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	Author	David Roche; Debora Gil; Jesus Giraldo
	Title	Using statistical inference for designing termination conditions ensuring convergence of Evolutionary Algorithms			Type	Conference Article
	Year	2011	Publication	11th European Conference on Artificial Life	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	A main challenge in Evolutionary Algorithms (EAs) is determining a termination condition ensuring stabilization close to the optimum in real-world applications. Although for known test functions distribution-based quantities are good candidates (as far as suitable parameters are used), in real-world problems an open question still remains unsolved. How can we estimate an upper-bound for the termination condition value ensuring a given accuracy for the (unknown) EA solution? We claim that the termination problem would be fully solved if we defined a quantity (depending only on the EA output) behaving like the solution accuracy. The open question would be, then, satisfactorily answered if we had a model relating both quantities, since accuracy could be predicted from the alternative quantity. We present a statistical inference framework addressing two topics: checking the correlation between the two quantities and defining a regression model for predicting (at a given confidence level) accuracy values from the EA output.
	Address	Paris, France
	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	ECAL
	Notes	IAM;			Approved	no
	Call Number	IAM @ iam @ RGG2011b			Serial	1678
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	Author	Marçal Rusiñol; David Aldavert; Dimosthenis Karatzas; Ricardo Toledo; Josep Llados
	Title	Interactive Trademark Image Retrieval by Fusing Semantic and Visual Content. Advances in Information Retrieval			Type	Conference Article
	Year	2011	Publication	33rd European Conference on Information Retrieval	Abbreviated Journal
	Volume	6611	Issue		Pages	314-325
	Keywords
	Abstract	In this paper we propose an efficient queried-by-example retrieval system which is able to retrieve trademark images by similarity from patent and trademark offices' digital libraries. Logo images are described by both their semantic content, by means of the Vienna codes, and their visual contents, by using shape and color as visual cues. The trademark descriptors are then indexed by a locality-sensitive hashing data structure aiming to perform approximate k-NN search in high dimensional spaces in sub-linear time. The resulting ranked lists are combined by using the Condorcet method and a relevance feedback step helps to iteratively revise the query and refine the obtained results. The experiments demonstrate the effectiveness and efficiency of this system on a realistic and large dataset.
	Address	Dublin, Ireland
	Corporate Author				Thesis
	Publisher	Springer	Place of Publication	Berlin	Editor	P. Clough; C. Foley; C. Gurrin; G.J.F. Jones; W. Kraaij; H. Lee; V. Murdoch
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title	LNCS
	Series Volume		Series Issue		Edition
	ISSN		ISBN	978-3-642-20160-8	Medium
	Area		Expedition		Conference	ECIR
	Notes	DAG; RV;ADAS			Approved	no
	Call Number	Admin @ si @ RAK2011			Serial	1737
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	Author	Jordi Roca; C. Alejandro Parraga; Maria Vanrell
	Title	Categorical Focal Colours are Structurally Invariant Under Illuminant Changes			Type	Conference Article
	Year	2011	Publication	European Conference on Visual Perception	Abbreviated Journal
	Volume		Issue		Pages	196
	Keywords
	Abstract	The visual system perceives the colour of surfaces approximately constant under changes of illumination. In this work, we investigate how stable is the perception of categorical \“focal\” colours and their interrelations with varying illuminants and simple chromatic backgrounds. It has been proposed that best examples of colour categories across languages cluster in small regions of the colour space and are restricted to a set of 11 basic terms (Kay and Regier, 2003 Proceedings of the National Academy of Sciences of the USA 100 9085\–9089). Following this, we developed a psychophysical paradigm that exploits the ability of subjects to reliably reproduce the most representative examples of each category, adjusting multiple test patches embedded in a coloured Mondrian. The experiment was run on a CRT monitor (inside a dark room) under various simulated illuminants. We modelled the recorded data for each subject and adapted state as a 3D interconnected structure (graph) in Lab space. The graph nodes were the subject\’s focal colours at each adaptation state. The model allowed us to get a better distance measure between focal structures under different illuminants. We found that perceptual focal structures tend to be preserved better than the structures of the physical \“ideal\” colours under illuminant changes.
