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	Author	Jon Almazan; Ernest Valveny; Alicia Fornes
	Title	Deforming the Blurred Shape Model for Shape Description and Recognition			Type	Conference Article
	Year	2011	Publication	5th Iberian Conference on Pattern Recognition and Image Analysis	Abbreviated Journal
	Volume	6669	Issue		Pages	1-8
	Keywords
	Abstract	This paper presents a new model for the description and recognition of distorted shapes, where the image is represented by a pixel density distribution based on the Blurred Shape Model combined with a non-linear image deformation model. This leads to an adaptive structure able to capture elastic deformations in shapes. This method has been evaluated using thee different datasets where deformations are present, showing the robustness and good performance of the new model. Moreover, we show that incorporating deformation and flexibility, the new model outperforms the BSM approach when classifying shapes with high variability of appearance.
	Address	Las Palmas de Gran Canaria. Spain
	Corporate Author				Thesis
	Publisher	Springer-Verlag	Place of Publication	Berlin	Editor	Jordi Vitria; Joao Miguel Raposo; Mario Hernandez
	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	DAG;			Approved	no
	Call Number	Admin @ si @ AVF2011			Serial	1732
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	Author	Maria Vanrell; Naila Murray; Robert Benavente; C. Alejandro Parraga; Xavier Otazu; Ramon Baldrich
	Title	Perception Based Representations for Computational Colour			Type	Conference Article
	Year	2011	Publication	3rd International Workshop on Computational Color Imaging	Abbreviated Journal
	Volume	6626	Issue		Pages	16-30
	Keywords	colour perception, induction, naming, psychophysical data, saliency, segmentation
	Abstract	The perceived colour of a stimulus is dependent on multiple factors stemming out either from the context of the stimulus or idiosyncrasies of the observer. The complexity involved in combining these multiple effects is the main reason for the gap between classical calibrated colour spaces from colour science and colour representations used in computer vision, where colour is just one more visual cue immersed in a digital image where surfaces, shadows and illuminants interact seemingly out of control. With the aim to advance a few steps towards bridging this gap we present some results on computational representations of colour for computer vision. They have been developed by introducing perceptual considerations derived from the interaction of the colour of a point with its context. We show some techniques to represent the colour of a point influenced by assimilation and contrast effects due to the image surround and we show some results on how colour saliency can be derived in real images. We outline a model for automatic assignment of colour names to image points directly trained on psychophysical data. We show how colour segments can be perceptually grouped in the image by imposing shading coherence in the colour space.
	Address	Milan, Italy
	Corporate Author				Thesis
	Publisher	Springer-Verlag	Place of Publication		Editor	Raimondo Schettini, Shoji Tominaga, Alain Trémeau
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title	LNCS
	Series Volume		Series Issue		Edition
	ISSN		ISBN	978-3-642-20403-6	Medium
	Area		Expedition		Conference	CCIW
	Notes	CIC			Approved	no
	Call Number	Admin @ si @ VMB2011			Serial	1733
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	Author	Michal Drozdzal; Santiago Segui; Carolina Malagelada; Fernando Azpiroz; Jordi Vitria; Petia Radeva
	Title	Interactive Labeling of WCE Images			Type	Conference Article
	Year	2011	Publication	5th Iberian Conference on Pattern Recognition and Image Analysis	Abbreviated Journal
	Volume	6669	Issue		Pages	143-150
	Keywords
	Abstract	A high quality labeled training set is necessary for any supervised machine learning algorithm. Labeling of the data can be a very expensive process, specially while dealing with data of high variability and complexity. A good example of such data are the videos from Wireless Capsule Endoscopy. Building a representative WCE data set means many videos to be labeled by an expert. The problem that occurs is the data diversity, in the space of the features, from different WCE studies. That means that when new data arrives it is highly probable that it will not be represented in the training set, thus getting a high probability of performing an error when applying machine learning schemes. In this paper an interactive labeling scheme that allows reducing expert effort in the labeling process is presented. It is shown that the number of human interventions can be significantly reduced. The proposed system allows the annotation of informative/non-informative frames of the WCE video with less than 100 clicks
	Address	Las Palmas de Gran Canaria. Spain
	Corporate Author				Thesis
	Publisher	Springer	Place of Publication		Editor	Vitria, Jordi; Sanches, João Miguel Raposo; Hernández, Mario
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference	IbPRIA
	Notes	MILAB;OR;MV			Approved	no
	Call Number	Admin @ si @ DSM2011			Serial	1734
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	Author	Pierluigi Casale; Oriol Pujol; Petia Radeva
	Title	Human Activity Recognition from Accelerometer Data using a Wearable Device			Type	Conference Article
	Year	2011	Publication	5th Iberian Conference on Pattern Recognition and Image Analysis	Abbreviated Journal
	Volume	6669	Issue		Pages	289-296
	Keywords
	Abstract	Activity Recognition is an emerging field of research, born from the larger fields of ubiquitous computing, context-aware computing and multimedia. Recently, recognizing everyday life activities becomes one of the challenges for pervasive computing. In our work, we developed a novel wearable system easy to use and comfortable to bring. Our wearable system is based on a new set of 20 computationally efficient features and the Random Forest classifier. We obtain very encouraging results with classification accuracy of human activities recognition of up to 94%.
