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Author Francesco Ciompi; Oriol Pujol; Carlo Gatta; Xavier Carrillo; J. 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 (up) 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
Permanent link to this record
 
 
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 (up) 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
Permanent link to this record
 
 
Author Simone Balocco; Carlo Gatta; Francesco Ciompi; Oriol Pujol; Xavier Carrillo; J. 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 (up) 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
Permanent link to this record
 
 
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 (up) 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
Permanent link to this record
 
 
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 (up) 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
Permanent link to this record
 
 
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 (up) 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
Permanent link to this record
 
 
Author Nataliya Shapovalova; Wenjuan Gong; Marco Pedersoli; Xavier Roca; Jordi Gonzalez
Title On Importance of Interactions and Context in Human Action Recognition Type Conference Article
Year 2011 Publication 5th Iberian Conference on Pattern Recognition and Image Analysis Abbreviated Journal
Volume 6669 Issue Pages 58-66
Keywords
Abstract This paper is focused on the automatic recognition of human events in static images. Popular techniques use knowledge of the human pose for inferring the action, and the most recent approaches tend to combine pose information with either knowledge of the scene or of the objects with which the human interacts. Our approach makes a step forward in this direction by combining the human pose with the scene in which the human is placed, together with the spatial relationships between humans and objects. Based on standard, simple descriptors like HOG and SIFT, recognition performance is enhanced when these three types of knowledge are taken into account. Results obtained in the PASCAL 2010 Action Recognition Dataset demonstrate that our technique reaches state-of-the-art results using simple descriptors and classifiers.
Address Las Palmas de Gran Canaria. Spain
Corporate Author Thesis
Publisher Springer Berlin Heidelberg Place of Publication Editor J. Vitria, J.M. Sanches, and M. Hernandez
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title (up) LNCS
Series Volume Series Issue Edition
ISSN 0302-9743 ISBN 978-3-642-21256-7 Medium
Area Expedition Conference IbPRIA
Notes ISE Approved no
Call Number Admin @ si @ SGP2011 Serial 1750
Permanent link to this record
 
 
Author Carlo Gatta; Simone Balocco; Victoria Martin Yuste; Ruben Leta; Petia Radeva
Title Non-rigid Multi-modal Registration of Coronary Arteries Using SIFTflow Type Conference Article
Year 2011 Publication 5th Iberian Conference on Pattern Recognition and Image Analysis Abbreviated Journal
Volume 6669 Issue Pages 159-166
Keywords
Abstract The fusion of clinically relevant information coming from different image modalities is an important topic in medical imaging. In particular, different cardiac imaging modalities provides complementary information for the physician: Computer Tomography Angiography (CTA) provides reliable pre-operative information on arteries geometry, even in the presence of chronic total occlusions, while X-Ray Angiography (XRA) allows intra-operative high resolution projections of a specific artery. The non-rigid registration of arteries between these two modalities is a difficult task. In this paper we propose the use of SIFTflow, in registering CTA and XRA images. At the best of our knowledge, this paper proposed SIFTflow as a XRay-CTA registration method for the first time in the literature. To highlight the arteries, so to guide the registration process, the well known Vesselness method has been employed. Results confirm that, to the aim of registration, the arteries must be highlighted and background objects removed as much as possible. Moreover, the comparison with the well known Free Form Deformation technique, suggests that SIFTflow has a great potential in the registration of multi-modal medical images.
Address Las Palmas de Gran Canaria. Spain
Corporate Author Thesis
Publisher Springer Berlin Heidelberg Place of Publication Berlin Editor Jordi Vitria; Joao Miguel Sanches; Mario Hernandez
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title (up) LNCS
Series Volume Series Issue Edition
ISSN 0302-9743 ISBN 978-3-642-21256-7 Medium
Area Expedition Conference IbPRIA
Notes MILAB Approved no
Call Number Admin @ si @ GBM2011 Serial 1752
Permanent link to this record
 
 
Author Pierluigi Casale; Oriol Pujol; Petia Radeva
Title Approximate Convex Hulls Family for One-Class Cassification Type Conference Article
Year 2011 Publication 10th International Workshop on Multiple Classifier Systems Abbreviated Journal
Volume 6713 Issue Pages 106-115
Keywords
Abstract In this work, a new method for one-class classification based on the Convex Hull geometric structure is proposed. The new method creates a family of convex hulls able to fit the geometrical shape of the training points. The increased computational cost due to the creation of the convex hull in multiple dimensions is circumvented using random projections. This provides an approximation of the original structure with multiple bi-dimensional views. In the projection planes, a mechanism for noisy points rejection has also been elaborated and evaluated. Results show that the approach performs considerably well with respect to the state the art in one-class classification.
Address Napoli, Italy
Corporate Author Thesis
Publisher Springer Berlin Heidelberg Place of Publication Editor Carlo Sansone; Josef Kittler; Fabio Roli
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title (up) LNCS
Series Volume Series Issue Edition
ISSN 0302-9743 ISBN 978-3-642-21556-8 Medium
Area Expedition Conference MCS
Notes MILAB;HuPBA Approved no
Call Number Admin @ si @ CPR2011b Serial 1761
Permanent link to this record
 
