Kelp detection in highly dynamic environments using texture recognition

Denuelle, Aymeric & Dunbabin, Matthew (2010) Kelp detection in highly dynamic environments using texture recognition. In Wyeth, Gordon & Upcroft, Ben (Eds.) Proceedings of the 2010 Australasian Conference on Robotics and Automation, Australian Robotics and Automation Association, Brisbane, Queensland, Australia, pp. 1-8.

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Abstract

This paper describes a texture recognition based method for segmenting kelp from images collected in highly dynamic shallow water environments by an Autonomous Underwater Vehicle (AUV). A particular challenge is image quality that is affected by uncontrolled lighting, reduced visibility, significantly varying perspective due to platform egomotion, and kelp sway from wave action. The kelp segmentation approach uses the Mahalanobis distance as a way to classify Haralick texture features from sub-regions within an image. The results illustrate the applicability of the method to classify kelp allowing construction of probability maps of kelp masses across a sequence of images.

Impact and interest:

1 citations in Scopus
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ID Code: 68821
Item Type: Conference Paper
Refereed: Yes
Keywords: Image processing, Texture recognition, Kelp detection, Dynamic environments, Autonomous Underwater Vehicle, Reduced visibility, Uncontrolled lighting
ISBN: 9780980740417
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2010 Australian Robotics and Automation Association Inc.
Deposited On: 19 Mar 2014 23:12
Last Modified: 02 Apr 2014 07:24

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