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Author | Carles Sanchez; Debora Gil; T. Gache; N. Koufos; Marta Diez-Ferrer; Antoni Rosell | ||||
Title | SENSA: a System for Endoscopic Stenosis Assessment | Type | Conference Article | ||
Year | 2016 | Publication | 28th Conference of the international Society for Medical Innovation and Technology | Abbreviated Journal | |
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Abstract | Documenting the severity of a static or dynamic Central Airway Obstruction (CAO) is crucial to establish proper diagnosis and treatment, predict possible treatment effects and better follow-up the patients. The subjective visual evaluation of a stenosis during video-bronchoscopy still remains the most common way to assess a CAO in spite of a consensus among experts for a need to standardize all calculations [1].
The Computer Vision Center in cooperation with the «Hospital de Bellvitge», has developed a System for Endoscopic Stenosis Assessment (SENSA), which computes CAO directly by analyzing standard bronchoscopic data without the need of using other imaging tecnologies. |
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Address | Rotterdam; The Netherlands; October 2016 | ||||
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Area | Expedition | Conference | SMIT | ||
Notes | IAM; | Approved | no | ||
Call Number | Admin @ si @ SGG2016 | Serial | 2942 | ||
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Author | Thanh Ha Do; Salvatore Tabbone; Oriol Ramos Terrades | ||||
Title | Sparse representation over learned dictionary for symbol recognition | Type | Journal Article | ||
Year | 2016 | Publication | Signal Processing | Abbreviated Journal | SP |
Volume | 125 | Issue | Pages | 36-47 | |
Keywords | Symbol Recognition; Sparse Representation; Learned Dictionary; Shape Context; Interest Points | ||||
Abstract | In this paper we propose an original sparse vector model for symbol retrieval task. More specically, we apply the K-SVD algorithm for learning a visual dictionary based on symbol descriptors locally computed around interest points. Results on benchmark datasets show that the obtained sparse representation is competitive related to state-of-the-art methods. Moreover, our sparse representation is invariant to rotation and scale transforms and also robust to degraded images and distorted symbols. Thereby, the learned visual dictionary is able to represent instances of unseen classes of symbols. | ||||
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Notes | DAG; 600.061; 600.077 | Approved | no | ||
Call Number | Admin @ si @ DTR2016 | Serial | 2946 | ||
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Author | Thanh Ha Do; Salvatore Tabbone; Oriol Ramos Terrades | ||||
Title | Spotting Symbol over Graphical Documents Via Sparsity in Visual Vocabulary | Type | Book Chapter | ||
Year | 2016 | Publication | Recent Trends in Image Processing and Pattern Recognition | Abbreviated Journal | |
Volume | 709 | Issue | Pages | ||
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Area | Expedition | Conference | RTIP2R | ||
Notes | DAG | Approved | no | ||
Call Number | Admin @ si @ HTR2016 | Serial | 2956 | ||
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Author | Marta Diez-Ferrer; Debora Gil; Elena Carreño; Susana Padrones; Samantha Aso; Vanesa Vicens; Cubero Noelia; Rosa Lopez Lisbona; Carles Sanchez; Agnes Borras; Antoni Rosell | ||||
Title | Positive Airway Pressure-Enhanced CT to Improve Virtual Bronchoscopic Navigation | Type | Journal Article | ||
Year | 2016 | Publication | Chest Journal | Abbreviated Journal | CHEST |
Volume | 150 | Issue | 4 | Pages | 1003A |
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Notes | IAM; 600.096; 600.075 | Approved | no | ||
Call Number | Admin @ si @ DGC2016 | Serial | 3099 | ||
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Author | Joana Maria Pujadas-Mora; Alicia Fornes; Josep Llados; Anna Cabre | ||||
Title | Bridging the gap between historical demography and computing: tools for computer-assisted transcription and the analysis of demographic sources | Type | Book Chapter | ||
Year | 2016 | Publication | The future of historical demography. Upside down and inside out | Abbreviated Journal | |
Volume | Issue | Pages | 127-131 | ||
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Publisher | Acco Publishers | Place of Publication | Editor | K.Matthijs; S.Hin; H.Matsuo; J.Kok | |
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ISSN | ISBN | 978-94-6292-722-3 | Medium | ||
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Notes | DAG; 600.097 | Approved | no | ||
Call Number | Admin @ si @ PFL2016 | Serial | 2907 | ||
