Authors:
Anand Mehtaa
;
Eraldo Ribeiro
;
Jessica Gilner
and
Robert van Woesikb
Affiliation:
Florida Institute of Technology, United States
Keyword(s):
Coral reef characterization, machine vision applications, texture classification, texture segmentation.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computer Vision, Visualization and Computer Graphics
;
Data Manipulation
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Image and Video Analysis
;
Methodologies and Methods
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Segmentation and Grouping
;
Sensor Networks
;
Soft Computing
Abstract:
The development of tools to examine the ecological parameters of coral reefs is seriously lagging behind available computer-based technology. Until recently the use of images in environmental and ecological data gathering has been limited to terrestrial analysis because of difficulties in underwater image capture and data analysis. In this paper, we propose the application of computer vision to address the problem of monitoring and classifying coral reef colonies. More specifically, we present a method to classify coral reef images based on their textural appearance using support vector machines (SVM). Our algorithm uses raw pixel color values directly as sample vectors. We show promising results on region classification of three coral types for low quality underwater images. This will allow for more timely analysis of coral reef images and broaden the capabilities of underwater data interpretation.