	Address	Las Palmas de Gran Canaria. Spain
	Corporate Author				Thesis
	Publisher	Springer Berlin Heidelberg	Place of Publication		Editor	Vitria, Jordi; Sanches, João Miguel Raposo; Hernández, Mario
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title	LNCS
	Series Volume		Series Issue		Edition
	ISSN	0302-9743	ISBN	978-3-642-21256-7	Medium
	Area		Expedition		Conference	IbPRIA
	Notes	MILAB;HuPBA			Approved	no
	Call Number	Admin @ si @ CPR2011a			Serial	1735
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	Author	Lluis Pere de las Heras; Gemma Sanchez
	Title	And-Or Graph Grammar for Architectural Floorplan Representation, Learning and Recognition. A Semantic, Structural and Hierarchical Model			Type	Conference Article
	Year	2011	Publication	5th Iberian Conference on Pattern Recognition and Image Analysis	Abbreviated Journal
	Volume	6669	Issue		Pages	17-24
	Keywords
	Abstract	This paper presents a syntactic model for architectural floor plan interpretation. A stochastic image grammar over an And-Or graph is inferred to represent the hierarchical, structural and semantic relations between elements of all possible floor plans. This grammar is augmented with three different probabilistic models, learnt from a training set, to account the frequency of that relations. Then, a Bottom-Up/Top-Down parser with a pruning strategy has been used for floor plan recognition. For a given input, the parser generates the most probable parse graph for that document. This graph not only contains the structural and semantic relations of its elements, but also its hierarchical composition, that allows to interpret the floor plan at different levels of abstraction.
	Address	Las Palmas de Gran Canaria. Spain
	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	978-3-642-21256-7	Medium
	Area		Expedition		Conference	IbPRIA
	Notes	DAG			Approved	no
	Call Number	Admin @ si @ HeS2011			Serial	1736
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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	Anjan Dutta; Josep Llados; Umapada Pal
	Title	A Bag-of-Paths Based Serialized Subgraph Matching for Symbol Spotting in Line Drawings			Type	Conference Article
	Year	2011	Publication	5th Iberian Conference on Pattern Recognition and Image Analysis	Abbreviated Journal
	Volume	6669	Issue		Pages	620-627
	Keywords
	Abstract	In this paper we propose an error tolerant subgraph matching algorithm based on bag-of-paths for solving the problem of symbol spotting in line drawings. Bag-of-paths is a factorized representation of graphs where the factorization is done by considering all the acyclic paths between each pair of connected nodes. Similar paths within the whole collection of documents are clustered and organized in a lookup table for efficient indexing. The lookup table contains the index key of each cluster and the corresponding list of locations as a single entry. The mean path of each of the clusters serves as the index key for each table entry. The spotting method is then formulated by a spatial voting scheme to the list of locations of the paths that are decided in terms of search of similar paths that compose the query symbol. Efficient indexing of common substructures helps to reduce the computational burden of usual graph based methods. The proposed method can also be seen as a way to serialize graphs which allows to reduce the complexity of the subgraph isomorphism. We have encoded the paths in terms of both attributed strings and turning functions, and presented a comparative results between them within the symbol spotting framework. Experimentations for matching different shape silhouettes are also reported and the method has been proved to work in noisy environment also.
	Address	Las Palmas de Gran Canaria. Spain
	Corporate Author				Thesis
	Publisher	Springer Berlin Heidelberg	Place of Publication	Berlin	Editor	Jordi Vitria; Joao Miguel Raposo; Mario Hernandez
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title	LNCS
	Series Volume		Series Issue		Edition
	ISSN	0302-9743	ISBN	978-3-642-21256-7	Medium
	Area		Expedition		Conference	IbPRIA
	Notes	DAG			Approved	no
	Call Number	Admin @ si @ DLP2011a			Serial	1738
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	Author	Francesco Ciompi; Oriol Pujol; Carlo Gatta; Xavier Carrillo; Josepa Mauri; Petia Radeva
	Title	A Holistic Approach for the Detection of Media-Adventitia Border in IVUS			Type	Conference Article
	Year	2011	Publication	14th International Conference on Medical Image Computing and Computer Assisted Intervention	Abbreviated Journal
	Volume	6893	Issue		Pages	401-408
	Keywords
	Abstract	In this paper we present a methodology for the automatic detection of media-adventitia border (MAb) in Intravascular Ultrasound. A robust computation of the MAb is achieved through a holistic approach where the position of the MAb with respect to other tissues of the vessel is used. A learned quality measure assures that the resulting MAb is optimal with respect to all other tissues. The mean distance error computed through a set of 140 images is 0.2164 (±0.1326) mm.