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Author | Victor Ponce | ||||
Title | Evolutionary Bags of Space-Time Features for Human Analysis | Type | Book Whole | ||
Year | 2016 | Publication | PhD Thesis Universitat de Barcelona, UOC and CVC | Abbreviated Journal | |
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Keywords | Computer algorithms; Digital image processing; Digital video; Analysis of variance; Dynamic programming; Evolutionary computation; Gesture | ||||
Abstract | The representation (or feature) learning has been an emerging concept in the last years, since it collects a set of techniques that are present in any theoretical or practical methodology referring to artificial intelligence. In computer vision, a very common representation has adopted the form of the well-known Bag of Visual Words. This representation appears implicitly in most approaches where images are described, and is also present in a huge number of areas and domains: image content retrieval, pedestrian detection, human-computer interaction, surveillance, e-health, and social computing, amongst others. The early stages of this dissertation provide an approach for learning visual representations inside evolutionary algorithms, which consists of evolving weighting schemes to improve the BoVW representations for the task of recognizing categories of videos and images. Thus, we demonstrate the applicability of the most common weighting schemes, which are often used in text mining but are less frequently found in computer vision tasks. Beyond learning these visual representations, we provide an approach based on fusion strategies for learning spatiotemporal representations, from multimodal data obtained by depth sensors. Besides, we specially aim at the evolutionary and dynamic modelling, where the temporal factor is present in the nature of the data, such as video sequences of gestures and actions. Indeed, we explore the effects of probabilistic modelling for those approaches based on dynamic programming, so as to handle the temporal deformation and variance amongst video sequences of different categories. Finally, we integrate dynamic programming and generative models into an evolutionary computation framework, with the aim of learning Bags of SubGestures (BoSG) representations and hence to improve the generalization capability of standard gesture recognition approaches. The results obtained in the experimentation demonstrate, first, that evolutionary algorithms are useful for improving the representation of BoVW approaches in several datasets for recognizing categories in still images and video sequences. On the other hand, our experimentation reveals that both, the use of dynamic programming and generative models to align video sequences, and the representations obtained from applying fusion strategies in multimodal data, entail an enhancement on the performance when recognizing some gesture categories. Furthermore, the combination of evolutionary algorithms with models based on dynamic programming and generative approaches results, when aiming at the classification of video categories on large video datasets, in a considerable improvement over standard gesture and action recognition approaches. Finally, we demonstrate the applications of these representations in several domains for human analysis: classification of images where humans may be present, action and gesture recognition for general applications, and in particular for conversational settings within the field of restorative justice | ||||
Address | June 2016 | ||||
Corporate Author | Thesis | Ph.D. thesis | |||
Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Sergio Escalera;Xavier Baro;Hugo Jair Escalante | |
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Notes | HuPBA | Approved | no | ||
Call Number | Pon2016 | Serial | 2814 | ||
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Author | German Ros | ||||
Title | Visual Scene Understanding for Autonomous Vehicles: Understanding Where and What | Type | Book Whole | ||
Year | 2016 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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Abstract | Making Ground Autonomous Vehicles (GAVs) a reality as a service for the society is one of the major scientific and technological challenges of this century. The potential benefits of autonomous vehicles include reducing accidents, improving traffic congestion and better usage of road infrastructures, among others. These vehicles must operate in our cities, towns and highways, dealing with many different types of situations while respecting traffic rules and protecting human lives. GAVs are expected to deal with all types of scenarios and situations, coping with an uncertain and chaotic world.
