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Environmental data extraction from multimedia resources

 
: Moumtzidou, A.; Epitropou, V.; Vrochidis, S.; Voth, S.; Bassoukos, A.; Karatzas, K.; Moßgraber, Jürgen; Kompatsiaris, I.; Karppinen, A.; Kukkonen, J.

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Postprint urn:nbn:de:0011-n-2257732 (647 KByte PDF)
MD5 Fingerprint: 4afd95bce991aca3cdb8b156f45ae83e
© ACM 2012 This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution.
Created on: 31.1.2013


Association for Computing Machinery -ACM-:
1st ACM International Workshop on Multimedia Analysis for Ecological Data, MAED 2012. Prodeedings : 2 November, Nara, Japan In Conjuction with 20th ACM Multimedia 2012
New York: ACM, 2012
ISBN: 978-1-4503-1588-3
pp.13-18
International Workshop on Multimedia Analysis for Ecological Data (MAED) <1, 2012, Nara>
International Conference on Multimedia (MM) <20, 2012, Nara>
English
Conference Paper, Electronic Publication
Fraunhofer IOSB ()
environmental; multimedia; image; heatmap; OCR; data reconstruction; template; configuration; pollen; air quality

Abstract
Extraction and analysis of environmental information is very important, since it strongly affects everyday life. Nowadays there are already many free services providing environmental information in several formats including multimedia (e.g. map images). Although such presentation formats might be very informative for humans, they complicate the automatic extraction and processing of the underlying data. A very characteristic example is the air quality and pollen forecasts, which are usually encoded in image maps of heterogeneous formats, while the initial (numerical) pollutant concentrations, calculated by a relevant model, remain unavailable. This work proposes a framework for the semi-automatic extraction of such information based on a template configuration tool, on Optical Character Recognition (OCR) techniques and on methodologies for data reconstruction from images. The system is tested with a different air quality and pollen forecast heatmaps demonstrating promising results.

: http://publica.fraunhofer.de/documents/N-225773.html