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Author Clement Guerin; Christophe Rigaud; Karell Bertet; Jean-Christophe Burie; Arnaud Revel ; Jean-Marc Ogier
Title Réduction de l’espace de recherche pour les personnages de bandes dessinées Type Conference Article
Year 2014 Publication 19th National Congress Reconnaissance de Formes et l'Intelligence Artificielle Abbreviated Journal
Volume Issue Pages
Keywords contextual search; document analysis; comics characters
Abstract Les bandes dessinées représentent un patrimoine culturel important dans de nombreux pays et leur numérisation massive offre la possibilité d'effectuer des recherches dans le contenu des images. À ce jour, ce sont principalement les structures des pages et leurs contenus textuels qui ont été étudiés, peu de travaux portent sur le contenu graphique. Nous proposons de nous appuyer sur des éléments déjà étudiés tels que la position des cases et des bulles, pour réduire l'espace de recherche et localiser les personnages en fonction de la queue des bulles. L'évaluation de nos différentes contributions à partir de la base eBDtheque montre un taux de détection des queues de bulle de 81.2%, de localisation des personnages allant jusqu'à 85% et un gain d'espace de recherche de plus de 50%.
Address Rouen; Francia; July 2014
Corporate Author Thesis
Publisher Place of Publication Editor (up)
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference RFIA
Notes DAG; 600.077 Approved no
Call Number Admin @ si @ GRB2014 Serial 2480
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Author Christophe Rigaud; Clement Guerin
Title Localisation contextuelle des personnages de bandes dessinées Type Conference Article
Year 2014 Publication Colloque International Francophone sur l'Écrit et le Document Abbreviated Journal
Volume Issue Pages
Keywords
Abstract Les auteurs proposent une méthode de localisation des personnages dans des cases de bandes dessinées en s'appuyant sur les caractéristiques des bulles de dialogue. L'évaluation montre un taux de localisation des personnages allant jusqu'à 65%.
Address Nancy; Francia; March 2014
Corporate Author Thesis
Publisher Place of Publication Editor (up)
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference CIFED
Notes DAG; 600.077 Approved no
Call Number Admin @ si @ RiG2014 Serial 2481
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Author Jon Almazan; Albert Gordo; Alicia Fornes; Ernest Valveny
Title Word Spotting and Recognition with Embedded Attributes Type Journal Article
Year 2014 Publication IEEE Transactions on Pattern Analysis and Machine Intelligence Abbreviated Journal TPAMI
Volume 36 Issue 12 Pages 2552 - 2566
Keywords
Abstract This article addresses the problems of word spotting and word recognition on images. In word spotting, the goal is to find all instances of a query word in a dataset of images. In recognition, the goal is to recognize the content of the word image, usually aided by a dictionary or lexicon. We describe an approach in which both word images and text strings are embedded in a common vectorial subspace. This is achieved by a combination of label embedding and attributes learning, and a common subspace regression. In this subspace, images and strings that represent the same word are close together, allowing one to cast recognition and retrieval tasks as a nearest neighbor problem. Contrary to most other existing methods, our representation has a fixed length, is low dimensional, and is very fast to compute and, especially, to compare. We test our approach on four public datasets of both handwritten documents and natural images showing results comparable or better than the state-of-the-art on spotting and recognition tasks.