Therefore, in order to fulfill these demanding requirements GAVs need to be endowed with the capability of understanding their surrounding at many different levels, by means of affordable sensors and artificial intelligence. This capacity to understand the surroundings and the current situation that the vehicle is involved in is called scene understanding. In this work we investigate novel techniques to bring scene understanding to autonomous vehicles by combining the use of cameras as the main source of information—due to their versatility and affordability—and algorithms based on computer vision and machine learning. We investigate different degrees of understanding of the scene, starting from basic geometric knowledge about where is the vehicle within the scene. A robust and efficient estimation of the vehicle location and pose with respect to a map is one of the most fundamental steps towards autonomous driving. We study this problem from the point of view of robustness and computational efficiency, proposing key insights to improve current solutions. Then we advance to higher levels of abstraction to discover what is in the scene, by recognizing and parsing all the elements present on a driving scene, such as roads, sidewalks, pedestrians, etc. We investigate this problem known as semantic segmentation, proposing new approaches to improve recognition accuracy and computational efficiency. We cover these points by focusing on key aspects such as: (i) how to leverage computation moving semantics to an offline process, (ii) how to train compact architectures based on deconvolutional networks to achieve their maximum potential, (iii) how to use virtual worlds in combination with domain adaptation to produce accurate models in a cost-effective fashion, and (iv) how to use transfer learning techniques to prepare models to new situations. We finally extend the previous level of knowledge enabling systems to reasoning about what has change in a scene with respect to a previous visit, which in return allows for efficient and cost-effective map updating. |
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Corporate Author | Thesis | Ph.D. thesis | |||
Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Angel Sappa;Julio Guerrero;Antonio Lopez | |
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ISSN | ISBN | 978-84-945373-1-8 | Medium | ||
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Notes | ADAS | Approved | no | ||
Call Number | Admin @ si @ Ros2016 | Serial | 2860 | ||
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Author | Francisco Cruz | ||||
Title | Probabilistic Graphical Models for Document Analysis | Type | Book Whole | ||
Year | 2016 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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Abstract | Latest advances in digitization techniques have fostered the interest in creating digital copies of collections of documents. Digitized documents permit an easy maintenance, loss-less storage, and efficient ways for transmission and to perform information retrieval processes. This situation has opened a new market niche to develop systems able to automatically extract and analyze information contained in these collections, specially in the ambit of the business activity.
Due to the great variety of types of documents this is not a trivial task. For instance, the automatic extraction of numerical data from invoices differs substantially from a task of text recognition in historical documents. However, in order to extract the information of interest, is always necessary to identify the area of the document where it is located. In the area of Document Analysis we refer to this process as layout analysis, which aims at identifying and categorizing the different entities that compose the document, such as text regions, pictures, text lines, or tables, among others. To perform this task it is usually necessary to incorporate a prior knowledge about the task into the analysis process, which can be modeled by defining a set of contextual relations between the different entities of the document. The use of context has proven to be useful to reinforce the recognition process and improve the results on many computer vision tasks. It presents two fundamental questions: What kind of contextual information is appropriate for a given task, and how to incorporate this information into the models. In this thesis we study several ways to incorporate contextual information to the task of document layout analysis, and to the particular case of handwritten text line segmentation. We focus on the study of Probabilistic Graphical Models and other mechanisms for this purpose, and propose several solutions to these problems. First, we present a method for layout analysis based on Conditional Random Fields. With this model we encode local contextual relations between variables, such as pair-wise constraints. Besides, we encode a set of structural relations between different classes of regions at feature level. Second, we present a method based on 2D-Probabilistic Context-free Grammars to encode structural and hierarchical relations. We perform a comparative study between Probabilistic Graphical Models and this syntactic approach. Third, we propose a method for structured documents based on Bayesian Networks to represent the document structure, and an algorithm based in the Expectation-Maximization to find the best configuration of the page. We perform a thorough evaluation of the proposed methods on two particular collections of documents: a historical collection composed of ancient structured documents, and a collection of contemporary documents. In addition, we present a general method for the task of handwritten text line segmentation. We define a probabilistic framework where we combine the EM algorithm with variational approaches for computing inference and parameter learning on a Markov Random Field. We evaluate our method on several collections of documents, including a general dataset of annotated administrative documents. Results demonstrate the applicability of our method to real problems, and the contribution of the use of contextual information to this kind of problems. |