 
Author Miguel Angel Bautista; Oriol Pujol; Xavier Baro; Sergio Escalera
Title Introducing the Separability Matrix for Error Correcting Output Codes Coding Type Conference Article
Year 2011 Publication 10th International conference on Multiple Classifier Systems Abbreviated Journal
Volume 6713 Issue Pages 227-236
Keywords
Abstract Error Correcting Output Codes (ECOC) have demonstrate to be a powerful tool for treating multi-class problems. Nevertheless, predefined ECOC designs may not benefit from Error-correcting principles for particular multi-class data. In this paper, we introduce the Separability matrix as a tool to study and enhance designs for ECOC coding. In addition, a novel problem-dependent coding design based on the Separability matrix is tested over a wide set of challenging multi-class problems, obtaining very satisfactory results.
Address Napoles, Italy
Corporate Author Thesis
Publisher Springer-Verlag Berlin Heidelberg Place of Publication Editor Carlo Sansone; Josef Kittler; Fabio Roli
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title (up) LNCS
Series Volume Series Issue Edition
ISSN ISBN 978-3-642-21556-8 Medium
Area Expedition Conference MCS
Notes MILAB; OR;HuPBA;MV Approved no
Call Number Admin @ si @ BPB2011a Serial 1771
Permanent link to this record
 
 
Author Carlo Gatta; Eloi Puertas; Oriol Pujol
Title Multi-Scale Stacked Sequential Learning Type Journal Article
Year 2011 Publication Pattern Recognition Abbreviated Journal PR
Volume 44 Issue 10-11 Pages 2414-2416
Keywords Stacked sequential learning; Multiscale; Multiresolution; Contextual classification
Abstract One of the most widely used assumptions in supervised learning is that data is independent and identically distributed. This assumption does not hold true in many real cases. Sequential learning is the discipline of machine learning that deals with dependent data such that neighboring examples exhibit some kind of relationship. In the literature, there are different approaches that try to capture and exploit this correlation, by means of different methodologies. In this paper we focus on meta-learning strategies and, in particular, the stacked sequential learning approach. The main contribution of this work is two-fold: first, we generalize the stacked sequential learning. This generalization reflects the key role of neighboring interactions modeling. Second, we propose an effective and efficient way of capturing and exploiting sequential correlations that takes into account long-range interactions by means of a multi-scale pyramidal decomposition of the predicted labels. Additionally, this new method subsumes the standard stacked sequential learning approach. We tested the proposed method on two different classification tasks: text lines classification in a FAQ data set and image classification. Results on these tasks clearly show that our approach outperforms the standard stacked sequential learning. Moreover, we show that the proposed method allows to control the trade-off between the detail and the desired range of the interactions.
Address
Corporate Author Thesis
Publisher Elsevier Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title (up) LNCS
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes MILAB;HuPBA Approved no
Call Number Admin @ si @ GPP2011 Serial 1802
Permanent link to this record
 
 
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 (up) 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
Permanent link to this record
 
 
Author Miguel Angel Bautista; Oriol Pujol; Xavier Baro; Sergio Escalera
Title Introducing the Separability Matrix for Error Correcting Output Codes Coding Type Conference Article
Year 2011 Publication 10th International Conference on Multiple Classifier Systems Abbreviated Journal
Volume 6713 Issue Pages 227-236
Keywords
Abstract Error Correcting Output Codes (ECOC) have demonstrate to be a powerful tool for treating multi-class problems. Nevertheless, predefined ECOC designs may not benefit from Error-correcting principles for particular multi-class data. In this paper, we introduce the Separability matrix as a tool to study and enhance designs for ECOC coding. In addition, a novel problem-dependent coding design based on the Separability matrix is tested over a wide set of challenging multi-class problems, obtaining very satisfactory results.
Address Napoles, Italy
Corporate Author Thesis
Publisher Springer-Verlag Berlin, Heidelberg Place of Publication Editor Carlo Sansone; Josef Kittler; Fabio Roli
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title (up) LNCS
Series Volume Series Issue Edition
ISSN 0302-9743 ISBN 978-3-642-21556-8 Medium
Area Expedition Conference MCS
Notes MILAB; OR;HuPBA;MV Approved no
Call Number Admin @ si @ BPB2011b Serial 1887
Permanent link to this record