	Address	Toronto, Canada
	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	978-3-642-23625-9	Medium
	Area		Expedition		Conference	MICCAI
	Notes	MILAB;HuPBA			Approved	no
	Call Number	Admin @ si @ CPG2011			Serial	1739
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	Author	Marina Alberti; Carlo Gatta; Simone Balocco; Francesco Ciompi; Oriol Pujol; Joana Silva; Xavier Carrillo; Petia Radeva
	Title	Automatic Branching Detection in IVUS Sequences			Type	Conference Article
	Year	2011	Publication	5th Iberian Conference on Pattern Recognition and Image Analysis	Abbreviated Journal
	Volume	6669	Issue		Pages	126-133
	Keywords
	Abstract	Atherosclerosis is a vascular pathology affecting the arterial walls, generally located in specific vessel sites, such as bifurcations. In this paper, for the first time, a fully automatic approach for the detection of bifurcations in IVUS pullback sequences is presented. The method identifies the frames and the angular sectors in which a bifurcation is visible. This goal is achieved by applying a classifier to a set of textural features extracted from each image of an IVUS pullback. A comparison between two state-of-the-art classifiers is performed, AdaBoost and Random Forest. A cross-validation scheme is applied in order to evaluate the performances of the approaches. The obtained results are encouraging, showing a sensitivity of 75% and an accuracy of 94% by using the AdaBoost algorithm.
	Address
	Corporate Author				Thesis
	Publisher	Springer Berlin Heidelberg	Place of Publication	Berlin	Editor	Jordi Vitria; Joao Miguel Raposo; Mario Hernandez
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title	LNCS
	Series Volume		Series Issue		Edition
	ISSN	0302-9743	ISBN	978-3-642-21256-7	Medium
	Area		Expedition		Conference	IbPRIA
	Notes	MILAB;HuPBA			Approved	no
	Call Number	Admin @ si @ AGB2011			Serial	1740
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	Author	Simone Balocco; Carlo Gatta; Francesco Ciompi; Oriol Pujol; Xavier Carrillo; Josepa Mauri; Petia Radeva
	Title	Combining Growcut and Temporal Correlation for IVUS Lumen Segmentation			Type	Conference Article
	Year	2011	Publication	5th Iberian Conference on Pattern Recognition and Image Analysis	Abbreviated Journal
	Volume	6669	Issue		Pages	556-563
	Keywords
	Abstract	The assessment of arterial luminal area, performed by IVUS analysis, is a clinical index used to evaluate the degree of coronary artery disease. In this paper we propose a novel approach to automatically segment the vessel lumen, which combines model-based temporal information extracted from successive frames of the sequence, with spatial classification using the Growcut algorithm. The performance of the method is evaluated by an in vivo experiment on 300 IVUS frames. The automatic and manual segmentation performances in general vessel and stent frames are comparable. The average segmentation error in vessel, stent and bifurcation frames are 0.17±0.08 mm, 0.18±0.07 mm and 0.31±0.12 mm respectively.
	Address	Las Palmas de Gran Canaria. Spain
	Corporate Author				Thesis
	Publisher	Springer Berlin Heidelberg	Place of Publication	Berlin	Editor	Jordi Vitria; Joao Miguel Raposo; Mario Hernandez
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title	LNCS
	Series Volume		Series Issue		Edition
	ISSN	0302-9743	ISBN	978-3-642-21256-7	Medium
	Area		Expedition		Conference	IbPRIA
	Notes	MILAB;HuPBA			Approved	no
	Call Number	Admin @ si @ BGC2011a			Serial	1741
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	Author	David Fernandez; Josep Llados; Alicia Fornes
	Title	Handwritten Word Spotting in Old Manuscript Images Using a Pseudo-Structural Descriptor Organized in a Hash Structure			Type	Conference Article
	Year	2011	Publication	5th Iberian Conference on Pattern Recognition and Image Analysis	Abbreviated Journal
	Volume	6669	Issue		Pages	628-635
	Keywords
	Abstract	There are lots of historical handwritten documents with information that can be used for several studies and projects. The Document Image Analysis and Recognition community is interested in preserving these documents and extracting all the valuable information from them. Handwritten word-spotting is the pattern classification task which consists in detecting handwriting word images. In this work, we have used a query-by-example formalism: we have matched an input image with one or multiple images from handwritten documents to determine the distance that might indicate a correspondence. We have developed an approach based in characteristic Loci Features stored in a hash structure. Document images of the marriage licences of the Cathedral of Barcelona are used as the benchmarking database.
