Authors:
Enrico Gutzeit
1
;
Christian Scheel
2
;
Tim Dolereit
1
and
Matthias Rust
3
Affiliations:
1
Fraunhofer Institute for Computer Graphics Research IGD, Germany
;
2
University of Rostock, Germany
;
3
Arivis AG, Germany
Keyword(s):
Segmentation, Contour, Split & Merge, Pre-Classification, Shape Features, Zooplankton, Large Images
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Early and Biologically-Inspired Vision
;
Image and Video Analysis
;
Segmentation and Grouping
;
Shape Representation and Matching
Abstract:
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. Experiment results on several huge images show that we are able to handle this huge images satisfactory.