| |
Records |
Links |
|
Author |
L. Calvet; A. Ferrer; M. Gomes; A. Juan; David Masip |
|
| |
Title |
Combining Statistical Learning with Metaheuristics for the Multi-Depot Vehicle Routing Problem with Market Segmentation |
Type |
Journal Article |
| |
Year |
2016 |
Publication |
Computers & Industrial Engineering |
Abbreviated Journal |
CIE |
|
| |
Volume |
94 |
Issue |
|
Pages |
93-104 |
|
| |
Keywords |
Multi-Depot Vehicle Routing Problem; market segmentation applications; hybrid algorithms; statistical learning |
|
| |
Abstract |
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 o er and customers show di erent willingness to consume depending on how well the assigned depot ts their preferences. Thus, we assume that di erent customer-depot assignment maps will lead to di erent 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 di ers in terms of the proposed solutions from the traditional one. |
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
PERGAMON-ELSEVIER SCIENCE LTD |
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
CIE |
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN  |
0360-8352 |
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
OR;MV; |
Approved |
no |
|
| |
Call Number |
Admin @ si @ CFG2016 |
Serial |
2749 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Jorge Bernal; F. Javier Sanchez; Gloria Fernandez Esparrach; Debora Gil; Cristina Rodriguez de Miguel; Fernando Vilariño |
|
| |
Title |
WM-DOVA Maps for Accurate Polyp Highlighting in Colonoscopy: Validation vs. Saliency Maps from Physicians |
Type |
Journal Article |
| |
Year |
2015 |
Publication |
Computerized Medical Imaging and Graphics |
Abbreviated Journal |
CMIG |
|
| |
Volume |
43 |
Issue |
|
Pages |
99-111 |
|
| |
Keywords |
Polyp localization; Energy Maps; Colonoscopy; Saliency; Valley detection |
|
| |
Abstract |
We introduce in this paper a novel polyp localization method for colonoscopy videos. Our method is based on a model of appearance for polyps which defines polyp boundaries in terms of valley information. We propose the integration of valley information in a robust way fostering complete, concave and continuous boundaries typically associated to polyps. This integration is done by using a window of radial sectors which accumulate valley information to create WMDOVA1 energy maps related with the likelihood of polyp presence. We perform a double validation of our maps, which include the introduction of two new databases, including the first, up to our knowledge, fully annotated database with clinical metadata associated. First we assess that the highest value corresponds with the location of the polyp in the image. Second, we show that WM-DOVA energy maps can be comparable with saliency maps obtained from physicians' fixations obtained via an eye-tracker. Finally, we prove that our method outperforms state-of-the-art computational saliency results. Our method shows good performance, particularly for small polyps which are reported to be the main sources of polyp miss-rate, which indicates the potential applicability of our method in clinical practice. |
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
0895-6111 |
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
MV; IAM; 600.047; 600.060; 600.075;SIAI |
Approved |
no |
|
| |
Call Number |
Admin @ si @ BSF2015 |
Serial |
2609 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Bogdan Raducanu; Fadi Dornaika |
|
| |
Title |
Texture-independent recognition of facial expressions in image snapshots and videos |
Type |
Journal Article |
| |
Year |
2013 |
Publication |
Machine Vision and Applications |
Abbreviated Journal |
MVA |
|
| |
Volume |
24 |
Issue |
4 |
Pages |
811-820 |
|
| |
Keywords |
|
|
| |
Abstract |
This paper addresses the static and dynamic recognition of basic facial expressions. It has two main contributions. First, we introduce a view- and texture-independent scheme that exploits facial action parameters estimated by an appearance-based 3D face tracker. We represent the learned facial actions associated with different facial expressions by time series. Second, we compare this dynamic scheme with a static one based on analyzing individual snapshots and show that the former performs better than the latter. We provide evaluations of performance using three subspace learning techniques: linear discriminant analysis, non-parametric discriminant analysis and support vector machines. |
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
Springer-Verlag |
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
0932-8092 |
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
OR; 600.046; 605.203;MV |
Approved |
no |
|
| |
Call Number |
Admin @ si @ RaD2013 |
Serial |
2230 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
David Masip; Agata Lapedriza; Jordi Vitria |
|
| |
Title |
Boosted Online Learning for Face Recognition |
Type |
Journal Article |
| |
Year |
2009 |
Publication |
IEEE Transactions on Systems, Man and Cybernetics part B |
Abbreviated Journal |
TSMCB |
|
| |
Volume |
39 |
Issue |
2 |
Pages |
530–538 |
|
| |
Keywords |
|
|
| |
Abstract |
Face recognition applications commonly suffer from three main drawbacks: a reduced training set, information lying in high-dimensional subspaces, and the need to incorporate new people to recognize. In the recent literature, the extension of a face classifier in order to include new people in the model has been solved using online feature extraction techniques. The most successful approaches of those are the extensions of the principal component analysis or the linear discriminant analysis. In the current paper, a new online boosting algorithm is introduced: a face recognition method that extends a boosting-based classifier by adding new classes while avoiding the need of retraining the classifier each time a new person joins the system. The classifier is learned using the multitask learning principle where multiple verification tasks are trained together sharing the same feature space. The new classes are added taking advantage of the structure learned previously, being the addition of new classes not computationally demanding. The present proposal has been (experimentally) validated with two different facial data sets by comparing our approach with the current state-of-the-art techniques. The results show that the proposed online boosting algorithm fares better in terms of final accuracy. In addition, the global performance does not decrease drastically even when the number of classes of the base problem is multiplied by eight. |
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
1083–4419 |
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
OR;MV |
Approved |
no |
|
| |
Call Number |
BCNPCL @ bcnpcl @ MLV2009 |
Serial |
1155 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Santiago Segui; Michal Drozdzal; Fernando Vilariño; Carolina Malagelada; Fernando Azpiroz; Petia Radeva; Jordi Vitria |
|
| |
Title |
Categorization and Segmentation of Intestinal Content Frames for Wireless Capsule Endoscopy |
Type |
Journal Article |
| |
Year |
2012 |
Publication |
IEEE Transactions on Information Technology in Biomedicine |
Abbreviated Journal |
TITB |
|
| |
Volume |
16 |
Issue |
6 |
Pages |
1341-1352 |
|
| |
Keywords |
|
|
| |
Abstract |
Wireless capsule endoscopy (WCE) is a device that allows the direct visualization of gastrointestinal tract with minimal discomfort for the patient, but at the price of a large amount of time for screening. In order to reduce this time, several works have proposed to automatically remove all the frames showing intestinal content. These methods label frames as {intestinal content – clear} without discriminating between types of content (with different physiological meaning) or the portion of image covered. In addition, since the presence of intestinal content has been identified as an indicator of intestinal motility, its accurate quantification can show a potential clinical relevance. In this paper, we present a method for the robust detection and segmentation of intestinal content in WCE images, together with its further discrimination between turbid liquid and bubbles. Our proposal is based on a twofold system. First, frames presenting intestinal content are detected by a support vector machine classifier using color and textural information. Second, intestinal content frames are segmented into {turbid, bubbles, and clear} regions. We show a detailed validation using a large dataset. Our system outperforms previous methods and, for the first time, discriminates between turbid from bubbles media. |
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
1089-7771 |
ISBN |
|
Medium |
|
|
| |
Area |
800 |
Expedition |
|
Conference |
|
|
| |
Notes |
MILAB; MV; OR;SIAI |
Approved |
no |
|
| |
Call Number |
Admin @ si @ SDV2012 |
Serial |
2124 |
|
| Permanent link to this record |
