Nonlinear correlation by using invariant identity vectors signatures to identify different species of fish

Claudia Fimbres-Castro; Josué Álvarez-Borrego

doi:10.1117/12.2025793

18 November 2013 Nonlinear correlation by using invariant identity vectors signatures to identify different species of fish

Claudia Fimbres-Castro, Josué Álvarez-Borrego

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Proceedings Volume 8785, 8th Iberoamerican Optics Meeting and 11th Latin American Meeting on Optics, Lasers, and Applications; 8785E2 (2013) https://doi.org/10.1117/12.2025793
Event: 8th Ibero American Optics Meeting/11th Latin American Meeting on Optics, Lasers, and Applications, 2013, Porto, Portugal

Abstract

In this work a new methodology to recognize objects is presented. This system is invariant to position, rotation and scale by using identity vectors signatures I_s obtained for both the target and the problem image. In this application, I_s are obtained by means of a simplification of the main features of the original image in addition of the properties of the Fourier transform. The nonlinear correlation by using a k^th law is used to obtain the digital correlation providing information on the similarity between different objects besides their great capacity to discriminate them even when are very similar. This new methodology recognizes objects in a more simple way providing a significant reduction of the image information of size m x n to one-dimensional vector of 1 x 256 consequently with low computational cost of approximately 0.02 s per image. In addition, the statistics of Euclidean distances is used as an alternative methodology for comparison of identity vectors signatures. Also, experiments were carried out in order to find the noise tolerance. The invariant to position, rotation and scale of this digital system was tested with different species of fish (real images). The results obtained have a confidence level above 95.4%.

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Claudia Fimbres-Castro and Josué Álvarez-Borrego "Nonlinear correlation by using invariant identity vectors signatures to identify different species of fish", Proc. SPIE 8785, 8th Iberoamerican Optics Meeting and 11th Latin American Meeting on Optics, Lasers, and Applications, 8785E2 (18 November 2013); https://doi.org/10.1117/12.2025793

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KEYWORDS

Nonlinear dynamics

Nonlinear filtering

Fourier transforms

Digital imaging

Image filtering

Linear filtering

Pattern recognition

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