Robust localization and identification of African clawed frogs in digital images

F. Boray Tek*, Flavio Cannavo, Giuseppe Nunnari, Izzet Kale

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)


We study the automatic localization and identification of African clawed frogs (Xenopus laevis sp.) in digital images taken in a laboratory environment. We propose a novel and stable frog body localization and skin pattern window extraction algorithm. We show that it compensates scale and rotation changes very well. Moreover, it is able to localize and extract highly overlapping regions (pattern windows) even in the cases of intense affine transformations, blurring, Gaussian noise, and intensity transformations. The frog skin pattern (i.e. texture) provides a unique feature for the identification of individual frogs. We investigate the suitability of five different feature descriptors (Gabor filters, area granulometry, HoG,. 11Histogram of Oriented Gradients. dense SIFT,. 22Scale invariant feature transform. and raw pixel values) to represent frog skin patterns. We compare the robustness of the features based on their identification performance using a nearest neighbor classifier. Our experiments show that among five features that we tested, the best performing feature against rotation, scale, and blurring modifications was the raw pixel feature, whereas the SIFT feature was the best performing one against affine and intensity modifications.

Original languageEnglish
Pages (from-to)3-12
Number of pages10
JournalEcological Informatics
Publication statusPublished - Sept 2014
Externally publishedYes


  • Area granulometry
  • Automated frog identification
  • HoG
  • SIFT
  • Skin pattern recognition
  • Xenopus laevis


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