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.
Locally Adaptive Enhancement, Binarization, And Segmentation Of Images For Machine Vision
Applications of Digital Image Processing VII ; 1984 ; San Diego,United States
Proc. SPIE ; 0504
1984-12-04
Conference paper
Electronic Resource
English
Adaptive Choice of Global Threshold for Binarization of Gradient Images
British Library Online Contents | 2005
|Improved Adaptive Fingerprint Binarization
IEEE | 2008
|Using Irregular Pyramid for Text Segmentation and Binarization of Gray Scale Images
British Library Conference Proceedings | 2003
|Evaluation of Binarization Methods for Utility Map Images
British Library Conference Proceedings | 1994
|