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Viola–Jones Algorithm in a Bioindicative Holographic Experiment with Daphnia magna Population
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Viola–Jones Algorithm in a Bioindicative Holographic Experiment with Daphnia magna Population
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Journal Article
Viola–Jones Algorithm in a Bioindicative Holographic Experiment with Daphnia magna Population
2025
Overview
This study considers the applicability and effectiveness of the Viola–Jones method to automatically distinguish zooplankton particles from the background in images reconstructed from digital holograms obtained in natural conditions. For the first time, this algorithm is applied to holographic images containing coherent noise and residual defocusing. The method was trained on 880 annotated (marked) holographic images of Daphnia magna along with 120 background frames. It was then tested on independent laboratory and field datasets, including morphologically related taxa. With optimized settings, the precision of the algorithm reached ~90% and F1~85% on noisy holographic images, and the algorithm also demonstrated the preliminary ability to recognize similar taxa without retraining. The algorithm is well suited for analyzing holographic data as a fast and resource-efficient pre-filter—it effectively separates particles from the background and thereby allows subsequent classification or its application in real-time aquatic environment monitoring systems. The article presents experimental results demonstrating the efficiency of this algorithm during plankton monitoring in situ.
Publisher
MDPI AG
Subject