	Address	Las Palmas de Gran Canaria. Spain
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor	Jordi Vitria; Joao Miguel Raposo; Mario Hernandez
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN	978-3-642-21256-7	Medium
	Area		Expedition		Conference	IbPRIA
	Notes	DAG			Approved	no
	Call Number	Admin @ si @ FLF2011			Serial	1742
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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	Jaume Gibert; Ernest Valveny; Horst Bunke
	Title	Vocabulary Selection for Graph of Words Embedding			Type	Conference Article
	Year	2011	Publication	5th Iberian Conference on Pattern Recognition and Image Analysis	Abbreviated Journal
	Volume	6669	Issue		Pages	216-223
	Keywords
	Abstract	The Graph of Words Embedding consists in mapping every graph in a given dataset to a feature vector by counting unary and binary relations between node attributes of the graph. It has been shown to perform well for graphs with discrete label alphabets. In this paper we extend the methodology to graphs with n-dimensional continuous attributes by selecting node representatives. We propose three different discretization procedures for the attribute space and experimentally evaluate the dependence on both the selector and the number of node representatives. In the context of graph classification, the experimental results reveal that on two out of three public databases the proposed extension achieves superior performance over a standard reference system.
	Address	Las Palmas de Gran Canaria. Spain
	Corporate Author				Thesis
	Publisher	Springer	Place of Publication	Berlin	Editor	Vitria, Jordi; Sanches, João Miguel Raposo; Hernández, Mario
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title	LNCS
	Series Volume		Series Issue		Edition
	ISSN		ISBN	978-3-642-21256-7	Medium
	Area		Expedition		Conference	IbPRIA
	Notes	DAG			Approved	no
	Call Number	Admin @ si @ GVB2011b			Serial	1744
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	Author	Jaume Gibert; Ernest Valveny; Oriol Ramos Terrades; Horst Bunke
	Title	Multiple Classifiers for Graph of Words Embedding			Type	Conference Article
	Year	2011	Publication	10th International Conference on Multiple Classifier Systems	Abbreviated Journal
	Volume	6713	Issue		Pages	36-45
	Keywords
	Abstract	During the last years, there has been an increasing interest in applying the multiple classifier framework to the domain of structural pattern recognition. Constructing base classifiers when the input patterns are graph based representations is not an easy problem. In this work, we make use of the graph embedding methodology in order to construct different feature vector representations for graphs. The graph of words embedding assigns a feature vector to every graph by counting unary and binary relations between node representatives and combining these pieces of information into a single vector. Selecting different node representatives leads to different vectorial representations and therefore to different base classifiers that can be combined. We experimentally show how this methodology significantly improves the classification of graphs with respect to single base classifiers.
	Address	Napoles, Italy
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor	Carlo Sansone; Josef Kittler; Fabio Roli
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title	LNCS
	Series Volume		Series Issue		Edition
	ISSN		ISBN	978-3-642-21556-8	Medium
	Area		Expedition		Conference	MCS
	Notes	DAG			Approved	no
	Call Number	Admin @ si @GVR2011			Serial	1745
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	Author	Sergio Escalera; David M.J. Tax; Oriol Pujol; Petia Radeva; Robert P.W. Duin
	Title	Multi-Class Classification in Image Analysis Via Error-Correcting Output Codes			Type	Book Chapter
	Year	2011	Publication	Innovations in Intelligent Image Analysis	Abbreviated Journal
	Volume	339	Issue		Pages	7-29
	Keywords
	Abstract	A common way to model multi-class classification problems is by means of Error-Correcting Output Codes (ECOC). Given a multi-class problem, the ECOC technique designs a codeword for each class, where each position of the code identifies the membership of the class for a given binary problem.A classification decision is obtained by assigning the label of the class with the closest code. In this paper, we overview the state-of-the-art on ECOC designs and test them in real applications. Results on different multi-class data sets show the benefits of using the ensemble of classifiers when categorizing objects in images.
	Address
	Corporate Author				Thesis
	Publisher	Springer Berlin Heidelberg	Place of Publication	Berlin	Editor	H. Kawasnicka; L.Jain
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	1860-949X	ISBN	978-3-642-17933-4	Medium
	Area		Expedition		Conference
	Notes	MILAB;HuPBA			Approved	no
	Call Number	Admin @ si @ ETP2011			Serial	1746
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