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Author |
Francesco Ciompi; Oriol Pujol; Carlo Gatta; Xavier Carrillo; J. Mauri; Petia Radeva |
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Title |
A Holistic Approach for the Detection of Media-Adventitia Border in IVUS |
Type |
Conference Article |
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Year |
2011 |
Publication |
14th International Conference on Medical Image Computing and Computer Assisted Intervention |
Abbreviated Journal |
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Volume |
6893 |
Issue |
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Pages |
401-408 |
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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. |
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Address |
Toronto, Canada |
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Publisher |
Springer Berlin Heidelberg |
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LNCS |
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Series Volume |
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Series Issue |
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Edition |
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ISSN |
0302-9743 |
ISBN |
978-3-642-23625-9 |
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Expedition |
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Conference |
MICCAI |
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Notes |
MILAB;HuPBA |
Approved |
no |
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Call Number |
Admin @ si @ CPG2011 |
Serial |
1739 |
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Permanent link to this record |
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Author |
Marina Alberti; Carlo Gatta; Simone Balocco; Francesco Ciompi; Oriol Pujol; Joana Silva; Xavier Carrillo; Petia Radeva |
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Title |
Automatic Branching Detection in IVUS Sequences |
Type |
Conference Article |
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Year |
2011 |
Publication |
5th Iberian Conference on Pattern Recognition and Image Analysis |
Abbreviated Journal |
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Volume |
6669 |
Issue |
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Pages |
126-133 |
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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. |
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Publisher |
Springer Berlin Heidelberg |
Place of Publication |
Berlin |
Editor |
Jordi Vitria; Joao Miguel Raposo; Mario Hernandez |
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Series Editor |
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Abbreviated Series Title |
LNCS |
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Series Volume |
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Series Issue |
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Edition |
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ISSN |
0302-9743 |
ISBN |
978-3-642-21256-7 |
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Expedition |
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Conference |
IbPRIA |
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Notes |
MILAB;HuPBA |
Approved |
no |
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Call Number |
Admin @ si @ AGB2011 |
Serial |
1740 |
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Permanent link to this record |
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Author |
Simone Balocco; Carlo Gatta; Francesco Ciompi; Oriol Pujol; Xavier Carrillo; J. Mauri; Petia Radeva |
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Title |
Combining Growcut and Temporal Correlation for IVUS Lumen Segmentation |
Type |
Conference Article |
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Year |
2011 |
Publication |
5th Iberian Conference on Pattern Recognition and Image Analysis |
Abbreviated Journal |
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Volume |
6669 |
Issue |
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Pages |
556-563 |
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Keywords |
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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. |
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Address |
Las Palmas de Gran Canaria. Spain |
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Corporate Author |
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Thesis |
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Publisher |
Springer Berlin Heidelberg |
Place of Publication |
Berlin |
Editor |
Jordi Vitria; Joao Miguel Raposo; Mario Hernandez |
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Series Editor |
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Abbreviated Series Title |
LNCS |
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Series Volume |
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Series Issue |
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Edition |
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ISSN |
0302-9743 |
ISBN |
978-3-642-21256-7 |
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Area |
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Expedition |
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Conference |
IbPRIA |
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Notes |
MILAB;HuPBA |
Approved |
no |
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Call Number |
Admin @ si @ BGC2011a |
Serial |
1741 |
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Permanent link to this record |
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Author |
Jaume Gibert; Ernest Valveny; Horst Bunke |
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Title |
Dimensionality Reduction for Graph of Words Embedding |
Type |
Conference Article |
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Year |
2011 |
Publication |
8th IAPR-TC-15 International Workshop. Graph-Based Representations in Pattern Recognition |
Abbreviated Journal |
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Volume |
6658 |
Issue |
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Pages |
22-31 |
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Keywords |
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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. |
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Address |
Münster, Germany |
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Editor |
Xiaoyi Jiang; Miquel Ferrer; Andrea Torsello |
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LNCS |
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Edition |
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ISSN |
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ISBN |
978-3-642-20843-0 |
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Expedition |
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Conference |
GbRPR |
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Notes |
DAG |
Approved |
no |
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Call Number |
Admin @ si @ GVB2011a |
Serial |
1743 |
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Permanent link to this record |
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Author |
Jaume Gibert; Ernest Valveny; Horst Bunke |
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Title |
Vocabulary Selection for Graph of Words Embedding |
Type |
Conference Article |
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Year |
2011 |
Publication |
5th Iberian Conference on Pattern Recognition and Image Analysis |
Abbreviated Journal |
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Volume |
6669 |
Issue |
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Pages |
216-223 |
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Keywords |
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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. |
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Address |
Las Palmas de Gran Canaria. Spain |
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Corporate Author |
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Thesis |
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Publisher |
Springer |
Place of Publication |
Berlin |
Editor |
Vitria, Jordi; Sanches, João Miguel Raposo; Hernández, Mario |
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Original Title |
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Series Editor |
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Series Title |
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Abbreviated Series Title |
LNCS |
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Series Volume |
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Series Issue |
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Edition |
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ISSN |
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ISBN |
978-3-642-21256-7 |
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Area |
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Expedition |
