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Author P. Ricaurte; C. Chilan; Cristhian A. Aguilera-Carrasco; Boris X. Vintimilla; Angel Sappa
Title Performance Evaluation of Feature Point Descriptors in the Infrared Domain Type Conference Article
Year 2014 Publication 9th International Conference on Computer Vision Theory and Applications Abbreviated Journal
Volume 1 Issue Pages 545-550
Keywords Infrared Imaging; Feature Point Descriptors
Abstract This paper presents a comparative evaluation of classical feature point descriptors when they are used in the long-wave infrared spectral band. Robustness to changes in rotation, scaling, blur, and additive noise are evaluated using a state of the art framework. Statistical results using an outdoor image data set are presented together with a discussion about the differences with respect to the results obtained when images from the visible spectrum are considered.
Address Lisboa; Portugal; January 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 ISBN Medium
Area Expedition Conference VISAPP
Notes ADAS; 600.055; 600.076 Approved no
Call Number (up) Admin @ si @ RCA2014b Serial 2476
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Author Marçal Rusiñol; J. Chazalon; Jean-Marc Ogier
Title Combining Focus Measure Operators to Predict OCR Accuracy in Mobile-Captured Document Images Type Conference Article
Year 2014 Publication 11th IAPR International Workshop on Document Analysis and Systems Abbreviated Journal
Volume Issue Pages 181 - 185
Keywords
Abstract Mobile document image acquisition is a new trend raising serious issues in business document processing workflows. Such digitization procedure is unreliable, and integrates many distortions which must be detected as soon as possible, on the mobile, to avoid paying data transmission fees, and losing information due to the inability to re-capture later a document with temporary availability. In this context, out-of-focus blur is major issue: users have no direct control over it, and it seriously degrades OCR recognition. In this paper, we concentrate on the estimation of focus quality, to ensure a sufficient legibility of a document image for OCR processing. We propose two contributions to improve OCR accuracy prediction for mobile-captured document images. First, we present 24 focus measures, never tested on document images, which are fast to compute and require no training. Second, we show that a combination of those measures enables state-of-the art performance regarding the correlation with OCR accuracy. The resulting approach is fast, robust, and easy to implement in a mobile device. Experiments are performed on a public dataset, and precise details about image processing are given.
Address Tours; France; 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 ISBN 978-1-4799-3243-6 Medium
Area Expedition Conference DAS
Notes DAG; 601.223; 600.077 Approved no
Call Number (up) Admin @ si @ RCO2014a Serial 2545
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Author Marçal Rusiñol; J. Chazalon; Jean-Marc Ogier
Title Normalisation et validation d'images de documents capturées en mobilité Type Conference Article
Year 2014 Publication Colloque International Francophone sur l'Écrit et le Document Abbreviated Journal
Volume Issue Pages 109-124
Keywords mobile document image acquisition; perspective correction; illumination correction; quality assessment; focus measure; OCR accuracy prediction
Abstract Mobile document image acquisition integrates many distortions which must be corrected or detected on the device, before the document becomes unavailable or paying data transmission fees. In this paper, we propose a system to correct perspective and illumination issues, and estimate the sharpness of the image for OCR recognition. The correction step relies on fast and accurate border detection followed by illumination normalization. Its evaluation on a private dataset shows a clear improvement on OCR accuracy. The quality assessment
step relies on a combination of focus measures. Its evaluation on a public dataset shows that this simple method compares well to state of the art, learning-based methods which cannot be embedded on a mobile, and outperforms metric-based methods.
Address Nancy; France; March 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 ISBN Medium
Area Expedition Conference CIFED
Notes DAG; 601.223; 600.077 Approved no
Call Number (up) Admin @ si @ RCO2014b Serial 2546
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Author Marçal Rusiñol; Volkmar Frinken; Dimosthenis Karatzas; Andrew Bagdanov; Josep Llados
Title Multimodal page classification in administrative document image streams Type Journal Article
Year 2014 Publication International Journal on Document Analysis and Recognition Abbreviated Journal IJDAR
Volume 17 Issue 4 Pages 331-341
Keywords Digital mail room; Multimodal page classification; Visual and textual document description
Abstract In this paper, we present a page classification application in a banking workflow. The proposed architecture represents administrative document images by merging visual and textual descriptions. The visual description is based on a hierarchical representation of the pixel intensity distribution. The textual description uses latent semantic analysis to represent document content as a mixture of topics. Several off-the-shelf classifiers and different strategies for combining visual and textual cues have been evaluated. A final step uses an n-gram model of the page stream allowing a finer-grained classification of pages. The proposed method has been tested in a real large-scale environment and we report results on a dataset of 70,000 pages.
