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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	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	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	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	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	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	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	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	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	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	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	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	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	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	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	Admin @ si @ SDE2014b			Serial	2548
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