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Plan Guanajuato (La Sandía), Mexico

Alba A.,Autonomous University of San Luis Potosi | Arce-Santana E.,Autonomous University of San Luis Potosi | Rivera M.,Research Center En Matematicas Ac
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2010

Motion estimation is one of the most important tasks in computer vision. One popular technique for computing dense motion fields consists in defining a large enough set of candidate motion vectors, and assigning one of such vectors to each pixel, so that a given cost function is minimized. In this work we propose a novel method for finding a small set of adequate candidates, making the minimization process computationally more efficient. Based on this method, we present algorithms for the estimation of dense optical flow using two minimization approaches: one based on a classic block-matching procedure, and another one based on entropy-controlled quadratic Markov measure fields which allow one to obtain smooth motion fields. Finally, we present the results obtained from the application of these algorithms to examples taken from the Middlebury database. © 2010 Springer-Verlag. Source


Ramirez-Manzanares A.,University of Guanajuato | Rivera M.,Research Center En Matematicas Ac | Kornprobst P.,French Institute for Research in Computer Science and Automation | Lauze F.,Copenhagen University
Journal of Mathematical Imaging and Vision | Year: 2011

Motion estimation in sequences with transparencies is an important problem in robotics and medical imaging applications. In this work we propose a variational approach for estimating multi-valued velocity fields in transparent sequences. Starting from existing local motion estimators, we derive a variational model for integrating in space and time such a local information in order to obtain a robust estimation of the multi-valued velocity field. With this approach, we can indeed estimate multi-valued velocity fields which are not necessarily piecewise constant on a layer-each layer can evolve according to a non-parametric optical flow. We show how our approach outperforms existing methods; and we illustrate its capabilities on challenging experiments on both synthetic and real sequences. © 2011 Springer Science+Business Media, LLC. Source


Velasco-Hernandez J.X.,National Autonomous University of Mexico | Nunez-Lopez M.,Metropolitan Autonomous University | Comas-Garcia A.,National Autonomous University of Mexico | Cherpitel D.E.N.,Autonomous University of San Luis Potosi | Ocampo M.C.,Research Center En Matematicas Ac
PLoS ONE | Year: 2015

The objective of this paper is to explain through the ecological hypothesis superinfection and competitive interaction between two viral populations and niche (host) availability, the alternating patterns of Respiratory Syncytial Virus (RSV) and influenza observed in a regional hospital in San Luis Potosí State, México using a mathematical model as a methodological tool. The data analyzed consists of community-based and hospital-based Acute Respiratory Infections (ARI) consultations provided by health-care institutions reported to the State Health Service Epidemiology Department from 2003 through 2009. © 2015 Velasco-Hernández et al. Source


Munoz E.,Research Center En Matematicas Ac | Capon-Garcia E.,ETH Zurich | Lainez-Aguirre J.M.,Purdue University | Espuna A.,Polytechnic University of Catalonia | Puigjaner L.,Polytechnic University of Catalonia
Computers and Chemical Engineering | Year: 2015

The integration of planning and scheduling decisions in rigorous mathematical models usually results in large scale problems. In order to tackle the problem complexity, decomposition techniques based on duality and information flows between a master and a set of subproblems are widely applied. In this sense, ontologies improve information sharing and communication in enterprises and can even represent holistic mathematical models facilitating the use of analytic tools and providing higher flexibility for model building. In this work, we exploit this ontologies' capability to address the optimal integration of planning and scheduling using a Lagrangian decomposition approach. Scheduling/planning sub-problems are created for each facility/supply chain entity and their dual solution information is shared by means of the ontological framework. Two case studies based on a STN representation of supply chain planning and scheduling models are presented to emphasize the advantages and limitations of the proposed approach. © 2014 Elsevier Ltd. Source


Munoz E.,Research Center En Matematicas Ac | Capon-Garcia E.,ETH Zurich | Lainez J.M.,Purdue University | Espuna A.,Polytechnic University of Catalonia | Puigjaner L.,Polytechnic University of Catalonia
Chemical Engineering Research and Design | Year: 2013

Enterprises are highly complex systems in which one or more organizations share a definite mission, goals and objectives to offer a product or service. In this study, an ontological framework is built as a mechanism for exchanging information and knowledge models for multiple applications and effective integration between hierarchical levels. The potential of the general semantic framework that is developed is demonstrated using a case study concerning the enterprise supply chain network design-planning problem. © 2013 The Institution of Chemical Engineers. Source

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