Editors:
Part of the book series: Lecture Notes in Computer Science (LNCS, volume 11351)
Part of the book sub series: Image Processing, Computer Vision, Pattern Recognition, and Graphics (LNIP)
Conference series link(s): ICPRAM: International Conference on Pattern Recognition Applications and Methods
Conference proceedings info: ICPRAM 2018.
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Table of contents (10 papers)
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Front Matter
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Theory and Methods
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Front Matter
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Applications
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Front Matter
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Back Matter
About this book
This book contains revised and extended versions of selected papers from the 7th International Conference on Pattern Recognition, ICPRAM 2018, held in Porto, Portugal, in January 2018. The 10 full papers presented were carefully reviewed and selected from 102 initial submissions.
The core of ICPRAM is intended to include theoretical studies yielding new insights in Pattern Recognition methods, as well as experimental validation and concrete application of Pattern Recognition techniques to real-world problems.
Editors and Affiliations
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Computer Science, Sapienza University of Rome, Rome, Italy
Maria De Marsico
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ICAR-CNR, Naples, Italy
Gabriella Sanniti di Baja
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University of Lisbon, Lisbon, Portugal
Ana Fred
Bibliographic Information
Book Title: Pattern Recognition Applications and Methods
Book Subtitle: 7th International Conference, ICPRAM 2018, Funchal, Madeira, Portugal, January 16-18, 2018, Revised Selected Papers
Editors: Maria De Marsico, Gabriella Sanniti di Baja, Ana Fred
Series Title: Lecture Notes in Computer Science
DOI: https://doi.org/10.1007/978-3-030-05499-1
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer Nature Switzerland AG 2019
Softcover ISBN: 978-3-030-05498-4Published: 05 January 2019
eBook ISBN: 978-3-030-05499-1Published: 04 January 2019
Series ISSN: 0302-9743
Series E-ISSN: 1611-3349
Edition Number: 1
Number of Pages: XVI, 203
Number of Illustrations: 59 b/w illustrations, 92 illustrations in colour
Topics: Pattern Recognition, Image Processing and Computer Vision, Machine Learning