2021
DOI: 10.3390/s21041134
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ALMI—A Generic Active Learning System for Computational Object Classification in Marine Observation Images

Abstract: In recent years, an increasing number of cabled Fixed Underwater Observatories (FUOs) have been deployed, many of them equipped with digital cameras recording high-resolution digital image time series for a given period. The manual extraction of quantitative information from these data regarding resident species is necessary to link the image time series information to data from other sensors but requires computational support to overcome the bottleneck problem in manual analysis. As a priori knowledge about t… Show more

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Cited by 3 publications
(2 citation statements)
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“…Within this context, high‐definition still and video image data constitute a key non‐destructive approach for the non‐invasive monitoring of aquatic marine environments (Aguzzi et al, 2019; Bicknell et al, 2016; Jahanbakht et al, 2021). Many studies in the literature contributed to the analysis of benthic fauna outside the Antarctic region (Lopez‐Vazquez et al, 2020; Möller & Nattkemper, 2021), including the analysis of corals (Osterloff et al, 2019; Zuazo et al, 2020) and sponge dynamics (Harrison et al, 2021; Möller et al, 2019).…”
Section: Discussionmentioning
confidence: 99%
“…Within this context, high‐definition still and video image data constitute a key non‐destructive approach for the non‐invasive monitoring of aquatic marine environments (Aguzzi et al, 2019; Bicknell et al, 2016; Jahanbakht et al, 2021). Many studies in the literature contributed to the analysis of benthic fauna outside the Antarctic region (Lopez‐Vazquez et al, 2020; Möller & Nattkemper, 2021), including the analysis of corals (Osterloff et al, 2019; Zuazo et al, 2020) and sponge dynamics (Harrison et al, 2021; Möller et al, 2019).…”
Section: Discussionmentioning
confidence: 99%
“…In the case of benthic biodiversity community assessment, as considered here, the amount of image data collected often greatly exceeds the capacity of visual inspection by domain experts and the potential of machine learning-based annotation has gained interest over the last decade (e.g. Schoening et al, 2012;Möller and Nattkemper, 2021;Mbani et al, 2023;Yamada et al, 2023). To respond to the growing amounts of imagery, the BIIGLE system has been equipped with the MAIA tool (Machine learning Assisted Image Annotation) (Zurowietz et al, 2018;Zurowietz and Nattkemper, 2020) and cloud storage so users can upload their image data into the BIIGLE cloud, and analyse their image data more efficiently, employing machine learning.…”
Section: Introductionmentioning
confidence: 99%