Address
Corporate Author Thesis
Publisher Springer Berlin Heidelberg Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1433-2833 ISBN Medium
Area Expedition Conference
Notes DAG; LAMP; 600.056; 600.061; 601.240; 601.223; 600.077; 600.079 Approved no
Call Number (up) Admin @ si @ RFK2014 Serial 2523
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Author Adriana Romero; Carlo Gatta; Gustavo Camps-Valls
Title Unsupervised Deep Feature Extraction Of Hyperspectral Images Type Conference Article
Year 2014 Publication 6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing Abbreviated Journal
Volume Issue Pages
Keywords Convolutional networks; deep learning; sparse learning; feature extraction; hyperspectral image classification
Abstract This paper presents an effective unsupervised sparse feature learning algorithm to train deep convolutional networks on hyperspectral images. Deep convolutional hierarchical representations are learned and then used for pixel classification. Features in lower layers present less abstract representations of data, while higher layers represent more abstract and complex characteristics. We successfully illustrate the performance of the extracted representations in a challenging AVIRIS hyperspectral image classification problem, compared to standard dimensionality reduction methods like principal component analysis (PCA) and its kernel counterpart (kPCA). The proposed method largely outperforms the previous state-ofthe-art results on the same experimental setting. Results show that single layer networks can extract powerful discriminative features only when the receptive field accounts for neighboring pixels. Regarding the deep architecture, we can conclude that: (1) additional layers in a deep architecture significantly improve the performance w.r.t. single layer variants; (2) the max-pooling step in each layer is mandatory to achieve satisfactory results; and (3) the performance gain w.r.t. the number of layers is upper bounded, since the spatial resolution is reduced at each pooling, resulting in too spatially coarse output features.
Address Lausanne; Switzerland; June 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 ISBN Medium
Area Expedition Conference WHISPERS
Notes MILAB; LAMP; 600.079 Approved no
Call Number (up) Admin @ si @ RGC2014 Serial 2513
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Author Marçal Rusiñol; Lluis Pere de las Heras; Oriol Ramos Terrades
Title Flowchart Recognition for Non-Textual Information Retrieval in Patent Search Type Journal Article
Year 2014 Publication Information Retrieval Abbreviated Journal IR
Volume 17 Issue 5-6 Pages 545-562
Keywords Flowchart recognition; Patent documents; Text/graphics separation; Raster-to-vector conversion; Symbol recognition
Abstract Relatively little research has been done on the topic of patent image retrieval and in general in most of the approaches the retrieval is performed in terms of a similarity measure between the query image and the images in the corpus. However, systems aimed at overcoming the semantic gap between the visual description of patent images and their conveyed concepts would be very helpful for patent professionals. In this paper we present a flowchart recognition method aimed at achieving a structured representation of flowchart images that can be further queried semantically. The proposed method was submitted to the CLEF-IP 2012 flowchart recognition task. We report the obtained results on this dataset.
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 1386-4564 ISBN Medium
Area Expedition Conference
Notes DAG; 600.077 Approved no
Call Number (up) Admin @ si @ RHR2013 Serial 2342
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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
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 (up) Admin @ si @ RiG2014 Serial 2481
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Author Christophe Rigaud; Dimosthenis Karatzas; Jean-Christophe Burie; Jean-Marc Ogier
Title Color descriptor for content-based drawing retrieval Type Conference Article
Year 2014 Publication 11th IAPR International Workshop on Document Analysis and Systems Abbreviated Journal
Volume Issue Pages 267 - 271
Keywords
Abstract Human detection in computer vision field is an active field of research. Extending this to human-like drawings such as the main characters in comic book stories is not trivial. Comics analysis is a very recent field of research at the intersection of graphics, texts, objects and people recognition. The detection of the main comic characters is an essential step towards a fully automatic comic book understanding. This paper presents a color-based approach for comics character retrieval using content-based drawing retrieval and color palette.
