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A Flexible Method for Localisation and Classification of Footprints of Small Species

  • Haokun Geng
  • James Russell
  • Bok-Suk Shin
  • Radu Nicolescu
  • Reinhard Klette
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7088)

Abstract

In environmental surveillance, ecology experts use a standard tracking tunnel system to acquire tracks or footprints of small animals, so that they can measure the presence of any selected animals or detect threatened species based on the manual analysis of gathered tracks. Unfortunately, distinguishing morphologically similar species through analysing their footprints is extremely difficult, and even very experienced experts find it hard to provide reliable results on footprint identification. This expensive task also requires a great amount of efforts on observation. In recent years, image processing technology has become a model example for applying computer science technology to many other study areas or industries, in order to improve accuracy, productivity, and reliability. In this paper, we propose a method based on image processing technology, it firstly detects significant interest points from input tracking card images. Secondly, it filters irrelevant interest points in order to extract regions of interest. Thirdly, it gathers useful information of footprint geometric features, such as angles, areas, distance, and so on. These geometric features can be generally found in footprints of small species. Analysing the detected features statistically can certainly provide strong proof of footprint localization and classification results. We also present experimental results on extracted footprints by the proposed method. With appropriate developments or modifications, this method has great potential for applying automated identification to any species.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Haokun Geng
    • 1
  • James Russell
    • 2
  • Bok-Suk Shin
    • 1
  • Radu Nicolescu
    • 1
  • Reinhard Klette
    • 1
  1. 1.Department of Computer ScienceUniversity of AucklandAucklandNew Zealand
  2. 2.School of Biological Sciences, Department of StatisticsUniversity of AucklandAucklandNew Zealand

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