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The National Marine Fisheries Service (NMFS) of the United States National Oceanic and Atmospheric Administration (NOAA), the Department of Fisheries of Western Australia, the Australia Institute of Marine Science, the European Union and many NGOs, private conservation groups, and large-scale aquaculture operations are increasingly using optical data streams to augment traditional terrestrial, marine, and aquatic wildlife surveys. A report by the US National Task Force for Improving Stock Assessment found the greatest impediment to producing accurate, precise, and credible stock assessments was the lack of adequate input data. If properly employed, camera-based surveys can reduce sampling error, increase sampling intensity and increase the spatio-temporal area or number of species surveyed. However, data volume quickly exceeds human processing capabilities. To date, this has been mitigated by subsampling available data, by using analysis methods that may not accurately characterize wildlife assemblages, and initial efforts at automating image and video analysis. Automated wildlife classifiers —similar to those that have been developed for human surveillance and biomedical applications—must be developed to effectively make use of available data. To this end, NOAA Fisheries has initiated a Strategic Initiative on Automated Image Analysis. Operating underwater; classifying cryptic, camouflaged, or morphologically similar individuals generates significant challenges not unlike those faced by aerial human surveillance. Lately, there has been a significant increase in vision algorithm development for wildlife video of animals including birds, bats, bees, flies, fish, large terrestrial mammals, and so on.

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Topics of interest include, but are not limited to: ·Animal segmentation and detection in still images ·Animal detection and tracking in video data ·Animal behavior analysis and classification in video data ·Size estimation from stereo-video ·3D reconstruction and volumetric assessment of animals ·Animal species classification through supervised and unsupervised clustering ·Normalcy modeling and anomaly detection in wildlife video ·Image and video enhancement in wildlife imaging ·Interactive methods for wildlife analysis
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