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Line Detection Methods for Spectrogram Images

  • Thomas A. Lampert
  • Simon E. M. O’Keefe
  • Nick E. Pears
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 57)

Summary

Accurate feature detection is key to higher level decisions regarding image content. Within the domain of spectrogram track detection and classification, the detection problem is compounded by low signal to noise ratios and high track appearance variation. Evaluation of standard feature detection methods present in the literature is essential to determine their strengths and weaknesses in this domain. With this knowledge, improved detection strategies can be developed. This paper presents a comparison of line detectors and a novel linear feature detector able to detect tracks of varying gradients. It is shown that the Equal Error Rates of existing methods are high, highlighting the need for research into novel detectors. Preliminary results obtained with a limited implementation of the novel method are presented which demonstrate an improvement over those evaluated.

Keywords

Receiver Operator Curve Equal Error Rate Line Detection Principal Component Analysis Method Receiver Operator Curve Curve 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Thomas A. Lampert
    • 1
  • Simon E. M. O’Keefe
    • 1
  • Nick E. Pears
    • 1
  1. 1.Department of Computer ScienceUniversity of YorkYorkU.K.

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