Spectrogram Track Detection: An Active Contour Algorithm

Research output: ThesisDoctoral Thesis


In many areas of science, near-periodic phenomena represent important information within time-series data. This thesis takes the example of the detection of non-transitory frequency components in passive sonar data, a problem which finds many applications. This problem is typically transformed into the pattern recognition domain by representing the time-series data as a spectrogram, in which slowly varying periodic signals appear as curvilinear tracks. The research is initiated with a survey of the literature, which is focused upon research into the detection of tracks within spectrograms. An investigation into low-level feature detection reveals that none of the evaluated methods perform adequately within the low signal-to-noise ratios of real-life spectrograms and, therefore, two novel feature detectors are proposed. An investigation into the various sources of information available to the detection process shows that the most simple of these, the individual pixel intensity values, used by most existing algorithms, is not sufficient for the problem. To overcome these limitations, a novel low-level feature detector is integrated into a novel active contour track detection algorithm, and this serves to greatly increase detection rates at low signal-to-noise ratios. Furthermore, the algorithm integrates a priori knowledge of the harmonic process, which describes the relative positions of tracks, to augment the available information in difficult conditions. Empirical evaluation of the algorithm demonstrates that it is effective at detecting tracks at signal-to-noise ratios as low as: 0.5 dB with vertical; 3 dB with oblique; and 2 dB with sinusoidal variation of harmonic features. It is also concluded that the proposed potential energy increases the active contour's effectiveness in detecting all the track structures by a factor of eight (as determined by the line location accuracy measure), even at relatively high signal-to-noise ratios, and that incorporating a priori knowledge of the harmonic process increases the detection rate by a factor of two.
Original languageEnglish
Awarding Institution
  • University of York
  • O'Keefe, Simon, Supervisor
Thesis sponsors
Award date21 Jan 2011
Place of PublicationYork
Publication statusPublished - 2010


  • Spectrogram
  • Track Detection
  • Active Contour
  • Edge Detection
  • Line Detection
  • Statistical Pattern Recognitino
  • Structural Pattern Recognition
  • Patter Recognition
  • Machine Learning
  • Remote Sensing
  • Frequency Detection
  • Signal Processing
  • Passive Sonar

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