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 ISBN 978-1-4799-3243-6 Medium
Area Expedition Conference DAS
Notes DAG; 600.056; 600.077 Approved no
Call Number (up) Admin @ si @ RKB2014 Serial 2479
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Author Marçal Rusiñol; Dimosthenis Karatzas; Josep Llados
Title Spotting Graphical Symbols in Camera-Acquired Documents in Real Time Type Book Chapter
Year 2014 Publication Graphics Recognition. Current Trends and Challenges Abbreviated Journal
Volume 8746 Issue Pages 3-10
Keywords
Abstract In this paper we present a system devoted to spot graphical symbols in camera-acquired document images. The system is based on the extraction and further matching of ORB compact local features computed over interest key-points. Then, the FLANN indexing framework based on approximate nearest neighbor search allows to efficiently match local descriptors between the captured scene and the graphical models. Finally, the RANSAC algorithm is used in order to compute the homography between the spotted symbol and its appearance in the document image. The proposed approach is efficient and is able to work in real time.
Address
Corporate Author Thesis
Publisher Springer Berlin Heidelberg Place of Publication Editor Bart Lamiroy; Jean-Marc Ogier
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN 0302-9743 ISBN 978-3-662-44853-3 Medium
Area Expedition Conference
Notes DAG; 600.045; 600.055; 600.061; 600.077 Approved no
Call Number (up) Admin @ si @ RKL2014 Serial 2700
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Author Marçal Rusiñol; V. Poulain d'Andecy; Dimosthenis Karatzas; Josep Llados
Title Classification of Administrative Document Images by Logo Identification Type Book Chapter
Year 2014 Publication Graphics Recognition. Current Trends and Challenges Abbreviated Journal
Volume 8746 Issue Pages 49-58
Keywords Administrative Document Classification; Logo Recognition; Logo Spotting
Abstract This paper is focused on the categorization of administrative document images (such as invoices) based on the recognition of the supplier’s graphical logo. Two different methods are proposed, the first one uses a bag-of-visual-words model whereas the second one tries to locate logo images described by the blurred shape model descriptor within documents by a sliding-window technique. Preliminar results are reported with a dataset of real administrative documents.
Address
Corporate Author Thesis
Publisher Springer Berlin Heidelberg Place of Publication Editor Bart Lamiroy; Jean-Marc Ogier
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0302-9743 ISBN 978-3-662-44853-3 Medium
Area Expedition Conference
Notes DAG; 600.056; 600.045; 605.203; 600.077 Approved no
Call Number (up) Admin @ si @ RPK2014 Serial 2701
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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
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes MILAB; LAMP; 600.079 Approved no
Call Number (up) Admin @ si @ RRG2014 Serial 2471
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Author Marçal Rusiñol; Josep Llados
Title Boosting the Handwritten Word Spotting Experience by Including the User in the Loop Type Journal Article
Year 2014 Publication Pattern Recognition Abbreviated Journal PR
Volume 47 Issue 3 Pages 1063–1072
Keywords Handwritten word spotting; Query by example; Relevance feedback; Query fusion; Multidimensional scaling
Abstract In this paper, we study the effect of taking the user into account in a query-by-example handwritten word spotting framework. Several off-the-shelf query fusion and relevance feedback strategies have been tested in the handwritten word spotting context. The increase in terms of precision when the user is included in the loop is assessed using two datasets of historical handwritten documents and two baseline word spotting approaches both based on the bag-of-visual-words model. We finally present two alternative ways of presenting the results to the user that might be more attractive and suitable to the user's needs than the classic ranked list.
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 0031-3203 ISBN Medium
Area Expedition Conference
Notes DAG; 600.045; 600.061; 600.077 Approved no
Call Number (up) Admin @ si @ RuL2013 Serial 2343
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Author Adria Ruiz; Joost Van de Weijer; Xavier Binefa
Title Regularized Multi-Concept MIL for weakly-supervised facial behavior categorization Type Conference Article
Year 2014 Publication 25th British Machine Vision Conference Abbreviated Journal
Volume Issue Pages
Keywords
Abstract We address the problem of estimating high-level semantic labels for videos of recorded people by means of analysing their facial expressions. This problem, to which we refer as facial behavior categorization, is a weakly-supervised learning problem where we do not have access to frame-by-frame facial gesture annotations but only weak-labels at the video level are available. Therefore, the goal is to learn a set of discriminative expressions and how they determine the video weak-labels. Facial behavior categorization can be posed as a Multi-Instance-Learning (MIL) problem and we propose a novel MIL method called Regularized Multi-Concept MIL to solve it. In contrast to previous approaches applied in facial behavior analysis, RMC-MIL follows a Multi-Concept assumption which allows different facial expressions (concepts) to contribute differently to the video-label. Moreover, to handle with the high-dimensional nature of facial-descriptors, RMC-MIL uses a discriminative approach to model the concepts and structured sparsity regularization to discard non-informative features. RMC-MIL is posed as a convex-constrained optimization problem where all the parameters are jointly learned using the Projected-Quasi-Newton method. In our experiments, we use two public data-sets to show the advantages of the Regularized Multi-Concept approach and its improvement compared to existing MIL methods. RMC-MIL outperforms state-of-the-art results in the UNBC data-set for pain detection.
