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  • fiducial_error_minimisation_ices_r2

    Accepted author manuscript, 1 MB, PDF-document

    Embargo ends: 31/12/99


Videogrammetry and photogrammetry are increasingly being used in marine science for unsupervised data collection. The camera systems used to collect such data are varied and often complex. In contrast, digital cameras and smartphones are ubiquitous, convenient for the user and an image automatically captures much of the data normally recorded on paper as metadata. The limitations of such an approach are primarily attributed to errors introduced through both the image acquisition process and lens distortion of the collected images. In the present study, a methodology is presented to achieve accurate 2‑dimensional (2-D) total length (TL) estimates ofish without specialist camera equipment or proprietary software. Photographs of depressed (flat) and fusiform fish were captured with an action camera using a background fiducial marker, positioned at the distal plane of the subject; a foreground fiducial marker, at the proximal plane of the subject and a laser marker, projected on to the subject’s surface. To correct image distortions, the geometric properties of the lens were modelled with OpenCV. The accuracy of TL estimates were corrected for parallax effects using a novel iterative algorithm requiring only the initial length estimate and known morphometric relationships. OpenCV was effective in correcting image distortion, decreasing RMSE by 96% and the percentage mean bias error (%MBE) by 50%. By undistorting the image and correcting for parallax effects a % MBE [95% CIs] of -0.6% [-1.0, -0.3] was achieved and RMSE was reduced by 86% to 2.1%. Estimation of the lens to subject distance using the similar triangles calibration method resulted in the best estimation of TL. The present study demonstrates that the morphometric measurement of different species can be accurately estimated without the need for expensive, complex or bulky camera equipment making it particularly suitable for deployment in citizen science and other volunteer based data collection endeavours.
Original languageEnglish
JournalICES Journal of Marine Science
Early online date14 Mar 2019
Publication statusE-pub ahead of print - 14 Mar 2019
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