No abstract

Abstract-The aim of this paper is to demonstrate the validity of using Gaussian mixture models (GMM) for representing probabilistic distributions in a decentralised data fusion (DDF) framework. GMMs are a powerful and compact stochastic representation allowing efficient communication of feature properties in large scale decentralised sensor networks. It will be shown that GMMs provide a basis for analytical solutions to the update and prediction operations for general Bayesian filtering. Furthermore, a variant on the Covariance Intersect algorithm for Gaussian mixtures will be presented ensuring a conservative update for the fusion of correlated information between two nodes in the network. In addition, purely visual sensory data will be used to show that decentralised data fusion and tracking of non-Gaussian states observed by multiple autonomous vehicles is feasible.

Stereoscopic imaging as a means of aiding the segmentation and classification of coral in underwater imagery. This work presents a framework for integrating range data into a formerly two dimensional classification pipeline. By taking an estimate of the rugosity and surface topography of segmented coral greater accuracy can be achieved in morphological discrimination of visually similar species. Utilizing these techniques allows for automatic processing of datasets and quicker turnaround in achieving the scientific goals of a survey. This work strives to produce a real-time system that is capable of segmenting and classifying coral.

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.