Address Nottingham; UK; 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 ISBN Medium
Area Expedition Conference BMVC
Notes LAMP; CIC; 600.074; 600.079 Approved no
Call Number (up) Admin @ si @ RWB2014 Serial 2508
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Author Carles Sanchez
Title Tracheal Structure Characterization using Geometric and Appearance Models for Efficient Assessment of Stenosis in Videobronchoscopy Type Book Whole
Year 2014 Publication PhD Thesis, Universitat Autonoma de Barcelona-CVC Abbreviated Journal
Volume Issue Pages
Keywords
Abstract Recent advances in endoscopic devices have increased their use for minimal invasive diagnostic and intervention procedures. Among all endoscopic modalities, bronchoscopy is one of the most frequent with around 261 millions of procedures per year. Although the use of bronchoscopy is spread among clinical facilities it presents some drawbacks, being the visual inspection for the assessment of anatomical measurements the most prevalent of them. In
particular, inaccuracies in the estimation of the degree of stenosis (the percentage of obstructed airway) decreases its diagnostic yield and might lead to erroneous treatments. An objective computation of tracheal stenosis in bronchoscopy videos would constitute a breakthrough for this non-invasive technique and a reduction in treatment cost.
This thesis settles the first steps towards on-line reliable extraction of anatomical information from videobronchoscopy for computation of objective measures. In particular, we focus on the computation of the degree of stenosis, which is obtained by comparing the area delimited by a healthy tracheal ring and the stenosed lumen. Reliable extraction of airway structures in interventional videobronchoscopy is a challenging task. This is mainly due to the large variety of acquisition conditions (positions and illumination), devices (different digitalizations) and in videos acquired at the operating room the unpredicted presence of surgical devices (such as probe ends). This thesis contributes to on-line stenosis assessment in several ways. We
propose a parametric strategy for the extraction of lumen and tracheal rings regions based on the characterization of their geometry and appearance that guide a deformable model. The geometric and appearance characterization is based on a physical model describing the way bronchoscopy images are obtained and includes local and global descriptions. In order to ensure a systematic applicability we present a statistical framework to select the optimal
parameters of our method. Experiments perform on the first public annotated database, show that the performance of our method is comparable to the one provided by clinicians and its computation time allows for a on-line implementation in the operating room.
Address
Corporate Author Thesis Ph.D. thesis
Publisher Ediciones Graficas Rey Place of Publication Editor F. Javier Sanchez;Debora Gil;Jorge Bernal
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN 978-84-940902-9-5 Medium
Area Expedition Conference
Notes IAM; 600.075 Approved no
Call Number (up) Admin @ si @ San2014 Serial 2575
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Author Frederic Sampedro; Anna Domenech; Sergio Escalera
Title Static and dynamic computational cancer spread quantification in whole body FDG-PET/CT scans Type Journal Article
Year 2014 Publication Journal of Medical Imaging and Health Informatics Abbreviated Journal JMIHI
Volume 4 Issue 6 Pages 825-831
Keywords CANCER SPREAD; COMPUTER AIDED DIAGNOSIS; MEDICAL IMAGING; TUMOR QUANTIFICATION
Abstract In this work we address the computational cancer spread quantification scenario in whole body FDG-PET/CT scans. At the static level, this setting can be modeled as a clustering problem on the set of 3D connected components of the whole body PET tumoral segmentation mask carried out by nuclear medicine physicians. At the dynamic level, and ad-hoc algorithm is proposed in order to quantify the cancer spread time evolution which, when combined with other existing indicators, gives rise to the metabolic tumor volume-aggressiveness-spread time evolution chart, a novel tool that we claim that would prove useful in nuclear medicine and oncological clinical or research scenarios. Good performance results of the proposed methodologies both at the clinical and technological level are shown using a dataset of 48 segmented whole body FDG-PET/CT scans.
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 ISBN Medium
Area Expedition Conference
Notes HuPBA;MILAB Approved no
Call Number (up) Admin @ si @ SDE2014b Serial 2548
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