Contour based split and merge segmentation and pre-classification of zooplankton in very large images

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 non-zooplankton 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.

Authors

Institutions

Publication Details

Journal
Fraunhofer-Gesellschaft zur Förderung der angewandten Forschung e.V
Published
2025-04-17
DOI
https://doi.org/10.24406/publica-r-383918
Primary Topic
Water Quality Monitoring Technologies
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Contour based split and merge segmentation and pre-classification of zooplankton in very large images

Tim Dolereit, Christian Scheel, Enrico Gutzeit, Matthias Rust
Fraunhofer-Gesellschaft zur Förderung der angewandten Forschung e.V
Water Quality Monitoring Technologies
article

Contour based split and merge segmentation and pre-classification of zooplankton in very large images

Tim Dolereit, Christian Scheel, Enrico Gutzeit, Matthias Rust
article en

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 non-zooplankton 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.

Fraunhofer-Gesellschaft zur Förderung der angewandten Forschung e.VVol. 1
Fraunhofer Institute for Computer Graphics Research (DE), University of Rostock (DE)
Life below water
Openalex Percentile: Top 100%
Water Quality Monitoring Technologies
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Contour based split and merge segmentation and pre-classification of zooplankton in very large images — Tim Dolereit, Christian Scheel, et al. · Fraunhofer-Gesellschaft zur Förderung der angewandten Forschung e.V (2025) | TGRS Research Map | TGRS