Address
Corporate Author Thesis
Publisher Place of Publication Editor (up)
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0162-8828 ISBN Medium
Area Expedition Conference
Notes DAG; 600.056; 600.045; 600.061; 602.006; 600.077 Approved no
Call Number Admin @ si @ AGF2014a Serial 2483
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Author Jon Almazan; Albert Gordo; Alicia Fornes; Ernest Valveny
Title Segmentation-free Word Spotting with Exemplar SVMs Type Journal Article
Year 2014 Publication Pattern Recognition Abbreviated Journal PR
Volume 47 Issue 12 Pages 3967–3978
Keywords Word spotting; Segmentation-free; Unsupervised learning; Reranking; Query expansion; Compression
Abstract In this paper we propose an unsupervised segmentation-free method for word spotting in document images. Documents are represented with a grid of HOG descriptors, and a sliding-window approach is used to locate the document regions that are most similar to the query. We use the Exemplar SVM framework to produce a better representation of the query in an unsupervised way. Then, we use a more discriminative representation based on Fisher Vector to rerank the best regions retrieved, and the most promising ones are used to expand the Exemplar SVM training set and improve the query representation. Finally, the document descriptors are precomputed and compressed with Product Quantization. This offers two advantages: first, a large number of documents can be kept in RAM memory at the same time. Second, the sliding window becomes significantly faster since distances between quantized HOG descriptors can be precomputed. Our results significantly outperform other segmentation-free methods in the literature, both in accuracy and in speed and memory usage.
Address
Corporate Author Thesis
Publisher Place of Publication Editor (up)
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes DAG; 600.045; 600.056; 600.061; 602.006; 600.077 Approved no
Call Number Admin @ si @ AGF2014b Serial 2485
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Author Carlo Gatta; Adriana Romero; Joost Van de Weijer
Title Unrolling loopy top-down semantic feedback in convolutional deep networks Type Conference Article
Year 2014 Publication Workshop on Deep Vision: Deep Learning for Computer Vision Abbreviated Journal
Volume Issue Pages 498-505
Keywords
Abstract In this paper, we propose a novel way to perform top-down semantic feedback in convolutional deep networks for efficient and accurate image parsing. We also show how to add global appearance/semantic features, which have shown to improve image parsing performance in state-of-the-art methods, and was not present in previous convolutional approaches. The proposed method is characterised by an efficient training and a sufficiently fast testing. We use the well known SIFTflow dataset to numerically show the advantages provided by our contributions, and to compare with state-of-the-art image parsing convolutional based approaches.
Address Columbus; Ohio; June 2014
Corporate Author Thesis
Publisher Place of Publication Editor (up)
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference CVPRW
Notes LAMP; MILAB; 601.160; 600.079 Approved no
Call Number Admin @ si @ GRW2014 Serial 2490
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Author Dimosthenis Karatzas; Sergi Robles; Lluis Gomez
Title An on-line platform for ground truthing and performance evaluation of text extraction systems Type Conference Article
Year 2014 Publication 11th IAPR International Workshop on Document Analysis and Systems Abbreviated Journal
Volume Issue Pages 242 - 246
Keywords
Abstract This paper presents a set of on-line software tools for creating ground truth and calculating performance evaluation metrics for text extraction tasks such as localization, segmentation and recognition. The platform supports the definition of comprehensive ground truth information at different text representation levels while it offers centralised management and quality control of the ground truthing effort. It implements a range of state of the art performance evaluation algorithms and offers functionality for the definition of evaluation scenarios, on-line calculation of various performance metrics and visualisation of the results. The
presented platform, which comprises the backbone of the ICDAR 2011 (challenge 1) and 2013 (challenges 1 and 2) Robust Reading competitions, is now made available for public use.
Address Tours; Francia; April 2014
Corporate Author Thesis
Publisher Place of Publication Editor (up)
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN 978-1-4799-3243-6 Medium
Area Expedition Conference DAS
Notes DAG; 600.056; 600.077 Approved no
Call Number Admin @ si @ KRG2014 Serial 2491
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Author Lluis Gomez; Dimosthenis Karatzas
Title MSER-based Real-Time Text Detection and Tracking Type Conference Article
Year 2014 Publication 22nd International Conference on Pattern Recognition Abbreviated Journal
Volume Issue Pages 3110 - 3115
Keywords
Abstract We present a hybrid algorithm for detection and tracking of text in natural scenes that goes beyond the fulldetection approaches in terms of time performance optimization.
