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Segmentation of Laterally Symmetric Overlapping Objects: Application to Images of Collective Animal Behavior

Institute of Complex Systems, South Bohemian Research Center of Aquaculture and Biodiversity of Hydrocenoses, Kompetenzzentrum MechanoBiologie in Regenerativer Medizin, Faculty of Fisheries and Protection of Waters, University of South Bohemia in České Budějovice, Zámek 136, 373 33 Nové Hrady, Czech Republic
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Symmetry 2019, 11(7), 866; https://doi.org/10.3390/sym11070866
Received: 5 June 2019 / Revised: 25 June 2019 / Accepted: 27 June 2019 / Published: 3 July 2019
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Abstract

Video analysis is currently the main non-intrusive method for the study of collective behavior. However, 3D-to-2D projection leads to overlapping of observed objects. The situation is further complicated by the absence of stall shapes for the majority of living objects. Fortunately, living objects often possess a certain symmetry which was used as a basis for morphological fingerprinting. This technique allowed us to record forms of symmetrical objects in a pose-invariant way. When combined with image skeletonization, this gives a robust, nonlinear, optimization-free, and fast method for detection of overlapping objects, even without any rigid pattern. This novel method was verified on fish (European bass, Dicentrarchus labrax, and tiger barbs, Puntius tetrazona) swimming in a reasonably small tank, which forced them to exhibit a large variety of shapes. Compared with manual detection, the correct number of objects was determined for up to almost 90 % of overlaps, and the mean Dice-Sørensen coefficient was around 0.83 . This implies that this method is feasible in real-life applications such as toxicity testing. View Full-Text
Keywords: image segmentation; lateral objects; 2D vision image segmentation; lateral objects; 2D vision
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    Doi: doi:10.5061/dryad.1j29991
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Lonhus, K.; Štys, D.; Saberioon, M.; Rychtáriková, R. Segmentation of Laterally Symmetric Overlapping Objects: Application to Images of Collective Animal Behavior. Symmetry 2019, 11, 866.

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