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Marine and Freshwater Research Marine and Freshwater Research Society
Advances in the aquatic sciences
RESEARCH ARTICLE

A new method for robust feature extraction of otolith growth marks using fingerprint recognition methods

M. Palmer A B , A. Álvarez A , J. Tomás A and B. Morales-Nin A
+ Author Affiliations
- Author Affiliations

A IMEDEA (CSIC - UIB), Instituto Mediterráneo de Estudios Avanzados, Miquel Marqués, 21, 07190 Esporles (Balearic Islands), Spain.

B Corresponding author. Email: ieampv@uib.es

Marine and Freshwater Research 56(5) 791-794 https://doi.org/10.1071/MF04207
Submitted: 6 August 2004  Accepted: 6 April 2005   Published: 24 July 2005

Abstract

Individual and population age structures constitute essential knowledge for proper management of commercial fisheries. Despite the important advances made in age determination using otolith growth structures, there is still a need to improve both precision and accuracy. The problem of increasing precision in age estimations has been addressed via increasing automation in the identification of growth marks. However, approaches based on otolith size, weight, perimeter, and related measurements (including contour analysis) have moderate success in age prediction. Likewise, early attempts of image analysis have reported poor results, both in cases of 1D (grey-intensity profiles) or 2D images. Recent developments in image analysis have broken this trend, and fully automatic techniques could be an alternative for routine ageing in the near future. Here, we propose a new method for 2D feature extraction that provides robust numerical descriptors of the growth structures of otoliths.

Extra keyword: image-analysis.


References

Campana, S. E. (2001). Accuracy, precision and quality control in age determination, including a review of the use and abuse of age validation methods. Journal of Fish Biology 59, 197–242.
Crossref | GoogleScholarGoogle Scholar | Morales-Nin B., and Panfili J. (2002). IV. Validation and verification methods. D. Verification. In ‘Manual of Schlerochronology in Fishes’. (Eds J. Panfili, H. Troadec, H. de Puntual and P. J. Wright.) pp. 129–141. (IFREMER-IRD: Brest.)

Robertson, S. G. , and Morison, A. K. (1999). A trial of artificial neural networks for automatically estimating the age of fish. Marine and Freshwater Research 50, 73–82.
Crossref | GoogleScholarGoogle Scholar | Shen W., and Eshera M. A. (2004). Feature extraction in fingerprint images. In ‘Automatic Fingerprint Recognition Systems’. (Eds N. Ratha and R. Bolle.) pp. 145–181. (Springer-Verlag: New York.)

Torrence, C. , and Compo, G. P. (1998). A practical guide to Wavelet analysis. Bulletin of the American Meteorological Society 79, 61–78.
Crossref | GoogleScholarGoogle Scholar | Troadec H., and Benzinou A. (2002). VI. Computer-assited age estimation. In ‘Manual of Schlerochronology in Fishes’. (Eds J. Panfili, H. Troadec, H. de Puntual and P. J. Wright.) pp. 199–242. (IFREMER-IRD: Brest.)