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Author Jiaolong Xu; Sebastian Ramos; David Vazquez; Antonio Lopez
Title Incremental Domain Adaptation of Deformable Part-based Models Type Conference Article
Year 2014 Publication 25th British Machine Vision Conference Abbreviated Journal
Volume Issue Pages
Keywords Pedestrian Detection; Part-based models; Domain Adaptation
Abstract Nowadays, classifiers play a core role in many computer vision tasks. The underlying assumption for learning classifiers is that the training set and the deployment environment (testing) follow the same probability distribution regarding the features used by the classifiers. However, in practice, there are different reasons that can break this constancy assumption. Accordingly, reusing existing classifiers by adapting them from the previous training environment (source domain) to the new testing one (target domain)
is an approach with increasing acceptance in the computer vision community. In this paper we focus on the domain adaptation of deformable part-based models (DPMs) for object detection. In particular, we focus on a relatively unexplored scenario, i.e. incremental domain adaptation for object detection assuming weak-labeling. Therefore, our algorithm is ready to improve existing source-oriented DPM-based detectors as soon as a little amount of labeled target-domain training data is available, and keeps improving as more of such data arrives in a continuous fashion. For achieving this, we follow a multiple
instance learning (MIL) paradigm that operates in an incremental per-image basis. As proof of concept, we address the challenging scenario of adapting a DPM-based pedestrian detector trained with synthetic pedestrians to operate in real-world scenarios. The obtained results show that our incremental adaptive models obtain equally good accuracy results as the batch learned models, while being more flexible for handling continuously arriving target-domain data.
Address Nottingham; uk; September 2014
Corporate Author Thesis
Publisher BMVA Press Place of Publication Editor Valstar, Michel and French, Andrew and Pridmore, Tony
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN (up) ISBN Medium
Area Expedition Conference BMVC
Notes ADAS; 600.057; 600.054; 600.076 Approved no
Call Number XRV2014c; ADAS @ adas @ xrv2014c Serial 2455
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Author Sergio Vera; Debora Gil; Miguel Angel Gonzalez Ballester
Title Anatomical parameterization for volumetric meshing of the liver Type Conference Article
Year 2014 Publication SPIE – Medical Imaging Abbreviated Journal
Volume 9036 Issue Pages
Keywords Coordinate System; Anatomy Modeling; Parameterization
Abstract A coordinate system describing the interior of organs is a powerful tool for a systematic localization of injured tissue. If the same coordinate values are assigned to specific anatomical landmarks, the coordinate system allows integration of data across different medical image modalities. Harmonic mappings have been used to produce parametric coordinate systems over the surface of anatomical shapes, given their flexibility to set values
at specific locations through boundary conditions. However, most of the existing implementations in medical imaging restrict to either anatomical surfaces, or the depth coordinate with boundary conditions is given at sites
of limited geometric diversity. In this paper we present a method for anatomical volumetric parameterization that extends current harmonic parameterizations to the interior anatomy using information provided by the
volume medial surface. We have applied the methodology to define a common reference system for the liver shape and functional anatomy. This reference system sets a solid base for creating anatomical models of the patient’s liver, and allows comparing livers from several patients in a common framework of reference.
