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)


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.


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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Paris, S., Jauffret, C.: A new tracker for multiple frequency line. In: Proc. of the IEEE Conference for Aerospace, vol. 4, pp. 1771–1782. IEEE, Los Alamitos (2001)Google Scholar
  2. 2.
    Lampert, T.A., O’Keefe, S.E.M.: Active contour detection of linear patterns in spectrogram images. In: Proc. of the 19th International Conference on Pattern Recognition (ICPR 2008), Tampa, Florida, USA, December 2008, pp. 1–4 (2008)Google Scholar
  3. 3.
    Abel, J.S., Lee, H.J., Lowell, A.P.: An image processing approach to frequency tracking. In: Proc. of the IEEE Int. Conference on Acoustics, Speech and Signal Processing, March 1992, vol. 2, pp. 561–564 (1992)Google Scholar
  4. 4.
    Martino, J.C.D., Tabbone, S.: An approach to detect lofar lines. Pattern Recognition Letters 17(1), 37–46 (1996)CrossRefGoogle Scholar
  5. 5.
    Mingzhi, L., Meng, L., Weining, M.: The detection and tracking of weak frequency line based on double-detection algorithm. In: Int. Symposium on Microwave, Antenna, Propagation and EMC Technologies for Wireless Communications, August 2007, pp. 1195–1198 (2007)Google Scholar
  6. 6.
    Morrissey, R.P., Ward, J., DiMarzio, N., Jarvis, S., Moretti, D.J.: Passive acoustic detection and localisation of sperm whales (Physeter Macrocephalus) in the tongue of the ocean. Applied Acoustics 67, 1091–1105 (2006)CrossRefGoogle Scholar
  7. 7.
    Mellinger, D.K., Nieukirk, S.L., Matsumoto, H., Heimlich, S.L., Dziak, R.P., Haxel, J., Fowler, M., Meinig, C., Miller, H.V.: Seasonal occurrence of north atlantic right whale (Eubalaena glacialis) vocalizations at two sites on the scotian shelf. Marine Mammal Science 23, 856–867 (2007)CrossRefGoogle Scholar
  8. 8.
    Yang, S., Li, Z., Wang, X.: Ship recognition via its radiated sound: The fractal based approaches. Journal of the Acoustic Society of America 11(1), 172–177 (2002)CrossRefGoogle Scholar
  9. 9.
    Chen, C.H., Lee, J.D., Lin, M.C.: Classification of underwater signals using neural networks. Tamkang J. of Science and Engineering 3(1), 31–48 (2000)Google Scholar
  10. 10.
    Ghosh, J., Turner, K., Beck, S., Deuser, L.: Integration of neural classifiers for passive sonar signals. Control and Dynamic Systems - Advances in Theory and Applications 77, 301–338 (1996)Google Scholar
  11. 11.
    Howell, B.P., Wood, S., Koksal, S.: Passive sonar recognition and analysis using hybrid neural networks. In: Proc. of OCEANS 2003, September 2003, vol. 4, pp. 1917–1924 (2003)Google Scholar
  12. 12.
    Shi, Y., Chang, E.: Spectrogram-based formant tracking via particle filters. In: Proc. of the IEEE Int. Conference on Acoustics, Speech and Signal Processing, April 2003, vol. 1, pp. I–168–I–171 (2003)Google Scholar
  13. 13.
    Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 3rd edn. Prentice-Hall, Inc., Upper Saddle River (2006)Google Scholar
  14. 14.
    Nayar, S., Baker, S., Murase, H.: Parametric feature detection. Int. J. of Computer Vision 27, 471–477 (1998)Google Scholar
  15. 15.
    Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. Wiley-Interscience Publication, Hoboken (2000)Google Scholar

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.

Personalised recommendations