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Corporate Author | Thesis | Ph.D. thesis | |||
Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Oriol Ramos Terrades | |
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ISSN | ISBN | 978-84-945373-2-5 | Medium | ||
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Notes | DAG | Approved | no | ||
Call Number | Admin @ si @ Cru2016 | Serial | 2861 | ||
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Author | Egils Avots; M. Daneshmanda; Andres Traumann; Sergio Escalera; G. Anbarjafaria | ||||
Title | Automatic garment retexturing based on infrared information | Type | Journal Article | ||
Year | 2016 | Publication | Computers & Graphics | Abbreviated Journal | CG |
Volume | 59 | Issue | Pages | 28-38 | |
Keywords | Garment Retexturing; Texture Mapping; Infrared Images; RGB-D Acquisition Devices; Shading | ||||
Abstract | This paper introduces a new automatic technique for garment retexturing using a single static image along with the depth and infrared information obtained using the Microsoft Kinect II as the RGB-D acquisition device. First, the garment is segmented out from the image using either the Breadth-First Search algorithm or the semi-automatic procedure provided by the GrabCut method. Then texture domain coordinates are computed for each pixel belonging to the garment using normalised 3D information. Afterwards, shading is applied to the new colours from the texture image. As the main contribution of the proposed method, the latter information is obtained based on extracting a linear map transforming the colour present on the infrared image to that of the RGB colour channels. One of the most important impacts of this strategy is that the resulting retexturing algorithm is colour-, pattern- and lighting-invariant. The experimental results show that it can be used to produce realistic representations, which is substantiated through implementing it under various experimentation scenarios, involving varying lighting intensities and directions. Successful results are accomplished also on video sequences, as well as on images of subjects taking different poses. Based on the Mean Opinion Score analysis conducted on many randomly chosen users, it has been shown to produce more realistic-looking results compared to the existing state-of-the-art methods suggested in the literature. From a wide perspective, the proposed method can be used for retexturing all sorts of segmented surfaces, although the focus of this study is on garment retexturing, and the investigation of the configurations is steered accordingly, since the experiments target an application in the context of virtual fitting rooms. | ||||
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Publisher | Elsevier | Place of Publication | Editor | ||
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Notes | HuPBA;MILAB; | Approved | no | ||
Call Number | Admin @ si @ ADT2016 | Serial | 2759 | ||
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Author | Simone Balocco; Maria Zuluaga; Guillaume Zahnd; Su-Lin Lee; Stefanie Demirci | ||||
Title | Computing and Visualization for Intravascular Imaging and Computer Assisted Stenting | Type | Book Whole | ||
Year | 2016 | Publication | Computing and Visualization for Intravascular Imaging and Computer-Assisted Stenting | Abbreviated Journal | |
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Publisher | Elsevier | Place of Publication | Editor | ||
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ISSN | ISBN | 9780128110188 | Medium | ||
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Notes | MILAB | Approved | no | ||
Call Number | Admin @ si @ BZZ2016 | Serial | 2821 | ||
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Author | Pedro Martins; Paulo Carvalho; Carlo Gatta | ||||
Title | On the completeness of feature-driven maximally stable extremal regions | Type | Journal Article | ||
Year | 2016 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL |
Volume | 74 | Issue | Pages | 9-16 | |
Keywords | Local features; Completeness; Maximally Stable Extremal Regions | ||||
Abstract | By definition, local image features provide a compact representation of the image in which most of the image information is preserved. This capability offered by local features has been overlooked, despite being relevant in many application scenarios. In this paper, we analyze and discuss the performance of feature-driven Maximally Stable Extremal Regions (MSER) in terms of the coverage of informative image parts (completeness). This type of features results from an MSER extraction on saliency maps in which features related to objects boundaries or even symmetry axes are highlighted. These maps are intended to be suitable domains for MSER detection, allowing this detector to provide a better coverage of informative image parts. Our experimental results, which were based on a large-scale evaluation, show that feature-driven MSER have relatively high completeness values and provide more complete sets than a traditional MSER detection even when sets of similar cardinality are considered. | ||||
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Publisher | Elsevier B.V. | Place of Publication | Editor | ||
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ISSN | 0167-8655 | ISBN | Medium | ||
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Notes | LAMP;MILAB; | Approved | no | ||