	Address
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title	Perception 40	Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference	ECVP
	Notes	CIC			Approved	no
	Call Number	Admin @ si @ RPV2011			Serial	1867
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	Author	Gemma Roig; Xavier Boix; F. de la Torre; Joan Serrat; C. Vilella
	Title	Hierarchical CRF with product label spaces for parts-based Models			Type	Conference Article
	Year	2011	Publication	IEEE Conference on Automatic Face and Gesture Recognition	Abbreviated Journal
	Volume		Issue		Pages	657-664
	Keywords	Shape; Computational modeling; Principal component analysis; Random variables; Color; Upper bound; Facial features
	Abstract	Non-rigid object detection is a challenging an open research problem in computer vision. It is a critical part in many applications such as image search, surveillance, human-computer interaction or image auto-annotation. Most successful approaches to non-rigid object detection make use of part-based models. In particular, Conditional Random Fields (CRF) have been successfully embedded into a discriminative parts-based model framework due to its effectiveness for learning and inference (usually based on a tree structure). However, CRF-based approaches do not incorporate global constraints and only model pairwise interactions. This is especially important when modeling object classes that may have complex parts interactions (e.g. facial features or body articulations), because neglecting them yields an oversimplified model with suboptimal performance. To overcome this limitation, this paper proposes a novel hierarchical CRF (HCRF). The main contribution is to build a hierarchy of part combinations by extending the label set to a hierarchy of product label spaces. In order to keep the inference computation tractable, we propose an effective method to reduce the new label set. We test our method on two applications: facial feature detection on the Multi-PIE database and human pose estimation on the Buffy dataset.
	Address	Santa Barbara, CA, USA, 2011
	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	FG
	Notes	ADAS			Approved	no
	Call Number	Admin @ si @ RBT2011			Serial	1862
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	Author	Jaume Gibert; Ernest Valveny; Horst Bunke
	Title	Dimensionality Reduction for Graph of Words Embedding			Type	Conference Article
	Year	2011	Publication	8th IAPR-TC-15 International Workshop. Graph-Based Representations in Pattern Recognition	Abbreviated Journal
	Volume	6658	Issue		Pages	22-31
	Keywords
	Abstract	The Graph of Words Embedding consists in mapping every graph of a given dataset to a feature vector by counting unary and binary relations between node attributes of the graph. While it shows good properties in classification problems, it suffers from high dimensionality and sparsity. These two issues are addressed in this article. Two well-known techniques for dimensionality reduction, kernel principal component analysis (kPCA) and independent component analysis (ICA), are applied to the embedded graphs. We discuss their performance compared to the classification of the original vectors on three different public databases of graphs.
	Address	Münster, Germany
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor	Xiaoyi Jiang; Miquel Ferrer; Andrea Torsello
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title	LNCS
	Series Volume		Series Issue		Edition
	ISSN		ISBN	978-3-642-20843-0	Medium
	Area		Expedition		Conference	GbRPR
	Notes	DAG			Approved	no
	Call Number	Admin @ si @ GVB2011a			Serial	1743
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	Author	Oscar Amoros; Sergio Escalera; Anna Puig
	Title	Adaboost GPU-based Classifier for Direct Volume Rendering			Type	Conference Article
	Year	2011	Publication	International Conference on Computer Graphics Theory and Applications	Abbreviated Journal
	Volume		Issue		Pages	215-219
	Keywords
	Abstract	In volume visualization, the voxel visibitity and materials are carried out through an interactive editing of Transfer Function. In this paper, we present a two-level GPU-based labeling method that computes in times of rendering a set of labeled structures using the Adaboost machine learning classifier. In a pre-processing step, Adaboost trains a binary classifier from a pre-labeled dataset and, in each sample, takes into account a set of features. This binary classifier is a weighted combination of weak classifiers, which can be expressed as simple decision functions estimated on a single feature values. Then, at the testing stage, each weak classifier is independently applied on the features of a set of unlabeled samples. We propose an alternative representation of these classifiers that allow a GPU-based parallelizated testing stage embedded into the visualization pipeline. The empirical results confirm the OpenCL-based classification of biomedical datasets as a tough problem where an opportunity for further research emerges.
