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Related Experiment Video

Updated: Jul 11, 2025

Reefshape: A System for the Efficient Collection and Automated Processing of Time-Series Underwater Photogrammetry Data for Benthic Habitat Monitoring

Reefshape: A System for the Efficient Collection and Automated Processing of Time-Series Underwater Photogrammetry Data for Benthic Habitat Monitoring

Published on: June 13, 2025

Machine learning framework for precise localization of bleached corals using bag-of-hybrid visual feature

Fawad¹, Iftikhar Ahmad¹, Arif Ullah¹

¹Department of Computer Engineering, Chosun University, Gwangju, 61452, Republic of Korea.

Scientific Reports

|November 9, 2023

Summary

This summary is machine-generated.

Researchers developed a robust method to locate bleached corals using hybrid visual features and deep learning, achieving 96.20% accuracy. This technique improves marine pharmacognosy research by accurately identifying coral bleaching in the Great Barrier Reef.

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Last Updated: Jul 11, 2025

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Multimodal Optical Microscopy Methods Reveal Polyp Tissue Morphology and Structure in Caribbean Reef Building Corals

Multimodal Optical Microscopy Methods Reveal Polyp Tissue Morphology and Structure in Caribbean Reef Building Corals

Published on: September 5, 2014

Area of Science:

Marine Biology
Computer Vision
Artificial Intelligence

Background:

Coral reefs are vital underwater ecosystems threatened by temperature-sensitive bleaching.
Bleached corals impact marine pharmacognosy, necessitating accurate localization for restoration efforts.
Existing visual classification methods struggle with varying illumination, orientation, scale, and view angles.

Purpose of the Study:

To develop a highly noise-robust and invariant localization method for bleached corals.
To improve the accuracy and reliability of bleached coral detection in the Great Barrier Reef.
To address the limitations of current visual classification techniques.

Main Methods:

Implemented a localization using bag-of-hybrid visual features (RL-BoHVF) approach.
Employed AlexNet deep neural network (DNN) combined with ColorTexture handcrafted features.
Utilized a bag-of-feature method to reduce dimensionality and enhance robustness.

Main Results:

Achieved a classification accuracy of 96.20% on a balanced dataset from the Great Barrier Reef.
Demonstrated superior localization performance compared to existing standalone and hybrid models.
Evaluated the model on 342 images, confirming its effectiveness across train and test segments.

Conclusions:

The proposed RL-BoHVF method offers a significant advancement in bleached coral localization.
The hybrid approach combining DNN and handcrafted features provides high accuracy and robustness.
This technique supports critical research in marine conservation and pharmacognosy.