A state-of-the-art scene text detection module based on Maximally Stable Extremal Regions (MSER) is used to detect text asynchronously, while on a separate thread detected text objects are tracked by MSER propagation. The cooperation of these two modules yields real time video processing at high frame rates even on low-resource devices.
Address Stockholm; August 2014
Corporate Author Thesis
Publisher Place of Publication Editor (up)
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1051-4651 ISBN Medium
Area Expedition Conference ICPR
Notes DAG; 600.056; 601.158; 601.197; 600.077 Approved no
Call Number Admin @ si @ GoK2014a Serial 2492
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Author Alejandro Tabas; Emili Balaguer-Ballester; Laura Igual
Title Spatial Discriminant ICA for RS-fMRI characterisation Type Conference Article
Year 2014 Publication 4th International Workshop on Pattern Recognition in Neuroimaging Abbreviated Journal
Volume Issue Pages 1-4
Keywords
Abstract Resting-State fMRI (RS-fMRI) is a brain imaging technique useful for exploring functional connectivity. A major point of interest in RS-fMRI analysis is to isolate connectivity patterns characterising disorders such as for instance ADHD. Such characterisation is usually performed in two steps: first, all connectivity patterns in the data are extracted by means of Independent Component Analysis (ICA); second, standard statistical tests are performed over the extracted patterns to find differences between control and clinical groups. In this work we introduce a novel, single-step, approach for this problem termed Spatial Discriminant ICA. The algorithm can efficiently isolate networks of functional connectivity characterising a clinical group by combining ICA and a new variant of the Fisher’s Linear Discriminant also introduced in this work. As the characterisation is carried out in a single step, it potentially provides for a richer characterisation of inter-class differences. The algorithm is tested using synthetic and real fMRI data, showing promising results in both experiments.
Address Tübingen; June 2014
Corporate Author Thesis
Publisher Place of Publication Editor (up)
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN 978-1-4799-4150-6 Medium
Area Expedition Conference PRNI
Notes OR;MILAB Approved no
Call Number Admin @ si @ TBI2014 Serial 2493
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Author Oualid M. Benkarim; Petia Radeva; Laura Igual
Title Label Consistent Multiclass Discriminative Dictionary Learning for MRI Segmentation Type Conference Article
Year 2014 Publication 8th Conference on Articulated Motion and Deformable Objects Abbreviated Journal
Volume 8563 Issue Pages 138-147
Keywords MRI segmentation; sparse representation; discriminative dic- tionary learning; multiclass classi cation
Abstract The automatic segmentation of multiple subcortical structures in brain Magnetic Resonance Images (MRI) still remains a challenging task. In this paper, we address this problem using sparse representation and discriminative dictionary learning, which have shown promising results in compression, image denoising and recently in MRI segmentation. Particularly, we use multiclass dictionaries learned from a set of brain atlases to simultaneously segment multiple subcortical structures.
We also impose dictionary atoms to be specialized in one given class using label consistent K-SVD, which can alleviate the bias produced by unbalanced libraries, present when dealing with small structures. The proposed method is compared with other state of the art approaches for the segmentation of the Basal Ganglia of 35 subjects of a public dataset.
The promising results of the segmentation method show the eciency of the multiclass discriminative dictionary learning algorithms in MRI segmentation problems.
