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Author David Fernandez; Pau Riba; Alicia Fornes; Josep Llados
Title On the Influence of Key Point Encoding for Handwritten Word Spotting Type Conference Article
Year 2014 Publication 14th International Conference on Frontiers in Handwriting Recognition Abbreviated Journal
Volume Issue Pages 476 - 481
Keywords Local descriptors; Interest points; Handwritten documents; Word spotting; Historical document analysis
Abstract In this paper we evaluate the influence of the selection of key points and the associated features in the performance of word spotting processes. In general, features can be extracted from a number of characteristic points like corners, contours, skeletons, maxima, minima, crossings, etc. A number of descriptors exist in the literature using different interest point detectors. But the intrinsic variability of handwriting vary strongly on the performance if the interest points are not stable enough. In this paper, we analyze the performance of different descriptors for local interest points. As benchmarking dataset we have used the Barcelona Marriage Database that contains handwritten records of marriages over five centuries.
Address Creete Island; Grecia; September 2014
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title (up) Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 2167-6445 ISBN 978-1-4799-4335-7 Medium
Area Expedition Conference ICFHR
Notes DAG; 600.056; 600.061; 602.006; 600.077 Approved no
Call Number Admin @ si @ FRF2014 Serial 2460
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Author David Fernandez; Jon Almazan; Nuria Cirera; Alicia Fornes; Josep Llados
Title BH2M: the Barcelona Historical Handwritten Marriages database Type Conference Article
Year 2014 Publication 22nd International Conference on Pattern Recognition Abbreviated Journal
Volume Issue Pages 256 - 261
Keywords
Abstract This paper presents an image database of historical handwritten marriages records stored in the archives of Barcelona cathedral, and the corresponding meta-data addressed to evaluate the performance of document analysis algorithms. The contribution of this paper is twofold. First, it presents a complete ground truth which covers the whole pipeline of handwriting
recognition research, from layout analysis to recognition and understanding. Second, it is the first dataset in the emerging area of genealogical document analysis, where documents are manuscripts pseudo-structured with specific lexicons and the interest is beyond pure transcriptions but context dependent.
Address Creete Island; Grecia; September 2014
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title (up) Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1051-4651 ISBN Medium
Area Expedition Conference ICPR
Notes DAG; 600.056; 600.061; 602.006; 600.077 Approved no
Call Number Admin @ si @ FAC2014 Serial 2461
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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 (up) Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 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
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Author Pau Riba; Jon Almazan; Alicia Fornes; David Fernandez; Ernest Valveny; Josep Llados
Title e-Crowds: a mobile platform for browsing and searching in historical demographyrelated manuscripts Type Conference Article
Year 2014 Publication 14th International Conference on Frontiers in Handwriting Recognition Abbreviated Journal
Volume Issue Pages 228 - 233
Keywords
Abstract This paper presents a prototype system running on portable devices for browsing and word searching through historical handwritten document collections. The platform adapts the paradigm of eBook reading, where the narrative is not necessarily sequential, but centered on the user actions. The novelty is to replace digitally born books by digitized historical manuscripts of marriage licenses, so document analysis tasks are required in the browser. With an active reading paradigm, the user can cast queries of people names, so he/she can implicitly follow genealogical links. In addition, the system allows combined searches: the user can refine a search by adding more words to search. As a second contribution, the retrieval functionality involves as a core technology a word spotting module with an unified approach, which allows combined query searches, and also two input modalities: query-by-example, and query-by-string.
Address Creete Island; Grecia; September 2014
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title (up) Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 2167-6445 ISBN 978-1-4799-4335-7 Medium
Area Expedition Conference ICFHR
Notes DAG; 600.056; 600.045; 600.061; 602.006; 600.077 Approved no
Call Number Admin @ si @ RAF2014 Serial 2463
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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 (up) Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 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 (up) Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN 978-84-940902-4-0 Medium
Area Expedition Conference
Notes DAG; 600.077 Approved no
Call Number Admin @ si @ Dut2014 Serial 2465
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Author Carlo Gatta; Francesco Ciompi
Title Stacked Sequential Scale-Space Taylor Context Type Journal Article
Year 2014 Publication IEEE Transactions on Pattern Analysis and Machine Intelligence Abbreviated Journal TPAMI
Volume 36 Issue 8 Pages 1694-1700
Keywords
Abstract We analyze sequential image labeling methods that sample the posterior label field in order to gather contextual information. We propose an effective method that extracts local Taylor coefficients from the posterior at different scales. Results show that our proposal outperforms state-of-the-art methods on MSRC-21, CAMVID, eTRIMS8 and KAIST2 data sets.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title (up) Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0162-8828 ISBN Medium
Area Expedition Conference
Notes LAMP; MILAB; 601.160; 600.079 Approved no
Call Number Admin @ si @ GaC2014 Serial 2466
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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 (up) Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes LAMP; 600.079;MILAB Approved no
Call Number Admin @ si @ MCG2014 Serial 2467
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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 (up) Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes MILAB; LAMP; 600.079 Approved no
Call Number Admin @ si @ PCR2014 Serial 2468
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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 (up) Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes MILAB; 605.203 Approved no
Call Number Admin @ si @ CPR2014a Serial 2469
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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 (up) Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 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 (up) Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes MILAB; LAMP; 600.079 Approved no
Call Number Admin @ si @ RRG2014 Serial 2471
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Author Naveen Onkarappa; Angel Sappa
Title Synthetic sequences and ground-truth flow field generation for algorithm validation Type Journal Article
Year 2015 Publication Multimedia Tools and Applications Abbreviated Journal MTAP
Volume 74 Issue 9 Pages 3121-3135
Keywords Ground-truth optical flow; Synthetic sequence; Algorithm validation
Abstract Research in computer vision is advancing by the availability of good datasets that help to improve algorithms, validate results and obtain comparative analysis. The datasets can be real or synthetic. For some of the computer vision problems such as optical flow it is not possible to obtain ground-truth optical flow with high accuracy in natural outdoor real scenarios directly by any sensor, although it is possible to obtain ground-truth data of real scenarios in a laboratory setup with limited motion. In this difficult situation computer graphics offers a viable option for creating realistic virtual scenarios. In the current work we present a framework to design virtual scenes and generate sequences as well as ground-truth flow fields. Particularly, we generate a dataset containing sequences of driving scenarios. The sequences in the dataset vary in different speeds of the on-board vision system, different road textures, complex motion of vehicle and independent moving vehicles in the scene. This dataset enables analyzing and adaptation of existing optical flow methods, and leads to invention of new approaches particularly for driver assistance systems.
Address
Corporate Author Thesis
Publisher Springer US Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title (up) Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1380-7501 ISBN Medium
Area Expedition Conference
Notes ADAS; 600.055; 601.215; 600.076 Approved no
Call Number Admin @ si @ OnS2014b Serial 2472
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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 (up) Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 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 (up) Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes ADAS;600.055; 600.076 Approved no
Call Number Admin @ si @ RCA2014a Serial 2474
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