Pierre Carlier - UPMC

Pierre Carlier
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Name
Pierre Carlier
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UPMC
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Computer Science - Learning (2)
 
Computer Science - Computer Vision and Pattern Recognition (2)
 
Computer Science - Logic in Computer Science (1)

Publications Authored By Pierre Carlier

A decade ago, Abdulla et al introduced the elegant concept of decisiveness for denumerable Markov chains [1]. Roughly decisiveness allows one to lift most good properties from finite Markov chains to denumerable ones, and therefore to adapt existing verification algorithms to infinite-state models. Denumerable Markov chains however do not encompass stochastic real-time systems, and general stochastic transition systems (STSs) are needed. Read More

2013Aug
Affiliations: 1INRIA Saclay - Ile de France, 2INRIA Saclay - Ile de France, CVN, 3INRIA Saclay - Ile de France, CVN, 4MIRCEN, UPMC, 5UPMC, 6INRIA Saclay - Ile de France, MAS, LIGM, ENPC, 7INRIA Saclay - Ile de France, CVN

The Random Walks (RW) algorithm is one of the most e - cient and easy-to-use probabilistic segmentation methods. By combining contrast terms with prior terms, it provides accurate segmentations of medical images in a fully automated manner. However, one of the main drawbacks of using the RW algorithm is that its parameters have to be hand-tuned. Read More

2013Jun
Affiliations: 1INRIA Saclay - Ile de France, 2INRIA Saclay - Ile de France, CVN, 3INRIA Saclay - Ile de France, CVN, 4MIRCEN, UPMC, 5UPMC, 6INRIA Saclay - Ile de France, LIGM, ENPC, MAS, 7INRIA Saclay - Ile de France, CVN

The Random Walks (RW) algorithm is one of the most e - cient and easy-to-use probabilistic segmentation methods. By combining contrast terms with prior terms, it provides accurate segmentations of medical images in a fully automated manner. However, one of the main drawbacks of using the RW algorithm is that its parameters have to be hand-tuned. Read More