Address Palma de Mallorca; July 2014
Corporate Author Thesis
Publisher Springer International Publishing Place of Publication Editor (up)
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN 0302-9743 ISBN 978-3-319-08848-8 Medium
Area Expedition Conference AMDO
Notes MILAB; OR Approved no
Call Number Admin @ si @ BRI2014 Serial 2494
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Author Patricia Marquez; H. Kause; A. Fuster; Aura Hernandez-Sabate; L. Florack; Debora Gil; Hans van Assen
Title Factors Affecting Optical Flow Performance in Tagging Magnetic Resonance Imaging Type Conference Article
Year 2014 Publication 17th International Conference on Medical Image Computing and Computer Assisted Intervention Abbreviated Journal
Volume 8896 Issue Pages 231-238
Keywords Optical flow; Performance Evaluation; Synthetic Database; ANOVA; Tagging Magnetic Resonance Imaging
Abstract Changes in cardiac deformation patterns are correlated with cardiac pathologies. Deformation can be extracted from tagging Magnetic Resonance Imaging (tMRI) using Optical Flow (OF) techniques. For applications of OF in a clinical setting it is important to assess to what extent the performance of a particular OF method is stable across di erent clinical acquisition artifacts. This paper presents a statistical validation framework, based on ANOVA, to assess the motion and appearance factors that have the largest in uence on OF accuracy drop.
In order to validate this framework, we created a database of simulated tMRI data including the most common artifacts of MRI and test three di erent OF methods, including HARP.
Address Boston; USA; September 2014
Corporate Author Thesis
Publisher Springer International Publishing Place of Publication Editor (up)
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN 0302-9743 ISBN 978-3-319-14677-5 Medium
Area Expedition Conference STACOM
Notes IAM; ADAS; 600.060; 601.145; 600.076; 600.075 Approved no
Call Number Admin @ si @ MKF2014 Serial 2495
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Author Hongxing Gao; Marçal Rusiñol; Dimosthenis Karatzas; Josep Llados
Title Fast Structural Matching for Document Image Retrieval through Spatial Databases Type Conference Article
Year 2014 Publication Document Recognition and Retrieval XXI Abbreviated Journal
Volume 9021 Issue Pages
Keywords Document image retrieval; distance transform; MSER; spatial database
Abstract The structure of document images plays a signi cant role in document analysis thus considerable e orts have been made towards extracting and understanding document structure, usually in the form of layout analysis approaches. In this paper, we rst employ Distance Transform based MSER (DTMSER) to eciently extract stable document structural elements in terms of a dendrogram of key-regions. Then a fast structural matching method is proposed to query the structure of document (dendrogram) based on a spatial database which facilitates the formulation of advanced spatial queries. The experiments demonstrate a signi cant improvement in a document retrieval scenario when compared to the use of typical Bag of Words (BoW) and pyramidal BoW descriptors.
Address Amsterdam; September 2014
Corporate Author Thesis
Publisher Place of Publication Editor (up)
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference SPIE-DRR
Notes DAG; 600.056; 600.061; 600.077 Approved no
Call Number Admin @ si @ GRK2014a Serial 2496
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Author Hongxing Gao; Marçal Rusiñol; Dimosthenis Karatzas; Josep Llados
Title Embedding Document Structure to Bag-of-Words through Pair-wise Stable Key-regions Type Conference Article
Year 2014 Publication 22nd International Conference on Pattern Recognition Abbreviated Journal
Volume Issue Pages 2903 - 2908
Keywords
Abstract Since the document structure carries valuable discriminative information, plenty of efforts have been made for extracting and understanding document structure among which layout analysis approaches are the most commonly used. In this paper, Distance Transform based MSER (DTMSER) is employed to efficiently extract the document structure as a dendrogram of key-regions which roughly correspond to structural elements such as characters, words and paragraphs. Inspired by the Bag
of Words (BoW) framework, we propose an efficient method for structural document matching by representing the document image as a histogram of key-region pairs encoding structural relationships.
Applied to the scenario of document image retrieval, experimental results demonstrate a remarkable improvement when comparing the proposed method with typical BoW and pyramidal BoW methods.
