An astronomical pattern-matching algorithm for computer-aided identification of whale sharks Rhincodon typus

By Z. Arzoumanian, Jason A. Holmberg, B. Norman. 2005

318 citations recorded by the source corpus.

Subfields: Ecology, Global and Planetary Change, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/d6d7ddc0303b7342a9ae52622bec382d85756036

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In Ecology

In Global and Planetary Change

In Nature and Landscape Conservation

Citations in the corpus

Aerial survey as a tool to estimate whale shark abundance trends (2009, background): “Individual sharks were identified daily by an in-water monitoring team either by a numbered placard tag attached to the shark's flank, or by using the spot pattern in the area behind the gills (Arzoumanian et al., 2005; Speed et al., 2007).”
Investigating the viability of photo-identification as an objective tool to study endangered sea turtle populations (2008, methodology): “However, confirmation is required of the stability of the natural features (i.e. colouration and relative scale sizes) being used (Forcada and Aguilar, 2003; Arzoumanian et al., 2005).”
Estimating population size, structure, and residency time for whale sharks Rhincodon typus through collaborative photo-identification (2009, methodology): “Two algorithms were employed for computer-assisted photo-identification based on spot patterning (Arzoumanian et al. 2005, Van Tienhoven et al. 2007), permitting rapid identification for mark-recapture analysis.”
Assessing the size, growth rate and structure of a seasonal population of whale sharks (Rhincodon typus Smith 1828) using conventional tagging and photo identification (2007, unknown): “This technique is used by the International Fund for Animal Welfare’s (IFAW) sperm whale identiﬁcation program (Whitehead, 1990) and was recently developed for whale sharks using algorithms derived from astronomical pattern matching (Arzoumanian et al., 2005).”
A computer vision for animal ecology. (2018, background): “Ecological computer vision has grown out of multiple disciplines, with contributions from computer science (Branson, Van Horn, Belongie, & Perona, 2014), astronomy (Arzoumanian, Holmberg, & Norman, 2005) and remote sensing (LaRue, Stapleton, & Anderson, 2016).”
Analysis of whale shark Rhincondon typus aggregations near South Ari Atoll, Maldives Archipelago (2010, background): “…behind the 5th gill slit; (2) dorsal of the proximal end of the pectoral fin; (3) anterior of a line drawn dorso-ventrally from the insertion point of the posterior end of the pectoral fin; and (4) ventral of the 3rd longitudinal ridge (Arzoumanian et al. 2005) on both sides of each shark (Fig.”
Multi‐scale features for identifying individuals in large biological databases: an application of pattern recognition technology to the marbled salamander Ambystoma opacum (2007, background): “More recently, Arzoumanian et al. (2005) modified algorithms designed for the recognition of star constellations to identify individual whale sharks Rhincodon typus and reported 92% success in identifying 27 known matches from sets of approximately 450 images.”
Size and structure of a photographically identified population of manta rays Manta alfredi in southern Mozambique (2011, background): “whale sharks and grey nurse sharks) (Arzoumanian et al. 2005; Van Tienhoven et al. 2007), the presence of many obvious identifying marks on M.”