Paper
1 April 1998 Scale-space approach to image thinning using the most prominent ridge line in the image pyramid data structure
Mark E. Hoffman, Edward K. Wong
Author Affiliations +
Proceedings Volume 3305, Document Recognition V; (1998) https://doi.org/10.1117/12.304636
Event: Photonics West '98 Electronic Imaging, 1998, San Jose, CA, United States
Abstract
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 threshold 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 increase finer image structure is suppressed. By treating the image as a 3D 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.
© (1998) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Mark E. Hoffman and Edward K. Wong "Scale-space approach to image thinning using the most prominent ridge line in the image pyramid data structure", Proc. SPIE 3305, Document Recognition V, (1 April 1998); https://doi.org/10.1117/12.304636
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Cited by 3 scholarly publications and 1 patent.
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KEYWORDS
Binary data

Digital imaging

Gaussian filters

Distortion

3D image processing

Algorithm development

Digital filtering

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