Symbolic dynamic filtering for image analysis: theory and experimental validation

Abstract

Recent literature has reported the theory of symbolic dynamic filtering (SDF) of one-dimensional time-series data and its various applications for anomaly detection and pattern recognition. This paper extends the theory of SDF in the two-dimensional domain, where symbol sequences are generated from image data (i.e., pixels). Given the symbol sequence, a probabilistic finite state automaton (PFSA), called the D-Markov machine, is constructed on the principles of Markov random fields to incorporate the spatial information in the local neighborhoods of a pixel. The image analysis algorithm has been experimentally validated on a computer-controlled fatigue test apparatus that is equipped with a traveling optical microscope and ultrasonic flaw detectors. The surface images of test specimens, made of a polycrystalline alloy, are analyzed to detect and quantify the evolution of fatigue damage. The results of two-dimensional SDF analysis are in close agreement with those obtained from analysis of one-dimensional time-series data from the ultrasonic sensor, which are simultaneously generated from the same test specimen.

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Correspondence to Asok Ray.

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This work has been supported in part by the U.S. Army Research Office under Grant No. W911NF-07-1-0376, and by NASA under Cooperative Agreement No. NNX07AK49A. Any opinions, findings and conclusions or recommendations expressed in this publication are those of the authors and do not necessarily reflect the views of the sponsoring agencies.

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Subbu, A., Srivastav, A., Ray, A. et al. Symbolic dynamic filtering for image analysis: theory and experimental validation. SIViP 4, 319–329 (2010). https://doi.org/10.1007/s11760-009-0122-7

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Keywords

  • Image analysis
  • Two-dimensional symbolic analysis
  • Statistical pattern recognition
  • Fatigue damage analysis