Address Amsterdam; September 2014
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 (up) ISBN Medium
Area Expedition Conference SPIE-MI
Notes IAM; 600.075 Approved no
Call Number Admin @ si @ VGG2014 Serial 2456
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Author Enric Marti; Antoni Gurgui; Debora Gil; Aura Hernandez-Sabate; Jaume Rocarias; Ferran Poveda
Title ABP on line: Seguimiento, estregas y evaluación en aprendizaje basado en proyectos Type Miscellaneous
Year 2014 Publication 8th International Congress on University Teaching and Innovation Abbreviated Journal
Volume Issue Pages
Keywords
Abstract
Address Tarragona; juliol 2014
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 (up) ISBN Medium
Area Expedition Conference CIDUI
Notes IAM; ADAS; 600.076; 600.063; 600.075 Approved no
Call Number Admin @ si @ MGG2014 Serial 2457
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Author Carles Sanchez; Oriol Ramos Terrades; Patricia Marquez; Enric Marti; Jaume Rocarias; Debora Gil
Title Evaluación automática de prácticas en Moodle para el aprendizaje autónomo en Ingenierías Type Miscellaneous
Year 2014 Publication 8th International Congress on University Teaching and Innovation Abbreviated Journal
Volume Issue Pages
Keywords
Abstract
Address Tarragona; juliol 2014
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 (up) ISBN Medium
Area Expedition Conference CIDUI
Notes IAM; 600.075;DAG Approved no
Call Number Admin @ si @ SRM2014 Serial 2458
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Author David Fernandez; R.Manmatha; Josep Llados; Alicia Fornes
Title Sequential Word Spotting in Historical Handwritten Documents Type Conference Article
Year 2014 Publication 11th IAPR International Workshop on Document Analysis and Systems Abbreviated Journal
Volume Issue Pages 101 - 105
Keywords
Abstract In this work we present a handwritten word spotting approach that takes advantage of the a priori known order of appearance of the query words. Given an ordered sequence of query word instances, the proposed approach performs a
sequence alignment with the words in the target collection. Although the alignment is quite sparse, i.e. the number of words in the database is higher than the query set, the improvement in the overall performance is sensitively higher than isolated word spotting. As application dataset, we use a collection of handwritten marriage licenses taking advantage of the ordered
index pages of family names.
Address Tours; Francia; April 2014
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 (up) ISBN 978-1-4799-3243-6 Medium
Area Expedition Conference DAS
Notes DAG; 600.061; 600.056; 602.006; 600.077 Approved no
Call Number Admin @ si @ FML2014 Serial 2462
Permanent link to this record
 
 
Author Monica Piñol
Title Reinforcement Learning of Visual Descriptors for Object Recognition Type Book Whole
Year 2014 Publication PhD Thesis, Universitat Autonoma de Barcelona-CVC Abbreviated Journal
Volume Issue Pages
Keywords
Abstract The human visual system is able to recognize the object in an image even if the object is partially occluded, from various points of view, in different colors, or with independence of the distance to the object. To do this, the eye obtains an image and extracts features that are sent to the brain, and then, in the brain the object is recognized. In computer vision, the object recognition branch tries to learns from the human visual system behaviour to achieve its goal. Hence, an algorithm is used to identify representative features of the scene (detection), then another algorithm is used to describe these points (descriptor) and finally the extracted information is used for classifying the object in the scene. The selection of this set of algorithms is a very complicated task and thus, a very active research field. In this thesis we are focused on the selection/learning of the best descriptor for a given image. In the state of the art there are several descriptors but we do not know how to choose the best descriptor because depends on scenes that we will use (dataset) and the algorithm chosen to do the classification. We propose a framework based on reinforcement learning and bag of features to choose the best descriptor according to the given image. The system can analyse the behaviour of different learning algorithms and descriptor sets. Furthermore the proposed framework for improving the classification/recognition ratio can be used with minor changes in other computer vision fields, such as video retrieval.
Address
Corporate Author Thesis Ph.D. thesis
Publisher Ediciones Graficas Rey Place of Publication Editor Ricardo Toledo;Angel Sappa
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN (up) ISBN 978-84-940902-5-7 Medium
Area Expedition Conference
Notes ADAS; 600.076 Approved no
Call Number Admin @ si @ Piñ2014 Serial 2464
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Author Anjan Dutta
Title Inexact Subgraph Matching Applied to Symbol Spotting in Graphical Documents Type Book Whole
Year 2014 Publication PhD Thesis, Universitat Autonoma de Barcelona-CVC Abbreviated Journal
Volume Issue Pages
Keywords
Abstract There is a resurgence in the use of structural approaches in the usual object recognition and retrieval problem. Graph theory, in particular, graph matching plays a relevant role in that. Specifically, the detection of an object (or a part of that) in an image in terms of structural features can be formulated as a subgraph matching. Subgraph matching is a challenging task. Specially due to the presence of outliers most of the graph matching algorithms do not perform well in subgraph matching scenario. Also exact subgraph isomorphism has proven to be an NP-complete problem. So naturally, in graph matching community, there are lot of efforts addressing the problem of subgraph matching within suboptimal bound. Most of them work with approximate algorithms that try to get an inexact solution in estimated way. In addition, usual recognition must cope with distortion. Inexact graph matching consists in finding the best isomorphism under a similarity measure. Theoretically this thesis proposes algorithms for solving subgraph matching in an approximate and inexact way.