	Address	Algarve, Portugal
	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	GRAPP
	Notes	MILAB; HuPBA			Approved	no
	Call Number	Admin @ si @ AEP2011			Serial	1774
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	Author	Lluis Pere de las Heras; Joan Mas; Gemma Sanchez; Ernest Valveny
	Title	Descriptor-based Svm Wall Detector			Type	Conference Article
	Year	2011	Publication	9th International Workshop on Graphic Recognition	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	Architectural floorplans exhibit a large variability in notation. Therefore, segmenting and identifying the elements of any kind of plan becomes a challenging task for approaches based on grouping structural primitives obtained by vectorization. Recently, a patch-based segmentation method working at pixel level and relying on the construction of a visual vocabulary has been proposed showing its adaptability to different notations by automatically learning the visual appearance of the elements in each different notation. In this paper we describe an evolution of this new approach in two directions: firstly we evaluate different features to obtain the description of every patch. Secondly, we train an SVM classifier to obtain the category of every patch instead of constructing a visual vocabulary. These modifications of the method have been tested for wall detection on two datasets of architectural floorplans with different notations and compared with the results obtained with the original approach.
	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	GREC
	Notes	DAG			Approved	no
	Call Number	Admin @ si @ HMS2011b			Serial	1819
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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	Conference Article
	Year	2011	Publication	In proceedings of 9th IAPR Workshop on Graphic Recognition	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	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	Seoul, Corea
	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	GREC
	Notes	DAG			Approved	no
	Call Number	Admin @ si @ RPK2011			Serial	1821
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	Author	Anjan Dutta; Josep Llados; Umapada Pal
	Title	Bag-of-GraphPaths Descriptors for Symbol Recognition and Spotting in Line Drawings			Type	Conference Article
	Year	2011	Publication	In proceedings of 9th IAPR Workshop on Graphic Recognition	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	Graphical symbol recognition and spotting recently have become an important research activity. In this work we present a descriptor for symbols, especially for line drawings. The descriptor is based on the graph representation of graphical objects. We construct graphs from the vectorized information of the binarized images, where the critical points detected by the vectorization algorithm are considered as nodes and the lines joining them are considered as edges. Graph paths between two nodes in a graph are the finite sequences of nodes following the order from the starting to the final node. The occurrences of different graph paths in a given graph is an important feature, as they capture the geometrical and structural attributes of a graph. So the graph representing a symbol can efficiently be represent by the occurrences of its different paths. Their occurrences in a symbol can be obtained in terms of a histogram counting the number of some fixed prototype paths, we call the histogram as the Bag-of-GraphPaths (BOGP). These BOGP histograms are used as a descriptor to measure the distance among the symbols in vector space. We use the descriptor for three applications, they are: (1) classification of the graphical symbols, (2) spotting of the architectural symbols on floorplans, (3) classification of the historical handwritten words.
	Address	Seoul, Korea
	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	978-3-642-36823-3	Medium
	Area		Expedition		Conference	GREC
	Notes	DAG			Approved	no
	Call Number	Admin @ si @ DLP2011c			Serial	1825
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	Author	Antonio Hernandez; Carlos Primo; Sergio Escalera
	Title	Automatic user interaction correction via Multi-label Graph cuts			Type	Conference Article
	Year	2011	Publication	In ICCV 2011 1st IEEE International Workshop on Human Interaction in Computer Vision HICV	Abbreviated Journal
	Volume		Issue		Pages	1276-1281
	Keywords
	Abstract	Most applications in image segmentation requires from user interaction in order to achieve accurate results. However, user wants to achieve the desired segmentation accuracy reducing effort of manual labelling. In this work, we extend standard multi-label α-expansion Graph Cut algorithm so that it analyzes the interaction of the user in order to modify the object model and improve final segmentation of objects. The approach is inspired in the fact that fast user interactions may introduce some pixel errors confusing object and background. Our results with different degrees of user interaction and input errors show high performance of the proposed approach on a multi-label human limb segmentation problem compared with classical α-expansion algorithm.
