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Automated Pattern Recognition of Phytoplankton – Procedure and Results
Author(s) -
Schlimpert Olaf,
Uhlmann Dietrich,
Schüller Martina,
Höhne Erhard
Publication year - 1980
Publication title -
internationale revue der gesamten hydrobiologie und hydrographie
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.524
H-Index - 52
eISSN - 1522-2632
pISSN - 0020-9309
DOI - 10.1002/iroh.19800650311
Subject(s) - raster graphics , pattern recognition (psychology) , artificial intelligence , identification (biology) , feature (linguistics) , feature vector , computer science , grid , point (geometry) , computer vision , mathematics , ecology , biology , linguistics , philosophy , geometry
Pictures of phytoplankton samples were analyzed as raster images by means of a television camera and a Robotron 4200 computer. A feature vector described the objects irrespective of their angle. Each of the five genera involved were identifiable by a characteristic point cluster in a p ‐dimensional feature space. A learning method was used during development of the classification structure, and the quality of identification was increased incrementally to the greatest possible degree. Asterionella formosa was identified in all cases without error despite the relatively coarse scanning grid. Errors in the identification of Fragilaria crotonensis can be reduced by improving the resolution (over 100 picture elements per colony).