We consider the symbol spotting problem on graphical documents or line drawings from application point of view. This is a well known problem in the graphics recognition community. It can be further applied for indexing and classification of documents based on their contents. The structural nature of this kind of documents easily motivates one for giving a graph based representation. So the symbol spotting problem on graphical documents can be considered as a subgraph matching problem. The main challenges in this application domain is the noise and distortions that might come during the usage, digitalization and raster to vector conversion of those documents. Apart from that computer vision nowadays is not any more confined within a limited number of images. So dealing a huge number of images with graph based method is a further challenge.
In this thesis, on one hand, we have worked on efficient and robust graph representation to cope with the noise and distortions coming from documents. On the other hand, we have worked on different graph based methods and framework to solve the subgraph matching problem in a better approximated way, which can also deal with considerable number of images. Firstly, we propose a symbol spotting method by hashing serialized subgraphs. Graph serialization allows to create factorized substructures such as graph paths, which can be organized in hash tables depending on the structural similarities of the serialized subgraphs. The involvement of hashing techniques helps to reduce the search space substantially and speeds up the spotting procedure. Secondly, we introduce contextual similarities based on the walk based propagation on tensor product graph. These contextual similarities involve higher order information and more reliable than pairwise similarities. We use these higher order similarities to formulate subgraph matching as a node and edge selection problem in the tensor product graph. Thirdly, we propose near convex grouping to form near convex region adjacency graph which eliminates the limitations of traditional region adjacency graph representation for graphic recognition. Fourthly, we propose a hierarchical graph representation by simplifying/correcting the structural errors to create a hierarchical graph of the base graph. Later these hierarchical graph structures are matched with some graph matching methods. Apart from that, in this thesis we have provided an overall experimental comparison of all the methods and some of the state-of-the-art methods. Furthermore, some dataset models have also been proposed.
Address
Corporate Author Thesis Ph.D. thesis
Publisher Ediciones Graficas Rey Place of Publication Editor Josep Llados;Umapada Pal
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN (up) ISBN 978-84-940902-4-0 Medium
Area Expedition Conference
Notes DAG; 600.077 Approved no
Call Number Admin @ si @ Dut2014 Serial 2465
Permanent link to this record
 
 
Author Pedro Martins; Paulo Carvalho; Carlo Gatta
Title Context-aware features and robust image representations Type Journal Article
Year 2014 Publication Journal of Visual Communication and Image Representation Abbreviated Journal JVCIR
Volume 25 Issue 2 Pages 339-348
Keywords
Abstract Local image features are often used to efficiently represent image content. The limited number of types of features that a local feature extractor responds to might be insufficient to provide a robust image representation. To overcome this limitation, we propose a context-aware feature extraction formulated under an information theoretic framework. The algorithm does not respond to a specific type of features; the idea is to retrieve complementary features which are relevant within the image context. We empirically validate the method by investigating the repeatability, the completeness, and the complementarity of context-aware features on standard benchmarks. In a comparison with strictly local features, we show that our context-aware features produce more robust image representations. Furthermore, we study the complementarity between strictly local features and context-aware ones to produce an even more robust representation.
