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VeSTIS: A Versatile Semi- Automatic Taxon Identification System from Digital Images
Nikolaou, Nikos
Sampaziotis, Pantelis
Aplikioti, Marilena
Drakos, Andreas
Kirmitzoglou, Ioannis
Argyrou, Marina
Papamarkos, Nikos
Promponas, Vasilis J.
2010
Abstract
In this work we present a flexible Open Source software platform
for training classifiers capable of identifying the taxonomy of a specimen from
digital images. We demonstrate the performance of our system in a pilot
study, building a feed-forward artificial neural network to effectively classify
five different species of marine annelid worms of the class Polychaeta. We
also discuss on the extensibility of the system, and its potential uses either as
a research tool or in assisting routine taxon identification procedures.
Publisher
EUT Edizioni Università di Trieste
Source
Nikos Nikolaou [et al.], VeSTIS: A Versatile Semi- Automatic Taxon Identification System from Digital Images, in Pier Luigi Nimis and Régine Vignes Lebbe (eds.): “Tools for Identifying Biodiversity: Progress and Problems. Proceedings of the International Congress, Paris, September 20-22, 2010”, Trieste, EUT Edizioni Università di Trieste, 2010, pp. 231-236.
Languages
en
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