Automatic Image-Based Coral Polyp Analysis through Multi-View Instance Segmentation
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Date
2025
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
Abstract
We present an automated framework for counting and measuring the polyps of Cladocora caespitosa, a Mediterranean reefbuilding coral. To our knowledge, the most practical method for counting polyps currently involves ecologists' visual inspection of a 3D model. However, measuring polyps from the model can lead to inaccuracies due to distortions in the reconstruction. Our method integrates deep learning-based instance segmentation on 2D images with 3D models for unique polyp identification, ensuring precise biometric extraction. The proposed pipeline automates polyp detection, counting, and measurement while overcoming the limitations of manual in situ methods. Laboratory validation demonstrates its accuracy and efficiency, paving the way for scalable, high-resolution phenotyping, and field monitoring of Mediterranean coral populations.
Description
CCS Concepts: Computing methodologies → Object recognition; Image segmentation; Shape analysis
@inproceedings{10.2312:egp.20251022,
booktitle = {Eurographics 2025 - Posters},
editor = {Günther, Tobias and Montazeri, Zahra},
title = {{Automatic Image-Based Coral Polyp Analysis through Multi-View Instance Segmentation}},
author = {Dutta, Somnath and Pavoni, Gaia and Cattini, Stefano and Rovati, Luigi and Capra, Alessandro and Castagnetti, Cristina and Corsini, Massimiliano and Ganovelli, Fabio and Cignoni, Paolo and Rossi, Paolo and Cenni, Elena and Simonini, Roberto and Grassi, Francesca and Cassanelli, Davide},
year = {2025},
publisher = {The Eurographics Association},
ISSN = {1017-4656},
ISBN = {978-3-03868-269-1},
DOI = {10.2312/egp.20251022}
}