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 (up) ISBN Medium
Area Expedition Conference
Notes LAMP; 600.079;MILAB Approved no
Call Number Admin @ si @ MCG2014 Serial 2467
Permanent link to this record
 
 
Author Simeon Petkov; Xavier Carrillo; Petia Radeva; Carlo Gatta
Title Diaphragm border detection in coronary X-ray angiographies: New method and applications Type Journal Article
Year 2014 Publication Computerized Medical Imaging and Graphics Abbreviated Journal CMIG
Volume 38 Issue 4 Pages 296-305
Keywords
Abstract X-ray angiography is widely used in cardiac disease diagnosis during or prior to intravascular interventions. The diaphragm motion and the heart beating induce gray-level changes, which are one of the main obstacles in quantitative analysis of myocardial perfusion. In this paper we focus on detecting the diaphragm border in both single images or whole X-ray angiography sequences. We show that the proposed method outperforms state of the art approaches. We extend a previous publicly available data set, adding new ground truth data. We also compose another set of more challenging images, thus having two separate data sets of increasing difficulty. Finally, we show three applications of our method: (1) a strategy to reduce false positives in vessel enhanced images; (2) a digital diaphragm removal algorithm; (3) an improvement in Myocardial Blush Grade semi-automatic estimation.
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 (up) ISBN Medium
Area Expedition Conference
Notes MILAB; LAMP; 600.079 Approved no
Call Number Admin @ si @ PCR2014 Serial 2468
Permanent link to this record
 
 
Author Pierluigi Casale; Oriol Pujol; Petia Radeva
Title Approximate polytope ensemble for one-class classification Type Journal Article
Year 2014 Publication Pattern Recognition Abbreviated Journal PR
Volume 47 Issue 2 Pages 854-864
Keywords One-class classification; Convex hull; High-dimensionality; Random projections; Ensemble learning
Abstract In this work, a new one-class classification ensemble strategy called approximate polytope ensemble is presented. The main contribution of the paper is threefold. First, the geometrical concept of convex hull is used to define the boundary of the target class defining the problem. Expansions and contractions of this geometrical structure are introduced in order to avoid over-fitting. Second, the decision whether a point belongs to the convex hull model in high dimensional spaces is approximated by means of random projections and an ensemble decision process. Finally, a tiling strategy is proposed in order to model non-convex structures. Experimental results show that the proposed strategy is significantly better than state of the art one-class classification methods on over 200 datasets.
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 (up) ISBN Medium
Area Expedition Conference
Notes MILAB; 605.203 Approved no
Call Number Admin @ si @ CPR2014a Serial 2469
Permanent link to this record
 
 
Author Francesco Ciompi; Oriol Pujol; Petia Radeva
Title ECOC-DRF: Discriminative random fields based on error correcting output codes Type Journal Article
Year 2014 Publication Pattern Recognition Abbreviated Journal PR
Volume 47 Issue 6 Pages 2193-2204
Keywords Discriminative random fields; Error-correcting output codes; Multi-class classification; Graphical models
Abstract We present ECOC-DRF, a framework where potential functions for Discriminative Random Fields are formulated as an ensemble of classifiers. We introduce the label trick, a technique to express transitions in the pairwise potential as meta-classes. This allows to independently learn any possible transition between labels without assuming any pre-defined model. The Error Correcting Output Codes matrix is used as ensemble framework for the combination of margin classifiers. We apply ECOC-DRF to a large set of classification problems, covering synthetic, natural and medical images for binary and multi-class cases, outperforming state-of-the art in almost all the experiments.
