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	Author	Miquel Ferrer; Ernest Valveny; F. Serratosa
	Title	Spectral Median Graphs Applied to Graphical Symbol Recognition			Type	Book Chapter
	Year	2006	Publication	11th Iberoamerican Congress on Pattern Recognition (CIARP´06), J.P. Martinez–Trinidad et al. (Eds.), LNCS 4225: 774–783	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract
	Address	Cancun (Mexico)
	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	DAG			Approved	no
	Call Number	DAG @ dag @ FVS2006b			Serial	698
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	Author	Fernando Vilariño; Panagiota Spyridonos; Jordi Vitria; Carolina Malagelada; Petia Radeva
	Title	Linear Radial Patterns Characterization for Automatic Detection of Tonic Intestinal Contractions			Type	Book Chapter
	Year	2006	Publication	11th Iberoamerican Congress on Pattern Recognition	Abbreviated Journal
	Volume	4225	Issue		Pages	178–187
	Keywords
	Abstract	This work tackles the categorization of general linear radial patterns by means of the valleys and ridges detection and the use of descriptors of directional information, which are provided by steerable filters in different regions of the image. We successfully apply our proposal in the specific case of automatic detection of tonic contractions in video capsule endoscopy, which represent a paradigmatic example of linear radial patterns.
	Address	Cancun (Mexico)
	Corporate Author				Thesis
	Publisher	Springer Verlag	Place of Publication	Berlin Heidelberg	Editor	.F. Mart ́ınez-Trinidad et al
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title	LNCS
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area	800	Expedition		Conference
	Notes	MV;OR;MILAB;SIAI			Approved	no
	Call Number	BCNPCL @ bcnpcl @ VSV2006c; IAM @ iam @ VSB2006f			Serial	728
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	Author	Fernando Vilariño; Panagiota Spyridonos; Jordi Vitria; Carolina Malagelada; Petia Radeva
	Title	A Machine Learning framework using SOMs: Applications in the Intestinal Motility Assessment			Type	Book Chapter
	Year	2006	Publication	11th Iberoamerican Congress on Pattern Recognition	Abbreviated Journal
	Volume	4225	Issue		Pages	188–197
	Keywords
	Abstract	Small Bowel Motility Assessment by means of Wireless Capsule Video Endoscopy constitutes a novel clinical methodology in which a capsule with a micro-camera attached to it is swallowed by the patient, emitting a RF signal which is recorded as a video of its trip throughout the gut. In order to overcome the main drawbacks associated with this technique -mainly related to the large amount of visualization time required-, our efforts have been focused on the development of a machine learning system, built up in sequential stages, which provides the specialists with the useful part of the video, rejecting those parts not valid for analysis. We successfully used Self Organized Maps in a general semi-supervised framework with the aim of tackling the different learning stages of our system. The analysis of the diverse types of images and the automatic detection of intestinal contractions is performed under the perspective of intestinal motility assessment in a clinical environment.
	Address	Cancun (Mexico)
	Corporate Author				Thesis
	Publisher	Springer Verlag	Place of Publication	Berlin-Heidelberg	Editor	J.P. Martinez–Trinidad et al
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title	LNCS
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area	800	Expedition		Conference	CIARP06
	Notes	MV;OR;MILAB;SIAI			Approved	no
	Call Number	BCNPCL @ bcnpcl @ VSV2006d; IAM @ iam @ VSV2006e			Serial	729
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	Author	Albert Tatjer; Bhalaji Nagarajan; Ricardo Marques; Petia Radeva
	Title	CCLM: Class-Conditional Label Noise Modelling			Type	Conference Article
	Year	2023	Publication	11th Iberian Conference on Pattern Recognition and Image Analysis	Abbreviated Journal
	Volume	14062	Issue		Pages	3-14
	Keywords
	Abstract	The performance of deep neural networks highly depends on the quality and volume of the training data. However, cost-effective labelling processes such as crowdsourcing and web crawling often lead to data with noisy (i.e., wrong) labels. Making models robust to this label noise is thus of prime importance. A common approach is using loss distributions to model the label noise. However, the robustness of these methods highly depends on the accuracy of the division of training set into clean and noisy samples. In this work, we dive in this research direction highlighting the existing problem of treating this distribution globally and propose a class-conditional approach to split the clean and noisy samples. We apply our approach to the popular DivideMix algorithm and show how the local treatment fares better with respect to the global treatment of loss distribution. We validate our hypothesis on two popular benchmark datasets and show substantial improvements over the baseline experiments. We further analyze the effectiveness of the proposal using two different metrics – Noise Division Accuracy and Classiness.
