Affinity Matting for Pixel-accurate Fin Shape Recovery from Great White Shark Imagery

Hughes, Benjamin; Burghardt, Tilo

doi:10.5244/c.29.mvab.8

Cited by 2 publications

(2 citation statements)

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“…We obtain a binary assignment of pixels (by threshold 0.5) to separate fin and background, and extract the resulting high resolution contour of best Chamfer distance fit as a precursor to biometric encoding. Full details of this edge refinement procedure can be found in Hughes and Burghardt (2015a).…”

Section: Biometric Contour Encodingmentioning

confidence: 99%

Automated Visual Fin Identification of Individual Great White Sharks

Hughes

Burghardt

2016

Int J Comput Vis

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This paper discusses the automated visual identification of individual great white sharks from dorsal fin imagery. We propose a computer vision photo ID system and report recognition results over a database of thousands of unconstrained fin images. To the best of our knowledge this line of work establishes the first fully automated contour-based visual ID system in the field of animal biometrics. The approach put forward appreciates shark fins as textureless, flexible and partially occluded objects with an individually characteristic shape. In order to recover animal identities from an image we first introduce an open contour stroke model, which extends multi-scale region segmentation to achieve robust fin detection. Secondly, we show that combinatorial, scale-space selective fingerprinting can successfully encode fin individuality. We then measure the species-specific distribution of visual individuality along the fin contour via an embedding into a global 'fin space'. Exploiting this domain, we finally propose a non-linear model for individual animal recognition and combine all approaches into a fine-grained multiinstance framework. We provide a system evaluation, compare results to prior work, and report performance and properties in detail.

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Section: Biometric Contour Encodingmentioning

confidence: 99%

Automated Visual Fin Identification of Individual Great White Sharks

Hughes

Burghardt

2016

Int J Comput Vis

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“…It has been used as an effective technique for data collection in ecological procedures such as capture-mark-recapture (CMR), as photographic images of an individual’s unique markings can be cross-matched within a photo-database for detection of recapture events ( Williams, Nichols & Conroy, 2002 ; Pebsworth & Lafleur, 2014 ). This process has been particularly useful for monitoring species that cannot be easily captured or artificially tagged for identification purposes ( Frisch & Hobbs, 2007 ; Arandjelović & Zisserman, 2011 ; Hughes & Burghardt, 2015 ), and has been applied to a diverse number of taxa including mammals ( Karanth & Nichols, 1998 ), large fish ( Arandjelović & Zisserman, 2011 ; Hughes & Burghardt, 2015 ), crustaceans ( Frisch & Hobbs, 2007 ), and herpetofauna ( Gardiner et al, 2014 ). With the increased affordability and use of smartphone devices that are equipped with cameras, as well as the advent of camera trapping technology, individuals can now be photographed under field conditions and differentiated with very little cost, logistics or expertise required ( Wagner et al, 2008 ; Haddock, Kim & Mukai, 2013 ; Pebsworth & Lafleur, 2014 ).…”

Section: Introductionmentioning

confidence: 99%

Using citizen science in the photo-identification of adult individuals of an amphibian based on two facial skin features

Gould

Clulow

2021

PeerJ

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Among amphibians, adults have traditionally been identified in capture-mark-recapture studies using invasive marking techniques with associated ethical, cost and logistical considerations. However, species in this group may be strong candidates for photo-identification based on natural skin features that removes many of these concerns, with this technique opening up opportunities for citizen scientists to be involved in animal monitoring programs. We investigated the feasibility of using citizen science to distinguish between individuals of an Australian anuran (the sandpaper frog, Lechriodus fletcheri) based on a visual analysis of their natural skin features. We collected photographs of marked individuals in the field over three breeding seasons using a smartphone device. This photo-database was used to create an online survey to determine how easily members of the general public could photo-match individuals by a comparison of two facial skin features; black banding that runs horizontally above the tympanum and a background array of tubercles present in this region. Survey participants were provided with 30 closed, multiple choice questions in which they were asked to match separate images of a query frog from small image pools of potential candidate matches. Participants were consistently able to match individuals with a low matching error rate (mean ± SD of 26 ± 5) despite the relatively low quality of photographs taken from a smartphone device in the field, with most query frogs being matched by a majority of participants (mean ± SD of 86.02 ± 9.52%). These features were found to be unique and stable among adult males and females. Thus, photo-identification is likely to be a valid, non-invasive method for capture-mark-recapture for L. fletcheri, and likely many anurans that display similar facial skin features. This may become an important alternative to artificial marking techniques, with the challenges of manual photo-matching reduced by spreading workloads among members of the public that can be recruited online.

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Affinity Matting for Pixel-accurate Fin Shape Recovery from Great White Shark Imagery

Cited by 2 publications

References 10 publications

Automated Visual Fin Identification of Individual Great White Sharks

Automated Visual Fin Identification of Individual Great White Sharks

Using citizen science in the photo-identification of adult individuals of an amphibian based on two facial skin features

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