	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	978-1-4673-0062-9	Medium
	Area		Expedition		Conference	HICV
	Notes	MILAB; HuPBA			Approved	no
	Call Number	Admin @ si @ HPE2011			Serial	1892
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	Author	Alicia Fornes; Volkmar Frinken; Andreas Fischer; Jon Almazan; G. Jackson; Horst Bunke
	Title	A Keyword Spotting Approach Using Blurred Shape Model-Based Descriptors			Type	Conference Article
	Year	2011	Publication	Proceedings of the 2011 Workshop on Historical Document Imaging and Processing	Abbreviated Journal
	Volume		Issue		Pages	83-90
	Keywords
	Abstract	The automatic processing of handwritten historical documents is considered a hard problem in pattern recognition. In addition to the challenges given by modern handwritten data, a lack of training data as well as effects caused by the degradation of documents can be observed. In this scenario, keyword spotting arises to be a viable solution to make documents amenable for searching and browsing. For this task we propose the adaptation of shape descriptors used in symbol recognition. By treating each word image as a shape, it can be represented using the Blurred Shape Model and the De-formable Blurred Shape Model. Experiments on the George Washington database demonstrate that this approach is able to outperform the commonly used Dynamic Time Warping approach.
	Address
	Corporate Author				Thesis
	Publisher	ACM	Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN	978-1-4503-0916-5	Medium
	Area		Expedition		Conference	HIP
	Notes	DAG			Approved	no
	Call Number	Admin @ si @ FFF2011a			Serial	1823
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	Author	Andreas Fischer; Volkmar Frinken; Alicia Fornes; Horst Bunke
	Title	Transcription Alignment of Latin Manuscripts Using Hidden Markov Models			Type	Conference Article
	Year	2011	Publication	Proceedings of the 2011 Workshop on Historical Document Imaging and Processing	Abbreviated Journal
	Volume		Issue		Pages	29-36
	Keywords
	Abstract	Transcriptions of historical documents are a valuable source for extracting labeled handwriting images that can be used for training recognition systems. In this paper, we introduce the Saint Gall database that includes images as well as the transcription of a Latin manuscript from the 9th century written in Carolingian script. Although the available transcription is of high quality for a human reader, the spelling of the words is not accurate when compared with the handwriting image. Hence, the transcription poses several challenges for alignment regarding, e.g., line breaks, abbreviations, and capitalization. We propose an alignment system based on character Hidden Markov Models that can cope with these challenges and efficiently aligns complete document pages. On the Saint Gall database, we demonstrate that a considerable alignment accuracy can be achieved, even with weakly trained character models.
	Address
	Corporate Author				Thesis
	Publisher	ACM	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	HIP
	Notes	DAG			Approved	no
	Call Number	Admin @ si @ FFF2011b			Serial	1824
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	Author	Miguel Reyes; Jose Ramirez Moreno; Juan R Revilla; Petia Radeva; Sergio Escalera
	Title	ADiBAS: Sistema Multisensor de Adquisicion Automatica de Datos Corporales Objetivos, Robustos y Fiables para el Analisis de la Postura y el Movimiento			Type	Conference Article
	Year	2011	Publication	6th Congreso Iberoamericano de Tecnologia de Apoyo a la Discapacidad	Abbreviated Journal
	Volume		Issue		Pages	939-944
	Keywords
	Abstract	El análisis de la postura y del rango de movimiento son fundamentales para conocer la optimización del gesto y mejorar, de este modo, el rendimiento y la detección de posibles lesiones. Esta cuantificación es especialmente interesante en deportistas o en pacientes que presentan alguna lesión neurológica o del sistema musculo-esquelético, ya que permite conocer el proceso evolutivo de estos pacientes, evaluar la eficacia de la terapia aplicada y proponer, en caso necesario, una modificación del protocolo de tratamiento. En este trabajo presentamos un sistema automático que permite, mediante una tecnología no invasiva, la captación automática de marcadores LED situados sobre el paciente y su posterior análisis con el fin de mostrar al especialista datos objetivos que permitan un mejor soporte diagnóstico. También se describe un sistema analítico de la postura corporal sin marcadores, donde su ejecución durante secuencias dinámicas aporta un alto grado de naturalidad al paciente a la hora de realizar los ejercicios funcionales.