	Address	Alicante; Spain; June 2023
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title	LNCS
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference	IbPRIA
	Notes	MILAB			Approved	no
	Call Number	Admin @ si @ TNM2023			Serial	3925
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	Author	Pau Riba; Josep Llados; Alicia Fornes
	Title	Error-tolerant coarse-to-fine matching model for hierarchical graphs			Type	Conference Article
	Year	2017	Publication	11th IAPR-TC-15 International Workshop on Graph-Based Representations in Pattern Recognition	Abbreviated Journal
	Volume	10310	Issue		Pages	107-117
	Keywords	Graph matching; Hierarchical graph; Graph-based representation; Coarse-to-fine matching
	Abstract	Graph-based representations are effective tools to capture structural information from visual elements. However, retrieving a query graph from a large database of graphs implies a high computational complexity. Moreover, these representations are very sensitive to noise or small changes. In this work, a novel hierarchical graph representation is designed. Using graph clustering techniques adapted from graph-based social media analysis, we propose to generate a hierarchy able to deal with different levels of abstraction while keeping information about the topology. For the proposed representations, a coarse-to-fine matching method is defined. These approaches are validated using real scenarios such as classification of colour images and handwritten word spotting.
	Address	Anacapri; Italy; May 2017
	Corporate Author				Thesis
	Publisher	Springer International Publishing	Place of Publication		Editor	Pasquale Foggia; Cheng-Lin Liu; Mario Vento
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference	GbRPR
	Notes	DAG; 600.097; 601.302; 600.121			Approved	no
	Call Number	Admin @ si @ RLF2017a			Serial	2951
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	Author	Pau Riba; Alicia Fornes; Josep Llados
	Title	Towards the Alignment of Handwritten Music Scores			Type	Conference Article
	Year	2015	Publication	11th IAPR International Workshop on Graphics Recognition	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	It is very common to find different versions of the same music work in archives of Opera Theaters. These differences correspond to modifications and annotations from the musicians. From the musicologist point of view, these variations are very interesting and deserve study. This paper explores the alignment of music scores as a tool for automatically detecting the passages that contain such differences. Given the difficulties in the recognition of handwritten music scores, our goal is to align the music scores and at the same time, avoid the recognition of music elements as much as possible. After removing the staff lines, braces and ties, the bar lines are detected. Then, the bar units are described as a whole using the Blurred Shape Model. The bar units alignment is performed by using Dynamic Time Warping. The analysis of the alignment path is used to detect the variations in the music scores. The method has been evaluated on a subset of the CVC-MUSCIMA dataset, showing encouraging results.
	Address	Nancy; France; August 2015
	Corporate Author				Thesis
	Publisher	Springer International Publishing	Place of Publication		Editor	Bart Lamiroy; Rafael Dueire Lins
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title	LNCS
	Series Volume		Series Issue		Edition
	ISSN		ISBN	978-3-319-52158-9	Medium
	Area		Expedition		Conference	GREC
	Notes	DAG			Approved	no
	Call Number	Admin @ si @			Serial	2874
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	Author	David Fernandez; R.Manmatha; Josep Llados; Alicia Fornes
	Title	Sequential Word Spotting in Historical Handwritten Documents			Type	Conference Article
	Year	2014	Publication	11th IAPR International Workshop on Document Analysis and Systems	Abbreviated Journal
	Volume		Issue		Pages	101 - 105
	Keywords
	Abstract	In this work we present a handwritten word spotting approach that takes advantage of the a priori known order of appearance of the query words. Given an ordered sequence of query word instances, the proposed approach performs a sequence alignment with the words in the target collection. Although the alignment is quite sparse, i.e. the number of words in the database is higher than the query set, the improvement in the overall performance is sensitively higher than isolated word spotting. As application dataset, we use a collection of handwritten marriage licenses taking advantage of the ordered index pages of family names.
	Address	Tours; Francia; April 2014
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		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	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	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
	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	P. Wang; V. Eglin; C. Garcia; C. Largeron; Josep Llados; Alicia Fornes
	Title	A Novel Learning-free Word Spotting Approach Based on Graph Representation			Type	Conference Article
	Year	2014	Publication	11th IAPR International Workshop on Document Analysis and Systems	Abbreviated Journal
	Volume		Issue		Pages	207-211
	Keywords
	Abstract	Effective information retrieval on handwritten document images has always been a challenging task. In this paper, we propose a novel handwritten word spotting approach based on graph representation. The presented model comprises both topological and morphological signatures of handwriting. Skeleton-based graphs with the Shape Context labelled vertexes are established for connected components. Each word image is represented as a sequence of graphs. In order to be robust to the handwriting variations, an exhaustive merging process based on DTW alignment result is introduced in the similarity measure between word images. With respect to the computation complexity, an approximate graph edit distance approach using bipartite matching is employed for graph matching. The experiments on the George Washington dataset and the marriage records from the Barcelona Cathedral dataset demonstrate that the proposed approach outperforms the state-of-the-art structural methods.
