9 resultados para Orth, Godlove Stoner, 1817-1882.
em Boston University Digital Common
Resumo:
http://www.archive.org/details/catholicgrieva00mealrich
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http://www.archive.org/details/historicalsketch00bartiala
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http://www.archive.org/details/cannibalmission00pattuoft
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http://www.archive.org/details/missionspacific00eellrich
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http://www.archive.org/details/womenofachieveme00brawrich
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http://moa.umdl.umich.edu/cgi/sgml/moa-idx?notisid=AAU8319 View book via University of Michigan
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http://www.archive.org/details/hinduismandchris00bachuoft
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An improved method for deformable shape-based image segmentation is described. Image regions are merged together and/or split apart, based on their agreement with an a priori distribution on the global deformation parameters for a shape template. The quality of a candidate region merging is evaluated by a cost measure that includes: homogeneity of image properties within the combined region, degree of overlap with a deformed shape model, and a deformation likelihood term. Perceptually-motivated criteria are used to determine where/how to split regions, based on the local shape properties of the region group's bounding contour. A globally consistent interpretation is determined in part by the minimum description length principle. Experiments show that the model-based splitting strategy yields a significant improvement in segmention over a method that uses merging alone.
Resumo:
A common assumption made in traffic matrix (TM) modeling and estimation is independence of a packet's network ingress and egress. We argue that in real IP networks, this assumption should not and does not hold. The fact that most traffic consists of two-way exchanges of packets means that traffic streams flowing in opposite directions at any point in the network are not independent. In this paper we propose a model for traffic matrices based on independence of connections rather than packets. We argue that the independent connection (IC) model is more intuitive, and has a more direct connection to underlying network phenomena than the gravity model. To validate the IC model, we show that it fits real data better than the gravity model and that it works well as a prior in the TM estimation problem. We study the model's parameters empirically and identify useful stability properties. This justifies the use of the simpler versions of the model for TM applications. To illustrate the utility of the model we focus on two such applications: synthetic TM generation and TM estimation. To the best of our knowledge this is the first traffic matrix model that incorporates properties of bidirectional traffic.