A symbolic dynamics based approach to pattern recognition in image sequences

Aparna Subbu, Eric E. Keller, Asok Ray

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    This paper presents the application of symbolic dynamic analysis to two-dimensional images for the purpose of pattern recognition in temporal image sequences. A specific example of flaw detection in polycrystalline alloys via image sequences obtained from a camera mounted on a microscope is considered. An anomaly measure which indicates the severity of a crack with a quantifiable numerical value was derived using the D-Markov machine. Use of a region-of-interest based analysis of the statistical properties of pixels makes the pre-processing step of image registration redundant. The problem due to the presence of relative motion between successive frames of an image sequence does not significantly affect the detection of a fatigue crack when symbolic dynamics is used. A comparison of the performance of the algorithm in the presence and absence of registration of images is shown.

    Original languageEnglish (US)
    Title of host publicationProceedings of the 2007 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2007
    Pages383-389
    Number of pages7
    StatePublished - 2007
    Event2007 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2007 - Las Vegas, NV, United States
    Duration: Jun 25 2007Jun 28 2007

    Other

    Other2007 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2007
    CountryUnited States
    CityLas Vegas, NV
    Period6/25/076/28/07

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    All Science Journal Classification (ASJC) codes

    • Computer Graphics and Computer-Aided Design
    • Computer Vision and Pattern Recognition

    Cite this

    Subbu, A., Keller, E. E., & Ray, A. (2007). A symbolic dynamics based approach to pattern recognition in image sequences. In Proceedings of the 2007 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2007 (pp. 383-389)