Locally Adaptive Enhancement, Binarization, And Segmentation Of Images For Machine Vision

A. F. Lehar; R. A. Gonsalves

doi:10.1117/12.944861

4 December 1984 Locally Adaptive Enhancement, Binarization, And Segmentation Of Images For Machine Vision

A. F. Lehar, R. A. Gonsalves

Proceedings Volume 0504, Applications of Digital Image Processing VII; (1984) https://doi.org/10.1117/12.944861
Event: 28th Annual Technical Symposium, 1984, San Diego, United States

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.

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A. F. Lehar and R. A. Gonsalves "Locally Adaptive Enhancement, Binarization, And Segmentation Of Images For Machine Vision", Proc. SPIE 0504, Applications of Digital Image Processing VII, (4 December 1984); https://doi.org/10.1117/12.944861

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