance compared with other algorithms},
	keywords = {Automated;Reproducibility of Results;Sensitivity and Specificity;, Computer-Assisted;Imaging, Euclidean distance;articulation invariant signatures;computer vision;inner distance;multidimensional scaling;part structure;shape classification;shape descriptors;shape silhouette;shortest path;computational geometry;computer vision;graph theory;image cla, Three-Dimensional;Pattern Recognition},
	isbn = {0162-8828},
	doi = {10.1109/TPAMI.2007.41},
	author = {Ling,Haibin and Jacobs, David W.}
}
