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A. Martinez; Jordi Vitria |
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Clustering in Image Space for Place Recognition and Visiual Annotations for Human-Robot Interaction. |
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2001 |
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IEEE Trans. on Systems, Man, and Cybernatics–Part B: Cybernetics, 31(5):669–682 (IF: 0.789) |
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OR;MV |
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BCNPCL @ bcnpcl @ MVi2001 |
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141 |
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L. Calvet; A. Ferrer; M. Gomes; A. Juan; David Masip |
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Combining Statistical Learning with Metaheuristics for the Multi-Depot Vehicle Routing Problem with Market Segmentation |
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2016 |
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Computers & Industrial Engineering |
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CIE |
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94 |
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93-104 |
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Multi-Depot Vehicle Routing Problem; market segmentation applications; hybrid algorithms; statistical learning |
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In real-life logistics and distribution activities it is usual to face situations in which the distribution of goods has to be made from multiple warehouses or depots to the nal customers. This problem is known as the Multi-Depot Vehicle Routing Problem (MDVRP), and it typically includes two sequential and correlated stages: (a) the assignment map of customers to depots, and (b) the corresponding design of the distribution routes. Most of the existing work in the literature has focused on minimizing distance-based distribution costs while satisfying a number of capacity constraints. However, no attention has been given so far to potential variations in demands due to the tness of the customerdepot mapping in the case of heterogeneous depots. In this paper, we consider this realistic version of the problem in which the depots are heterogeneous in terms of their commercial oer and customers show dierent willingness to consume depending on how well the assigned depot ts their preferences. Thus, we assume that dierent customer-depot assignment maps will lead to dierent customer-expenditure levels. As a consequence, market-segmentation strategiesneed to be considered in order to increase sales and total income while accounting for the distribution costs. To solve this extension of the MDVRP, we propose a hybrid approach that combines statistical learning techniques with a metaheuristic framework. First, a set of predictive models is generated from historical data. These statistical models allow estimating the demand of any customer depending on the assigned depot. Then, the estimated expenditure of each customer is included as part of an enriched objective function as a way to better guide the stochastic local search inside the metaheuristic framework. A set of computational experiments contribute to illustrate our approach and how the extended MDVRP considered here diers in terms of the proposed solutions from the traditional one. |
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PERGAMON-ELSEVIER SCIENCE LTD |
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0360-8352 |
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OR;MV; |
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Admin @ si @ CFG2016 |
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2749 |
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Laura Igual; Xavier Perez Sala; Sergio Escalera; Cecilio Angulo; Fernando De la Torre |
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Continuous Generalized Procrustes Analysis |
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2014 |
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Pattern Recognition |
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PR |
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47 |
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2 |
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659–671 |
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Procrustes analysis; 2D shape model; Continuous approach |
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PR4883, PII: S0031-3203(13)00327-0
Two-dimensional shape models have been successfully applied to solve many problems in computer vision, such as object tracking, recognition, and segmentation. Typically, 2D shape models are learned from a discrete set of image landmarks (corresponding to projection of 3D points of an object), after applying Generalized Procustes Analysis (GPA) to remove 2D rigid transformations. However, the
standard GPA process suffers from three main limitations. Firstly, the 2D training samples do not necessarily cover a uniform sampling of all the 3D transformations of an object. This can bias the estimate of the shape model. Secondly, it can be computationally expensive to learn the shape model by sampling 3D transformations. Thirdly, standard GPA methods use only one reference shape, which can might be insufficient to capture large structural variability of some objects.
To address these drawbacks, this paper proposes continuous generalized Procrustes analysis (CGPA).
CGPA uses a continuous formulation that avoids the need to generate 2D projections from all the rigid 3D transformations. It builds an efficient (in space and time) non-biased 2D shape model from a set of 3D model of objects. A major challenge in CGPA is the need to integrate over the space of 3D rotations, especially when the rotations are parameterized with Euler angles. To address this problem, we introduce the use of the Haar measure. Finally, we extended CGPA to incorporate several reference shapes. Experimental results on synthetic and real experiments show the benefits of CGPA over GPA. |
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OR; HuPBA; 605.203; 600.046;MILAB |
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Admin @ si @ IPE2014 |
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2352 |
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Petia Radeva; Judit Martinez; A. Tovar; X. Binefa; Jordi Vitria; Juan J. Villanueva |
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CORKIDENT: an automatic vision system for real-time inspection of natural products. |
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1999 |
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OR;MILAB;MV |
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BCNPCL @ bcnpcl @ RMT1999 |
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23 |
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Petia Radeva; Jordi Vitria |
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Corkinspect: Statistical Learning of Natural Material |
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2004 |
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Italian Beverage Technology, 13(38):11–18 |
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OR;MILAB;MV |
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BCNPCL @ bcnpcl @ RaV2004b |
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514 |
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