Automatic Image-Based Coral Polyp Analysis through Multi-View Instance Segmentation

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dc.contributor.authorDutta, Somnathen_US
dc.contributor.authorPavoni, Gaiaen_US
dc.contributor.authorCorsini, Massimilianoen_US
dc.contributor.authorGanovelli, Fabioen_US
dc.contributor.authorCignoni, Paoloen_US
dc.contributor.authorRossi, Paoloen_US
dc.contributor.authorCenni, Elenaen_US
dc.contributor.authorSimonini, Robertoen_US
dc.contributor.authorGrassi, Francescaen_US
dc.contributor.authorCassanelli, Davideen_US
dc.contributor.authorCattini, Stefanoen_US
dc.contributor.authorRovati, Luigien_US
dc.contributor.authorCapra, Alessandroen_US
dc.contributor.authorCastagnetti, Cristinaen_US
dc.contributor.editorGünther, Tobiasen_US
dc.contributor.editorMontazeri, Zahraen_US
dc.date.accessioned2025-05-09T09:31:31Z
dc.date.available2025-05-09T09:31:31Z
dc.date.issued2025
dc.description.abstractWe 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.en_US
dc.description.sectionheadersPosters
dc.description.seriesinformationEurographics 2025 - Posters
dc.identifier.doi10.2312/egp.20251022
dc.identifier.isbn978-3-03868-269-1
dc.identifier.issn1017-4656
dc.identifier.pages2 pages
dc.identifier.urihttps://doi.org/10.2312/egp.20251022
dc.identifier.urihttps://diglib.eg.org/handle/10.2312/egp20251022
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectCCS Concepts: Computing methodologies → Object recognition; Image segmentation; Shape analysis
dc.subjectComputing methodologies → Object recognition
dc.subjectImage segmentation
dc.subjectShape analysis
dc.titleAutomatic Image-Based Coral Polyp Analysis through Multi-View Instance Segmentationen_US
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