Title :
Segmentation and Classification of Coral for Oceanographic Surveys: A Semi-Supervised Machine Learning Approach
Author :
Johnson-Roberson, Matthew ; Kumar, Suresh ; Willams, Stefan
Author_Institution :
Univ. of Sydney, Sydney
Abstract :
This work presents a technique for the autonomous segmentation and classification of coral through the combination of visual and acoustic data. Autonomous Underwater Vehicles (AUVs) facilitate the live capture of multi-modal sensor information about coral reefs. Environmental monitoring of these reefs can be aided though the autonomous extraction and identification of certain coral species of interest. The technique presented employs a two phase procedure of segmentation and classification to gather statistics about coral density during autonomous missions with an AUV.
Keywords :
feature extraction; image classification; image segmentation; oceanographic techniques; remotely operated vehicles; underwater vehicles; AUV; Autonomous Underwater Vehicles; acoustic data; autonomous classification; autonomous extraction; autonomous identification; autonomous segmentation; coral density; coral reefs; environmental monitoring; multimodal sensor information; oceanographic surveys; semisupervised machine learning approach; statistics; visual data; Australia; Data mining; Image segmentation; Machine learning; Monitoring; Robot kinematics; Robot sensing systems; Statistics; Underwater acoustics; Underwater vehicles;
Conference_Titel :
OCEANS 2006 - Asia Pacific
Conference_Location :
Singapore
Print_ISBN :
978-1-4244-0138-3
Electronic_ISBN :
978-1-4244-0138-3
DOI :
10.1109/OCEANSAP.2006.4393835