	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; 600.061; 602.006; 600.077			Approved	no
	Call Number	Admin @ si @ WEG2014b			Serial	2517
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	Author	Thanh Ha Do; Salvatore Tabbone; Oriol Ramos Terrades
	Title	Spotting Symbol Using Sparsity over Learned Dictionary of Local Descriptors			Type	Conference Article
	Year	2014	Publication	11th IAPR International Workshop on Document Analysis and Systems	Abbreviated Journal
	Volume		Issue		Pages	156-160
	Keywords
	Abstract	This paper proposes a new approach to spot symbols into graphical documents using sparse representations. More specifically, a dictionary is learned from a training database of local descriptors defined over the documents. Following their sparse representations, interest points sharing similar properties are used to define interest regions. Using an original adaptation of information retrieval techniques, a vector model for interest regions and for a query symbol is built based on its sparsity in a visual vocabulary where the visual words are columns in the learned dictionary. The matching process is performed comparing the similarity between vector models. Evaluation on SESYD datasets demonstrates that our method is promising.
	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	978-1-4799-3243-6	Medium
	Area		Expedition		Conference	DAS
	Notes	DAG; 600.077			Approved	no
	Call Number	Admin @ si @ DTR2014			Serial	2543
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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	Mireia Sole; Joan Blanco; Debora Gil; Oliver Valero; G. Fonseka; M. Lawrie; Francesca Vidal; Zaida Sarrate
	Title	Chromosome Territories in Mice Spermatogenesis: A new three-dimensional methodology of study			Type	Conference Article
	Year	2017	Publication	11th European CytoGenesis Conference	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract
	Address	Florencia; Italia; July 2017
	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	ECA
	Notes	IAM; 600.096; 600.145			Approved	no
	Call Number	Admin @ si @ SBG2017a			Serial	2936
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	Author	Marco Pedersoli; Jordi Gonzalez; Andrew Bagdanov; Juan J. Villanueva
	Title	Recursive Coarse-to-Fine Localization for fast Object Recognition			Type	Conference Article
	Year	2010	Publication	11th European Conference on Computer Vision	Abbreviated Journal
	Volume	6313	Issue	II	Pages	280–293
	Keywords
	Abstract	Cascading techniques are commonly used to speed-up the scan of an image for object detection. However, cascades of detectors are slow to train due to the high number of detectors and corresponding thresholds to learn. Furthermore, they do not use any prior knowledge about the scene structure to decide where to focus the search. To handle these problems, we propose a new way to scan an image, where we couple a recursive coarse-to-fine refinement together with spatial constraints of the object location. For doing that we split an image into a set of uniformly distributed neighborhood regions, and for each of these we apply a local greedy search over feature resolutions. The neighborhood is defined as a scanning region that only one object can occupy. Therefore the best hypothesis is obtained as the location with maximum score and no thresholds are needed. We present an implementation of our method using a pyramid of HOG features and we evaluate it on two standard databases, VOC2007 and INRIA dataset. Results show that the Recursive Coarse-to-Fine Localization (RCFL) achieves a 12x speed-up compared to standard sliding windows. Compared with a cascade of multiple resolutions approach our method has slightly better performance in speed and Average-Precision. Furthermore, in contrast to cascading approach, the speed-up is independent of image conditions, the number of detected objects and clutter.
	Address	Crete (Greece)
	Corporate Author				Thesis
	Publisher	Springer Berlin Heidelberg	Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title	LNCS
	Series Volume		Series Issue		Edition
	ISSN	0302-9743	ISBN	978-3-642-15566-6	Medium
	Area		Expedition		Conference	ECCV
	Notes	ISE			Approved	no
	Call Number	DAG @ dag @ PGB2010			Serial	1438
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	Author	Carles Fernandez; Jordi Gonzalez; Xavier Roca
	Title	Automatic Learning of Background Semantics in Generic Surveilled Scenes			Type	Conference Article
	Year	2010	Publication	11th European Conference on Computer Vision	Abbreviated Journal
	Volume	6313	Issue	II	Pages	678–692
	Keywords
	Abstract	Advanced surveillance systems for behavior recognition in outdoor traffic scenes depend strongly on the particular configuration of the scenario. Scene-independent trajectory analysis techniques statistically infer semantics in locations where motion occurs, and such inferences are typically limited to abnormality. Thus, it is interesting to design contributions that automatically categorize more specific semantic regions. State-of-the-art approaches for unsupervised scene labeling exploit trajectory data to segment areas like sources, sinks, or waiting zones. Our method, in addition, incorporates scene-independent knowledge to assign more meaningful labels like crosswalks, sidewalks, or parking spaces. First, a spatiotemporal scene model is obtained from trajectory analysis. Subsequently, a so-called GI-MRF inference process reinforces spatial coherence, and incorporates taxonomy-guided smoothness constraints. Our method achieves automatic and effective labeling of conceptual regions in urban scenarios, and is robust to tracking errors. Experimental validation on 5 surveillance databases has been conducted to assess the generality and accuracy of the segmentations. The resulting scene models are used for model-based behavior analysis.
	Address	Crete (Greece)
	Corporate Author				Thesis
	Publisher	Springer Berlin Heidelberg	Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title	LNCS
	Series Volume		Series Issue		Edition
	ISSN	0302-9743	ISBN	978-3-642-15551-2	Medium
	Area		Expedition		Conference	ECCV
	Notes	ISE			Approved	no
	Call Number	ISE @ ise @ FGR2010			Serial	1439
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