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Conference |
IbPRIA |
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Notes |
DAG |
Approved |
no |
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Call Number |
Admin @ si @ GVB2011b |
Serial |
1744 |
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Permanent link to this record |
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Author |
Jaume Gibert; Ernest Valveny; Oriol Ramos Terrades; Horst Bunke |
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Title |
Multiple Classifiers for Graph of Words Embedding |
Type |
Conference Article |
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Year |
2011 |
Publication |
10th International Conference on Multiple Classifier Systems |
Abbreviated Journal |
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Volume |
6713 |
Issue |
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Pages |
36-45 |
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Keywords |
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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. |
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Address |
Napoles, Italy |
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Corporate Author |
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Place of Publication |
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Editor |
Carlo Sansone; Josef Kittler; Fabio Roli |
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Original Title |
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Series Editor |
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Series Title |
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Abbreviated Series Title |
LNCS |
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Series Volume |
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Series Issue |
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Edition |
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ISSN |
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ISBN |
978-3-642-21556-8 |
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Area |
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Expedition |
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Conference |
MCS |
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Notes |
DAG |
Approved |
no |
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Call Number |
Admin @ si @GVR2011 |
Serial |
1745 |
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Permanent link to this record |
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Author |
Nataliya Shapovalova; Wenjuan Gong; Marco Pedersoli; Xavier Roca; Jordi Gonzalez |
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Title |
On Importance of Interactions and Context in Human Action Recognition |
Type |
Conference Article |
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Year |
2011 |
Publication |
5th Iberian Conference on Pattern Recognition and Image Analysis |
Abbreviated Journal |
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Volume |
6669 |
Issue |
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Pages |
58-66 |
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Keywords |
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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. |
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Address |
Las Palmas de Gran Canaria. Spain |
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Corporate Author |
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Thesis |
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Publisher |
Springer Berlin Heidelberg |
Place of Publication |
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Editor |
J. Vitria, J.M. Sanches, and M. Hernandez |
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Language |
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Summary Language |
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Original Title |
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Series Editor |
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Series Title |
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Abbreviated Series Title |
LNCS |
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Series Volume |
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Series Issue |
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Edition |
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ISSN |
0302-9743 |
ISBN |
978-3-642-21256-7 |
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Area |
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Expedition |
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Conference |
IbPRIA |
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Notes |
ISE |
Approved |
no |
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Call Number |
Admin @ si @ SGP2011 |
Serial |
1750 |
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Permanent link to this record |
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Author |
Carlo Gatta; Simone Balocco; Victoria Martin Yuste; Ruben Leta; Petia Radeva |
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Title |
Non-rigid Multi-modal Registration of Coronary Arteries Using SIFTflow |
Type |
Conference Article |
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Year |
2011 |
Publication |
5th Iberian Conference on Pattern Recognition and Image Analysis |
Abbreviated Journal |
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Volume |
6669 |
Issue |
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Pages |
159-166 |
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Keywords |
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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. |
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Address |
Las Palmas de Gran Canaria. Spain |
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Corporate Author |
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Thesis |
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Publisher |
Springer Berlin Heidelberg |
Place of Publication |
Berlin |
Editor |
Jordi Vitria; Joao Miguel Sanches; Mario Hernandez |
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Language |
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Summary Language |
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Original Title |
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Series Editor |
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Series Title |
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Abbreviated Series Title |
LNCS |
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Series Volume |
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Series Issue |
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Edition |
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ISSN |
0302-9743 |
ISBN |
978-3-642-21256-7 |
Medium |
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Area |
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Expedition |
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Conference |
IbPRIA |
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Notes |
MILAB |
Approved |
no |
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Call Number |
Admin @ si @ GBM2011 |
Serial |
1752 |
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Permanent link to this record |
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Author |
Pierluigi Casale; Oriol Pujol; Petia Radeva |
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Title |
Approximate Convex Hulls Family for One-Class Cassification |
Type |
Conference Article |
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Year |
2011 |
Publication |
10th International Workshop on Multiple Classifier Systems |
Abbreviated Journal |
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Volume |
6713 |
Issue |
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Pages |
106-115 |
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Keywords |
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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. |
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Address |
Napoli, Italy |
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Corporate Author |
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Thesis |
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Publisher |
Springer Berlin Heidelberg |
Place of Publication |
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Editor |
Carlo Sansone; Josef Kittler; Fabio Roli |
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Language |
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Summary Language |
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Original Title |
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Series Editor |
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Series Title |
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Abbreviated Series Title |
LNCS |
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Series Volume |
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Series Issue |
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Edition |
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ISSN |