	Address	Palma de Mallorca
	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	IBERDISCAP
	Notes	MILAB;HuPBA			Approved	no
	Call Number	Admin @ si @ RRR2011			Serial	1768
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	Author	Muhammad Anwer Rao; David Vazquez; Antonio Lopez
	Title	Opponent Colors for Human Detection			Type	Conference Article
	Year	2011	Publication	5th Iberian Conference on Pattern Recognition and Image Analysis	Abbreviated Journal
	Volume	6669	Issue		Pages	363-370
	Keywords	Pedestrian Detection; Color; Part Based Models
	Abstract	Human detection is a key component in fields such as advanced driving assistance and video surveillance. However, even detecting non-occluded standing humans remains a challenge of intensive research. Finding good features to build human models for further detection is probably one of the most important issues to face. Currently, shape, texture and motion features have deserve extensive attention in the literature. However, color-based features, which are important in other domains (e.g., image categorization), have received much less attention. In fact, the use of RGB color space has become a kind of choice by default. The focus has been put in developing first and second order features on top of RGB space (e.g., HOG and co-occurrence matrices, resp.). In this paper we evaluate the opponent colors (OPP) space as a biologically inspired alternative for human detection. In particular, by feeding OPP space in the baseline framework of Dalal et al. for human detection (based on RGB, HOG and linear SVM), we will obtain better detection performance than by using RGB space. This is a relevant result since, up to the best of our knowledge, OPP space has not been previously used for human detection. This suggests that in the future it could be worth to compute co-occurrence matrices, self-similarity features, etc., also on top of OPP space, i.e., as we have done with HOG in this paper.
	Address	Las Palmas de Gran Canaria. Spain
	Corporate Author				Thesis
	Publisher	Springer	Place of Publication	Berlin Heidelberg	Editor	J. Vitria; J.M. Sanches; M. Hernandez
	Language	English	Summary Language	English	Original Title	Opponent Colors for Human Detection
	Series Editor		Series Title	Lecture Notes on Computer Science	Abbreviated Series Title	LNCS
	Series Volume		Series Issue		Edition
	ISSN	0302-9743	ISBN	978-3-642-21256-7	Medium
	Area		Expedition		Conference	IbPRIA
	Notes	ADAS			Approved	no
	Call Number	ADAS @ adas @ RVL2011a			Serial	1666
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	Author	Jorge Bernal; F. Javier Sanchez; Fernando Vilariño
	Title	A Region Segmentation Method for Colonoscopy Images Using a Model of Polyp Appearance			Type	Conference Article
	Year	2011	Publication	5th Iberian Conference on Pattern Recognition and Image Analysis	Abbreviated Journal
	Volume	6669	Issue		Pages	134-143
	Keywords	Colonoscopy, Polyp Detection, Region Merging, Region Segmentation.
	Abstract	This work aims at the segmentation of colonoscopy images into a minimum number of informative regions. Our method performs in a way such, if a polyp is present in the image, it will be exclusively and totally contained in a single region. This result can be used in later stages to classify regions as polyp-containing candidates. The output of the algorithm also defines which regions can be considered as non-informative. The algorithm starts with a high number of initial regions and merges them taking into account the model of polyp appearance obtained from available data. The results show that our segmentations of polyp regions are more accurate than state-of-the-art methods.
	Address	Las Palmas de Gran Canaria, June 2011
	Corporate Author	SpringerLink			Thesis
	Publisher		Place of Publication		Editor	Vitrià, Jordi and Sanches, João and Hernández, Mario
	Language		Summary Language		Original Title
	Series Editor		Series Title	Lecture Notes in Computer Science	Abbreviated Series Title	LNCS
	Series Volume		Series Issue		Edition
	ISSN		ISBN	978-3-642-21256-7	Medium
	Area	800	Expedition		Conference	IbPRIA
	Notes	MV;SIAI			Approved	no
	Call Number	IAM @ iam @ BSV2011c			Serial	1696
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