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Automated Image Analysis for the Detection of Benthic Crustaceans and Bacterial Mat Coverage Using the VENUS Undersea Cabled Network

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dc.contributor.author Aguzzi, Jacopo
dc.contributor.author Costa, Corrado
dc.contributor.author Robert, Katleen
dc.contributor.author Matabos, Marjolaine
dc.contributor.author Antonucci, Francesca
dc.contributor.author Juniper, S. Kim
dc.contributor.author Menesatti, Paolo
dc.date.accessioned 2020-10-05T17:58:17Z
dc.date.available 2020-10-05T17:58:17Z
dc.date.copyright 2011 en_US
dc.date.issued 2011
dc.identifier.citation Aguzzi, J., Costa, C., Robert, K., Matabos, M., Antonucci, F., Juniper, S. K., & Menesatti, P. (2011). Automated Image Analysis for the Deteftion of Benthic Crustaceans and Bacterial Mat Coverage Using the VENUS Undersea Cabled Network. Sensors, 11(11), 10534-10556. https://doi.org/10.3390/s111110534. en_US
dc.identifier.uri https://doi.org/10.3390/s111110534
dc.identifier.uri http://hdl.handle.net/1828/12166
dc.description.abstract The development and deployment of sensors for undersea cabled observatories is presently biased toward the measurement of habitat variables, while sensor technologies for biological community characterization through species identification and individual counting are less common. The VENUS cabled multisensory network (Vancouver Island, Canada) deploys seafloor camera systems at several sites. Our objective in this study was to implement new automated image analysis protocols for the recognition and counting of benthic decapods (i.e., the galatheid squat lobster, Munida quadrispina), as well as for the evaluation of changes in bacterial mat coverage (i.e., Beggiatoa spp.), using a camera deployed in Saanich Inlet (103 m depth). For the counting of Munida we remotely acquired 100 digital photos at hourly intervals from 2 to 6 December 2009. In the case of bacterial mat coverage estimation, images were taken from 2 to 8 December 2009 at the same time frequency. The automated image analysis protocols for both study cases were created in MatLab 7.1. Automation for Munida counting incorporated the combination of both filtering and background correction (Median- and Top-Hat Filters) with Euclidean Distances (ED) on Red-Green-Blue (RGB) channels. The Scale-Invariant Feature Transform (SIFT) features and Fourier Descriptors (FD) of tracked objects were then extracted. Animal classifications were carried out with the tools of morphometric multivariate statistic (i.e., Partial Least Square Discriminant Analysis; PLSDA) on Mean RGB (RGBv) value for each object and Fourier Descriptors (RGBv+FD) matrices plus SIFT and ED. The SIFT approach returned the better results. Higher percentages of images were correctly classified and lower misclassification errors (an animal is present but not detected) occurred. In contrast, RGBv+FD and ED resulted in a high incidence of records being generated for non-present animals. Bacterial mat coverage was estimated in terms of Percent Coverage and Fractal Dimension. A constant Region of Interest (ROI) was defined and background extraction by a Gaussian Blurring Filter was performed. Image subtraction within ROI was followed by the sum of the RGB channels matrices. Percent Coverage was calculated on the resulting image. Fractal Dimension was estimated using the box-counting method. The images were then resized to a dimension in pixels equal to a power of 2, allowing subdivision into sub-multiple quadrants. In comparisons of manual and automated Percent Coverage and Fractal Dimension estimates, the former showed an overestimation tendency for both parameters. The primary limitations on the automatic analysis of benthic images were habitat variations in sediment texture and water column turbidity. The application of filters for background corrections is a required preliminary step for the efficient recognition of animals and bacterial mat patches. en_US
dc.description.sponsorship The present work was developed in the context of the following Research Projects funded by: the Spanish Ministry for Science and Innovation-MICINN (RITFIM, CTM2010-16274), the Italian Ministry of Agricultural, Food and Forestry Politics-MIPAAF (HighVision, DM 19177/7303/08), the Canada Foundation for Innovation and the British Columbia Knowledge Development Fund (NEPTUNE Canada and VENUS project; University of Victoria), and an NSERC Canada Strategic Networks grant to the Canadian Healthy Oceans Network (CHONe). J. Aguzzi is a Postdoctoral Fellow of the Ramón y Cajal Program (MICINN). M. Matabos conducted this study during a post-doctoral fellowship funded by the Canadian Healthy Ocean Network (CHONe). K. Robert benefited from scholarships from the Natural Sciences and Engineering Research Council (Canada), the Fonds du Québec de Recherche—Nature et Technologies and the Bob Wright Foundation (University of Victoria) The authors would like to thank J. Rose and K. Nicolich for their help with image acquisition. We also would like to thank the VENUS, NEPTUNE Canada and ROV ROPOS teams for their helpful collaboration. en_US
dc.language.iso en en_US
dc.publisher Sensors en_US
dc.subject cabled observatory en_US
dc.subject automated image analysis en_US
dc.subject squat lobster (Munida quadrispina) en_US
dc.subject bacterial mat (Beggiatoa spp.) en_US
dc.subject Scale-Invariant Feature Transform (SIFT) en_US
dc.subject Fourier Descriptors (FD) en_US
dc.subject Partial Least Square Discriminant Analysis (PLSDA) en_US
dc.subject percentage of coverage en_US
dc.subject fractal dimension en_US
dc.title Automated Image Analysis for the Detection of Benthic Crustaceans and Bacterial Mat Coverage Using the VENUS Undersea Cabled Network en_US
dc.type Article en_US
dc.description.scholarlevel Faculty en_US
dc.description.reviewstatus Reviewed en_US


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