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 (up) ISBN Medium
Area Expedition Conference
Notes LAMP; HuPBA; MILAB; 605.203; 600.046; 601.043; 600.079 Approved no
Call Number Admin @ si @ CPR2014b Serial 2470
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Author Adriana Romero; Petia Radeva; Carlo Gatta
Title No more meta-parameter tuning in unsupervised sparse feature learning Type Miscellaneous
Year 2014 Publication Arxiv Abbreviated Journal
Volume Issue Pages
Keywords
Abstract CoRR abs/1402.5766
We propose a meta-parameter free, off-the-shelf, simple and fast unsupervised feature learning algorithm, which exploits a new way of optimizing for sparsity. Experiments on STL-10 show that the method presents state-of-the-art performance and provides discriminative features that generalize well.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
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ISSN (up) ISBN Medium
Area Expedition Conference
Notes MILAB; LAMP; 600.079 Approved no
Call Number Admin @ si @ RRG2014 Serial 2471
Permanent link to this record
 
 
Author Monica Piñol; Angel Sappa; Ricardo Toledo
Title Adaptive Feature Descriptor Selection based on a Multi-Table Reinforcement Learning Strategy Type Journal Article
Year 2015 Publication Neurocomputing Abbreviated Journal NEUCOM
Volume 150 Issue A Pages 106–115
Keywords Reinforcement learning; Q-learning; Bag of features; Descriptors
Abstract This paper presents and evaluates a framework to improve the performance of visual object classification methods, which are based on the usage of image feature descriptors as inputs. The goal of the proposed framework is to learn the best descriptor for each image in a given database. This goal is reached by means of a reinforcement learning process using the minimum information. The visual classification system used to demonstrate the proposed framework is based on a bag of features scheme, and the reinforcement learning technique is implemented through the Q-learning approach. The behavior of the reinforcement learning with different state definitions is evaluated. Additionally, a method that combines all these states is formulated in order to select the optimal state. Finally, the chosen actions are obtained from the best set of image descriptors in the literature: PHOW, SIFT, C-SIFT, SURF and Spin. Experimental results using two public databases (ETH and COIL) are provided showing both the validity of the proposed approach and comparisons with state of the art. In all the cases the best results are obtained with the proposed approach.
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 (up) ISBN Medium
Area Expedition Conference
Notes ADAS; 600.055; 600.076 Approved no
Call Number Admin @ si @ PST2015 Serial 2473
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Author P. Ricaurte ; C. Chilan; Cristhian A. Aguilera-Carrasco; Boris X. Vintimilla; Angel Sappa
Title Feature Point Descriptors: Infrared and Visible Spectra Type Journal Article
Year 2014 Publication Sensors Abbreviated Journal SENS
Volume 14 Issue 2 Pages 3690-3701
Keywords
Abstract This manuscript evaluates the behavior of classical feature point descriptors when they are used in images from long-wave infrared spectral band and compare them with the results obtained in the visible spectrum. Robustness to changes in rotation, scaling, blur, and additive noise are analyzed using a state of the art framework. Experimental results using a cross-spectral outdoor image data set are presented and conclusions from these experiments are given.
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 (up) ISBN Medium
Area Expedition Conference
Notes ADAS;600.055; 600.076 Approved no
Call Number Admin @ si @ RCA2014a Serial 2474
Permanent link to this record
 
 
Author Jorge Bernal; Joan M. Nuñez; F. Javier Sanchez; Fernando Vilariño
Title Polyp Segmentation Method in Colonoscopy Videos by means of MSA-DOVA Energy Maps Calculation Type Conference Article
Year 2014 Publication 3rd MICCAI Workshop on Clinical Image-based Procedures: Translational Research in Medical Imaging Abbreviated Journal
Volume 8680 Issue Pages 41-49
Keywords Image segmentation; Polyps; Colonoscopy; Valley information; Energy maps
Abstract In this paper we present a novel polyp region segmentation method for colonoscopy videos. Our method uses valley information associated to polyp boundaries in order to provide an initial segmentation. This first segmentation is refined to eliminate boundary discontinuities caused by image artifacts or other elements of the scene. Experimental results over a publicly annotated database show that our method outperforms both general and specific segmentation methods by providing more accurate regions rich in polyp content. We also prove how image preprocessing is needed to improve final polyp region segmentation.
Address Boston; USA; September 2014
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 (up) ISBN Medium
Area Expedition Conference CLIP
Notes MV; 600.060; 600.044; 600.047;SIAI Approved no
Call Number Admin @ si @ BNS2014 Serial 2502
Permanent link to this record