Scale-space approach to image thinning using the most prominent ridge-line in the image pyramid data structure

Mark E. Hoffman, Edward K. Wong

    Research output: Contribution to journalConference articlepeer-review


    Image thinning methods can be divided into two categories based on the type of image they are designed to thin: binary image thinning and grayscale image thinning. Typically, grayscale images are thresholded to allow binary image thinning methods to be applied. However, thresholding grayscale images may introduce uneven object contours that are a difficulty for binary methods. The scale-space approach to image thinning includes scale as an additional dimension where images at scale t are derived from the original image at scale zero by applying the Gaussian filter. As scale increases finer image structure is suppressed. By treating the image as a 3-D surface with intensity as the third dimension, the Most Prominent Ridge-Line (MPRL) is the union of topographical features: peak, ridge, and saddle point, such that each has greatest contrast with its surroundings. The MPRL is computed by minimizing its second spatial derivative over scale. The result forms a trajectory in scale-space. The thinned image is the projection of the MPRL on the base level. The MPRL has been implemented using the image pyramid data structure, and has been applied to binary and grayscale images of printed characters. Experimental results show that the method is less sensitive to contour unevenness. It also offers the option of choosing different levels of fine structure to include.

    Original languageEnglish (US)
    Pages (from-to)242-252
    Number of pages11
    JournalProceedings of SPIE - The International Society for Optical Engineering
    StatePublished - 1998
    EventDocuments Recognition V - San Jose, CA, United States
    Duration: Jan 28 1998Jan 29 1998


    • Contour perturbation
    • Image pyramid
    • Image thinning
    • Ridge-line
    • Scale-space
    • Sensitivity
    • Topography

    ASJC Scopus subject areas

    • Electronic, Optical and Magnetic Materials
    • Condensed Matter Physics
    • Computer Science Applications
    • Applied Mathematics
    • Electrical and Electronic Engineering


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