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Author
Gemma Sanchez; Josep Llados; K. Tombre
Title
A mean string algorithm to compute the average among a set of 2D shapes
Type
Journal Article
Year
2002
Publication
Pattern Recognition Letters
Abbreviated Journal
PRL
Volume
23
Issue
1-3
Pages
203–214
Keywords
Abstract
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
Notes
DAG; IF: 0.409
Approved
no
Call Number
DAG @ dag @ SLT2002
Serial
275
Permanent link to this record
Author
Gemma Sanchez; Alicia Fornes; Joan Mas; Josep Llados
Title
Computer Vision Tools for Visually Impaired Children Learning
Type
Journal
Year
2007
Publication
Abbreviated Journal
Volume
Issue
Pages
Keywords
Abstract
Address
Corporate Author
Thesis
Publisher
Place of Publication
Editor
Language
Summary Language
Original Title
Series Editor
Series Title
Abbreviated Series Title
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Series Issue
Edition
ISSN
ISBN
Medium
Area
Expedition
Conference
Notes
DAG
Approved
no
Call Number
DAG @ dag @ SFM2007b
Serial
892
Permanent link to this record
Author
Gemma Sanchez; Alicia Fornes; Joan Mas; Josep Llados
Title
Computer Vision Tools for Visually Impaired Children Learning
Type
Journal
Year
2007
Publication
Abbreviated Journal
Volume
Issue
Pages
Keywords
Abstract
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
Notes
DAG
Approved
no
Call Number
DAG @ dag @ SFM2007a
Serial
891
Permanent link to this record
Author
Oriol Ramos Terrades; Ernest Valveny; Salvatore Tabbone
Title
Optimal Classifier Fusion in a Non-Bayesian Probabilistic Framework
Type
Journal Article
Year
2009
Publication
IEEE Transactions on Pattern Analysis and Machine Intelligence
Abbreviated Journal
TPAMI
Volume
31
Issue
9
Pages
1630–1644
Keywords
Abstract
The combination of the output of classifiers has been one of the strategies used to improve classification rates in general purpose classification systems. Some of the most common approaches can be explained using the Bayes' formula. In this paper, we tackle the problem of the combination of classifiers using a non-Bayesian probabilistic framework. This approach permits us to derive two linear combination rules that minimize misclassification rates under some constraints on the distribution of classifiers. In order to show the validity of this approach we have compared it with other popular combination rules from a theoretical viewpoint using a synthetic data set, and experimentally using two standard databases: the MNIST handwritten digit database and the GREC symbol database. Results on the synthetic data set show the validity of the theoretical approach. Indeed, results on real data show that the proposed methods outperform other common combination schemes.
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
0162-8828
ISBN
Medium
Area
Expedition
Conference
Notes
DAG
Approved
no
Call Number
DAG @ dag @ RVT2009
Serial
1220
Permanent link to this record
Author
Marçal Rusiñol; Josep Llados
Title
A Performance Evaluation Protocol for Symbol Spotting Systems in Terms of Recognition and Location Indices
Type
Journal Article
Year
2009
Publication
International Journal on Document Analysis and Recognition
Abbreviated Journal
IJDAR
Volume
12
Issue
2
Pages
83-96
Keywords
Performance evaluation; Symbol Spotting; Graphics Recognition
Abstract
Symbol spotting systems are intended to retrieve regions of interest from a document image database where the queried symbol is likely to be found. They shall have the ability to recognize and locate graphical symbols in a single step. In this paper, we present a set of measures to evaluate the performance of a symbol spotting system in terms of recognition abilities, location accuracy and scalability. We show that the proposed measures allow to determine the weaknesses and strengths of different methods. In particular we have tested a symbol spotting method based on a set of four different off-the-shelf shape descriptors.
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
1433-2833
ISBN
Medium
Area
Expedition
Conference
Notes
DAG
Approved
no
Call Number
DAG @ dag @ RuL2009a
Serial
1166
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