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TitleImage Thresholding Based on Spatial Variation Attribute Similarity
DownloadDownloads (Preprint)
LicencePlease note the adoption of the Open Government Licence - Canada supersedes any previous licences.
AuthorBeauchemin, M; Fung, K B
Source25th Canadian Remote Sensing Symposium & 11th Congress of the Association québécoise de télédétection, Montréal, Québec, Canada, October 14-17; 2003 p. 8, Open Access logo Open Access
Alt SeriesEarth Sciences Sector, Contribution Series 20043251
Mediapaper; on-line; digital
File formatpdf
Released2003 01 01
AbstractAccording to a recent study, image thresholding can be categorized into six groups of methods that are based on histogram shape, clustering, entropy, attribute, spatial, and local information. In this paper, we describe two algorithms for image binarization that are based on attribute similarity relying on spatial measures. The rationale of the method is to binarize an image in such a way that it best reproduces the spatial variation of the original image across several scales. Two different measures that characterize image spatial variation have been selected to pursue that objective: semivariance and lacunarity. Semivariance measures the spatial variation of a variable at a given scale. Lacunarity is a measure of translational invariance, at a given scale, and is often refer to as a measure of 'gappiness'. In both approaches, the threshold is selected so that the scale-dependant measure in the bi-level image best approximate, in the least square sense, the ones of the original image. Both methods are illustrated with remote sensing images of high spatial resolution. The results are compared with some other popular thresholding techniques.

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