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Contour based Split and Merge Segmentation and Pre-classification of Zooplankton in Very Large Images
Author(s) -
Enrico Gutzeit,
Christian Scheel,
Tim Dolereit,
Matthias Rust
Publication year - 2014
Publication title -
2014 international conference on computer vision theory and applications (visapp)
Language(s) - English
Resource type - Conference proceedings
DOI - 10.5220/0004648604170424
Subject(s) - zooplankton , merge (version control) , computer science , segmentation , artificial intelligence , image segmentation , pattern recognition (psychology) , computer vision , ecology , information retrieval , biology
Zooplankton is an important component in the water ecosystem and food chain. To understand the influence of zooplankton on the ecosystem a data collection is necessary. In research the automatic image based recognition of zooplankton is of growing interest. Several systems have been developed for zooplankton recognition on low resolution images. For large images approaches are seldom. Images of this size easily exceed the main memory of standard computers. Our novel automatic segmentation approach is able to handle these large images. We developed a contour based Split & Merge approach for segmentation and, to reduce the nonzooplankton segments, combine it with a pre-classification of the segments in reference to their shape. The latter includes a detection of quasi round segments and a novel one for thin segments. Experimental results on several large images show that we are able to handle them satisfactorily

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