Call Number | Admin @ si @ MCG2016 | Serial | 2748 | ||
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Author | Gerard Canal; Sergio Escalera; Cecilio Angulo | ||||
Title | A Real-time Human-Robot Interaction system based on gestures for assistive scenarios | Type | Journal Article | ||
Year | 2016 | Publication | Computer Vision and Image Understanding | Abbreviated Journal | CVIU |
Volume | 149 | Issue | Pages | 65-77 | |
Keywords | Gesture recognition; Human Robot Interaction; Dynamic Time Warping; Pointing location estimation | ||||
Abstract | Natural and intuitive human interaction with robotic systems is a key point to develop robots assisting people in an easy and effective way. In this paper, a Human Robot Interaction (HRI) system able to recognize gestures usually employed in human non-verbal communication is introduced, and an in-depth study of its usability is performed. The system deals with dynamic gestures such as waving or nodding which are recognized using a Dynamic Time Warping approach based on gesture specific features computed from depth maps. A static gesture consisting in pointing at an object is also recognized. The pointed location is then estimated in order to detect candidate objects the user may refer to. When the pointed object is unclear for the robot, a disambiguation procedure by means of either a verbal or gestural dialogue is performed. This skill would lead to the robot picking an object in behalf of the user, which could present difficulties to do it by itself. The overall system — which is composed by a NAO and Wifibot robots, a KinectTM v2 sensor and two laptops — is firstly evaluated in a structured lab setup. Then, a broad set of user tests has been completed, which allows to assess correct performance in terms of recognition rates, easiness of use and response times. | ||||
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Publisher | Elsevier B.V. | Place of Publication | Editor | ||
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Notes | HuPBA;MILAB; | Approved | no | ||
Call Number | Admin @ si @ CEA2016 | Serial | 2768 | ||
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Author | Miguel Oliveira; Victor Santos; Angel Sappa; P. Dias; A. Moreira | ||||
Title | Incremental Scenario Representations for Autonomous Driving using Geometric Polygonal Primitives | Type | Journal Article | ||
Year | 2016 | Publication | Robotics and Autonomous Systems | Abbreviated Journal | RAS |
Volume | 83 | Issue | Pages | 312-325 | |
Keywords | Incremental scene reconstruction; Point clouds; Autonomous vehicles; Polygonal primitives | ||||
Abstract | When an autonomous vehicle is traveling through some scenario it receives a continuous stream of sensor data. This sensor data arrives in an asynchronous fashion and often contains overlapping or redundant information. Thus, it is not trivial how a representation of the environment observed by the vehicle can be created and updated over time. This paper presents a novel methodology to compute an incremental 3D representation of a scenario from 3D range measurements. We propose to use macro scale polygonal primitives to model the scenario. This means that the representation of the scene is given as a list of large scale polygons that describe the geometric structure of the environment. Furthermore, we propose mechanisms designed to update the geometric polygonal primitives over time whenever fresh sensor data is collected. Results show that the approach is capable of producing accurate descriptions of the scene, and that it is computationally very efficient when compared to other reconstruction techniques. | ||||
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Publisher | Elsevier B.V. | Place of Publication | Editor | ||
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Notes | ADAS; 600.086, 600.076 | Approved | no | ||
Call Number | Admin @ si @OSS2016a | Serial | 2806 | ||
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Author | Angel Sappa; Cristhian A. Aguilera-Carrasco; Juan A. Carvajal Ayala; Miguel Oliveira; Dennis Romero; Boris X. Vintimilla; Ricardo Toledo | ||||
Title | Monocular visual odometry: A cross-spectral image fusion based approach | Type | Journal Article | ||
Year | 2016 | Publication | Robotics and Autonomous Systems | Abbreviated Journal | RAS |
Volume | 85 | Issue | Pages | 26-36 | |
Keywords | Monocular visual odometry; LWIR-RGB cross-spectral imaging; Image fusion | ||||
Abstract | This manuscript evaluates the usage of fused cross-spectral images in a monocular visual odometry approach. Fused images are obtained through a Discrete Wavelet Transform (DWT) scheme, where the best setup is empirically obtained by means of a mutual information based evaluation metric. The objective is to have a flexible scheme where fusion parameters are adapted according to the characteristics of the given images. Visual odometry is computed from the fused monocular images using an off the shelf approach. Experimental results using data sets obtained with two different platforms are presented. Additionally, comparison with a previous approach as well as with monocular-visible/infrared spectra are also provided showing the advantages of the proposed scheme. | ||||
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Publisher | Elsevier B.V. | Place of Publication | Editor | ||
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Notes | ADAS;600.086; 600.076 | Approved | no | ||
Call Number | Admin @ si @SAC2016 | Serial | 2811 | ||
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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 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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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 | ||
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