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Proceedings Paper

Locally Adaptive Enhancement, Binarization, And Segmentation Of Images For Machine Vision
Author(s): A. F. Lehar; R. A. Gonsalves
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Paper Abstract

This paper describes a flexible gray scale image enhancement scheme coupled with segmentation algorithms to automatically describe elemental shapes arising in a wide variety of images of interest in machine vision applications. The enhancement algorithm is a locally adaptive Fourier filter configured so as to easily perform either contrast enhancement or additionally apply more complex Fourier filters to enhance periodic features. The enhanced images are then presented to a thresholding and region filling algorithm which breaks the objects of interest into elemental shapes. These shapes are characterized by simple measures such as size, perimeter, and Euler number, and feature extraction tasks are built on the basis of these descriptors. The method has been applied to fingerprint classification, seismic data inspection, and automated handling of packages.

Paper Details

Date Published: 4 December 1984
PDF: 6 pages
Proc. SPIE 0504, Applications of Digital Image Processing VII, (4 December 1984); doi: 10.1117/12.944861
Show Author Affiliations
A. F. Lehar, EIKONIX Corporation (United States)
R. A. Gonsalves, EIKONIX Corporation (United States)


Published in SPIE Proceedings Vol. 0504:
Applications of Digital Image Processing VII
Andrew G. Tescher, Editor(s)

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