Address Stockholm; Sweden; August 2014
Corporate Author Thesis
Publisher Place of Publication Editor (up)
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference ICPR
Notes DAG; 600.056; 600.061; 600.077 Approved no
Call Number Admin @ si @ GRK2014b Serial 2497
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Author Joan Marc Llargues Asensio; Juan Peralta; Raul Arrabales; Manuel Gonzalez Bedia; Paulo Cortez; Antonio Lopez
Title Artificial Intelligence Approaches for the Generation and Assessment of Believable Human-Like Behaviour in Virtual Characters Type Journal Article
Year 2014 Publication Expert Systems With Applications Abbreviated Journal EXSY
Volume 41 Issue 16 Pages 7281–7290
Keywords Turing test; Human-like behaviour; Believability; Non-player characters; Cognitive architectures; Genetic algorithm; Artificial neural networks
Abstract Having artificial agents to autonomously produce human-like behaviour is one of the most ambitious original goals of Artificial Intelligence (AI) and remains an open problem nowadays. The imitation game originally proposed by Turing constitute a very effective method to prove the indistinguishability of an artificial agent. The behaviour of an agent is said to be indistinguishable from that of a human when observers (the so-called judges in the Turing test) cannot tell apart humans and non-human agents. Different environments, testing protocols, scopes and problem domains can be established to develop limited versions or variants of the original Turing test. In this paper we use a specific version of the Turing test, based on the international BotPrize competition, built in a First-Person Shooter video game, where both human players and non-player characters interact in complex virtual environments. Based on our past experience both in the BotPrize competition and other robotics and computer game AI applications we have developed three new more advanced controllers for believable agents: two based on a combination of the CERA–CRANIUM and SOAR cognitive architectures and other based on ADANN, a system for the automatic evolution and adaptation of artificial neural networks. These two new agents have been put to the test jointly with CCBot3, the winner of BotPrize 2010 competition (Arrabales et al., 2012), and have showed a significant improvement in the humanness ratio. Additionally, we have confronted all these bots to both First-person believability assessment (BotPrize original judging protocol) and Third-person believability assessment, demonstrating that the active involvement of the judge has a great impact in the recognition of human-like behaviour.
Address
Corporate Author Thesis
Publisher Place of Publication Editor (up)
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes ADAS; 600.055; 600.057; 600.076 Approved no
Call Number Admin @ si @ LPA2014 Serial 2500
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Author Jorge Bernal; Debora Gil; Carles Sanchez; F. Javier Sanchez
Title Discarding Non Informative Regions for Efficient Colonoscopy Image Analysis Type Conference Article
Year 2014 Publication 1st MICCAI Workshop on Computer-Assisted and Robotic Endoscopy Abbreviated Journal
Volume 8899 Issue Pages 1-10
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 Springer International Publishing Place of Publication Editor (up)
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN 0302-9743 ISBN 978-3-319-13409-3 Medium
Area Expedition Conference CARE
Notes MV; IAM; 600.044; 600.047; 600.060; 600.075 Approved no
Call Number Admin @ si @ BGS2014b Serial 2503
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Author Joan M. Nuñez; Jorge Bernal; Miquel Ferrer; Fernando Vilariño
Title Impact of Keypoint Detection on Graph-based Characterization of Blood Vessels in Colonoscopy Videos Type Conference Article
Year 2014 Publication CARE workshop Abbreviated Journal
Volume Issue Pages
Keywords Colonoscopy; Graph Matching; Biometrics; Vessel; Intersection
Abstract We explore the potential of the use of blood vessels as anatomical landmarks for developing image registration methods in colonoscopy images. An unequivocal representation of blood vessels could be used to guide follow-up methods to track lesions over different interventions. We propose a graph-based representation to characterize network structures, such as blood vessels, based on the use of intersections and endpoints. We present a study consisting of the assessment of the minimal performance a keypoint detector should achieve so that the structure can still be recognized. Experimental results prove that, even by achieving a loss of 35% of the keypoints, the descriptive power of the associated graphs to the vessel pattern is still high enough to recognize blood vessels.
Address Boston; USA; September 2014
Corporate Author Thesis
Publisher Place of Publication Editor (up)
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference CARE
Notes MV; DAG; 600.060; 600.047; 600.077;SIAI Approved no
Call Number Admin @ si @ NBF2014 Serial 2504
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