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Multi-View Acoustic Sizing and Classification of Individual Fish

  • P. L. D. RobertsEmail author
  • J.S. Jaffe
Conference paper
Part of the Acoustical Imaging book series (ACIM, volume 30)

Abstract

Estimating biophysical parameters of fish populations in situ such as size, orientation, shape, and taxa is a fundamental goal in oceanography. Towards this end, acoustics is a natural choice due to its rapid, non-invasive capabilities. Here, multi-view methods are explored for classification, size and orientation estimation, and 2D image reconstruction for individual fish. Size- and shape-based classification using multi-view data is shown to be accurate (~10% error) using kernel methods and discriminant analysis. For species-based classification in the absence of significant differences in size or shape, multi-view methods offer significant (~40%) reduction in error, but absolute error rates remain high (~20%) due to the lack of discriminant information in acoustic scatter. Length and orientation estimation are investigated using a parameter-based approach with a simple ellipsoidal scattering model. Good accuracy is obtained when the views span the full 360°. When the span is limited to less than 60°, incorporating a prior constraint on possible body shapes can lead to reduced uncertainty in the estimated parameters. Finally, using views that span the full 360°, sparse Bayesian learning coupled with a conventional Radon transform yields accurate two-dimensional, projected images of the fish.

Keywords

Acoustics Fish classification Scattering Size estimation Radon transform Bayesian learning Distorted wave Born approximation 

Notes

Acknowledgements

This work was supported by the California Sea Grant. The authors would like to thank Eddie Kisfaludy, Robert Glatts, Fernando Simonet, Ben Maurer, Erdem Karakoylu, and the SIO machine shop for help with the design and implementation of these laboratory experiments.

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Copyright information

© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  1. 1.Department of Electrical and Computer EngineeringUniversity of CaliforniaSan DiegoUSA
  2. 2.Department of Electrical and Computer EngineeringUniversity of California San DiegoSan DiegoUSA

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