@conference {13281,
	title = {Constant-time navigation in four-dimensional nested simplicial meshes},
	booktitle = {Shape Modeling Applications, 2004. Proceedings},
	year = {2004},
	month = {2004/06//},
	pages = {221 - 230},
	abstract = {We consider a recursive decomposition of a four-dimensional hypercube into a hierarchy of nested 4-dimensional simplexes, that we call pentatopes. The paper presents an algorithm for finding the neighbors of a pentatope along its five tetrahedral faces in constant time. To this aim, we develop a labeling technique for nested pentatopes that enables their identification by using location codes. The constant-time behavior is achieved through bit manipulation operations, thus avoiding traversing the simplicial hierarchy via pointer following. We discuss an application of this representation to multi-resolution representations of four-dimensional scalar fields. Extracting adaptive continuous approximations of the scalar field from such a model requires generating conforming meshes, i.e., meshes in which the pentatopes match along their tetrahedral faces. Our neighbor finding algorithm enables computing face-adjacent pentatopes efficiently.},
	keywords = {4-dimensional, adaptive, algorithm;, approximations;, bit, codes;, computational, constant-time, continuous, decomposition;, face-adjacent, faces;, fields;, finding, following;, four-dimensional, generation;, geometry;, hierarchy;, hypercube;, location, manipulation, mesh, meshes;, multiresolution, navigation;, neighbor, nested, operations;, pentatopes;, pointer, recursive, representation;, scalar, simplexes;, simplicial, tetrahedral},
	doi = {10.1109/SMI.2004.1314509},
	author = {Lee,M. and De Floriani, Leila and Samet, Hanan}
}
@conference {12766,
	title = {Probabilistic recognition of human faces from video},
	booktitle = {Image Processing. 2002. Proceedings. 2002 International Conference on},
	volume = {1},
	year = {2002},
	month = {2002///},
	pages = {I-41 - I-44 vol.1 - I-41 - I-44 vol.1},
	abstract = {Most present face recognition approaches recognize faces based on still images. We present a novel approach to recognize faces in video. In that scenario, the face gallery may consist of still images or may be derived from a videos. For evidence integration we use classical Bayesian propagation over time and compute the posterior distribution using sequential importance sampling. The probabilistic approach allows us to handle uncertainties in a systematic manner. Experimental results using videos collected by NIST/USF and CMU illustrate the effectiveness of this approach in both still-to-video and video-to-video scenarios with appropriate model choices.},
	keywords = {Bayes, Bayesian, CMU;, distribution;, Face, faces;, gallery;, handling;, human, image, images;, importance, likelihood;, methods;, NIST/USF;, observation, posterior, probabilistic, probability;, processing;, propagation;, recognition;, sampling;, sequential, signal, still, Still-to-video, Uncertainty, video, Video-to-video},
	doi = {10.1109/ICIP.2002.1037954},
	author = {Chellapa, Rama and Kruger, V. and Zhou,Shaohua}
}
