Title :
Threshold selection using second derivatives of the gray scale image
Author_Institution :
Image Analysis Lab., State Univ. of New York, Stony Brook, NY, USA
Abstract :
It is known that when a bilevel image is blurred, the intensity of the original pixels is related with the sign of the curvature of the pixels of the blurred image. A technique for threshold selection is presented where a partial histogram is constructed solely from the pixels where curvature achieves extrema values. The method is most suitable when low-contrast images with textured backgrounds (but not sparse dot matrices) are a large fraction of the input population
Keywords :
image recognition; image segmentation; optical character recognition; bilevel image; blurred image; character recognition; curvature; dot matrices; extrema values; gray scale image; input population; low-contrast images; partial histogram; pixel intensity; textured backgrounds; threshold selection; Colored noise; Convolution; Feature extraction; Histograms; Image analysis; Image color analysis; Iterative methods; Laboratories; Pixel; Production facilities;
Conference_Titel :
Document Analysis and Recognition, 1993., Proceedings of the Second International Conference on
Conference_Location :
Tsukuba Science City
Print_ISBN :
0-8186-4960-7
DOI :
10.1109/ICDAR.1993.395733