By Topic

Bayesian approach to object detection in sidescan sonar

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $31
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Calder, B.R. ; Dept. of Comput. & Electr. Eng., Heriot-Watt Univ., Edinburgh, UK ; Linnett, L.M. ; Carmichael, D.R.

The authors consider the problem of detecting discrete objects in sidescan sonar, utilising the geometric and statistical properties of the objects expressed in a Bayesian framework. The model proposed hypothesises that the observed image is formed through observations of a simple texturing process, modified to incorporate the presence of potential objects. Suitable priors are selected expressing geometric and spatial constraints, and a Markov chain Monte Carlo (MCMC) system is used to estimate texture labels and probability of object presence on a per pixel basis. The model and the method of parameter construction are described. Some examples of typical object detection are given, along with the results of a more detailed study on a groundtruth data set. It is concluded that the model proposed appears effective for this data set, and is flexible enough to be easily trained for use in others. Groundtruth object detection with a correct detection of 87% is observed, with a false alarm rate of 0.19/image

Published in:

Vision, Image and Signal Processing, IEE Proceedings -  (Volume:145 ,  Issue: 3 )