0302-9743 |
ISBN |
978-3-642-21556-8 |
Medium |
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Area |
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Expedition |
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Conference |
MCS |
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Notes |
MILAB;HuPBA |
Approved |
no |
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Call Number |
Admin @ si @ CPR2011b |
Serial |
1761 |
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Permanent link to this record |
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Author |
Miguel Angel Bautista; Oriol Pujol; Xavier Baro; Sergio Escalera |
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Title |
Introducing the Separability Matrix for Error Correcting Output Codes Coding |
Type |
Conference Article |
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Year |
2011 |
Publication |
10th International conference on Multiple Classifier Systems |
Abbreviated Journal |
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Volume |
6713 |
Issue |
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Pages |
227-236 |
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Keywords |
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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. |
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Address |
Napoles, Italy |
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Corporate Author |
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Thesis |
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Publisher |
Springer-Verlag Berlin Heidelberg |
Place of Publication |
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Editor |
Carlo Sansone; Josef Kittler; Fabio Roli |
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Language |
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Summary Language |
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Original Title |
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Series Editor |
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Series Title |
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Abbreviated Series Title |
LNCS |
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Series Volume |
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Series Issue |
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Edition |
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ISSN |
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ISBN |
978-3-642-21556-8 |
Medium |
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Expedition |
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Conference |
MCS |
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Notes |
MILAB; OR;HuPBA;MV |
Approved |
no |
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Call Number |
Admin @ si @ BPB2011a |
Serial |
1771 |
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Permanent link to this record |
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Author |
Carlo Gatta; Eloi Puertas; Oriol Pujol |
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Title |
Multi-Scale Stacked Sequential Learning |
Type |
Journal Article |
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Year |
2011 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
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Volume |
44 |
Issue |
10-11 |
Pages |
2414-2416 |
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Keywords |
Stacked sequential learning; Multiscale; Multiresolution; Contextual classification |
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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. |
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Corporate Author |
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Thesis |
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Publisher |
Elsevier |
Place of Publication |
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Editor |
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Language |
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Summary Language |
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Original Title |
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Series Editor |
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Series Title |
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Abbreviated Series Title |
LNCS |
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Series Volume |
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Series Issue |
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Edition |
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ISSN |
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ISBN |
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Medium |
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Area |
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Expedition |
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Conference |
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Notes |
MILAB;HuPBA |
Approved |
no |
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Call Number |
Admin @ si @ GPP2011 |
Serial |
1802 |
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Permanent link to this record |
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Author |
Anjan Dutta; Josep Llados; Umapada Pal |
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Title |
Bag-of-GraphPaths Descriptors for Symbol Recognition and Spotting in Line Drawings |
Type |
Conference Article |
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Year |
2011 |
Publication |
In proceedings of 9th IAPR Workshop on Graphic Recognition |
Abbreviated Journal |
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Volume |
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Issue |
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Pages |
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Keywords |
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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. |
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Address |
Seoul, Korea |
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Corporate Author |
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Thesis |
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Publisher |
Springer Berlin Heidelberg |
Place of Publication |
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Editor |
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Language |
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Summary Language |
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Original Title |
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Series Editor |
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Series Title |
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Abbreviated Series Title |
LNCS |
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Series Volume |
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Series Issue |
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Edition |
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ISSN |
0302-9743 |
ISBN |
978-3-642-36823-3 |
Medium |
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Area |
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Expedition |
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Conference |
GREC |
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Notes |
DAG |
Approved |
no |
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Call Number |
Admin @ si @ DLP2011c |
Serial |
1825 |
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Permanent link to this record |
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Author |
Miguel Angel Bautista; Oriol Pujol; Xavier Baro; Sergio Escalera |
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Title |
Introducing the Separability Matrix for Error Correcting Output Codes Coding |
Type |
Conference Article |
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Year |
2011 |
Publication |
10th International Conference on Multiple Classifier Systems |
Abbreviated Journal |
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Volume |
6713 |
Issue |
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Pages |
227-236 |
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Keywords |
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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. |
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Address |
Napoles, Italy |
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Publisher |
Springer-Verlag Berlin, Heidelberg |
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Editor |
Carlo Sansone; Josef Kittler; Fabio Roli |
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LNCS |
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Edition |
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ISSN |
0302-9743 |
ISBN |
978-3-642-21556-8 |
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Conference |
MCS |
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Notes |
MILAB; OR;HuPBA;MV |
Approved |
no |
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Call Number |
Admin @ si @ BPB2011b |
Serial |
1887 |
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