Abstract
A multi-scale, moving-window method for local thresholding based on Robust Automatic Threshold Selection (RATS) is developed. Using a model for the noise response of the optimal edge detector in this context, the reliability of thresholds computed at different scales is determined. The threshold computed at the smallest scale at which the reliability is sufficient is used. The performance on 2-D images is evaluated on synthetic an natural images in the presence of varying background and noise. Results show the method deals better with these problems than earlier versions of RATS at most noise levels.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Kittler, J., Illingworth, J., Föglein, J.: Threshold selection based on a simple image statistic. Comp. Vision Graph. Image Proc. 30, 125–147 (1985)
Otsu, N.: A threshold selection method from gray-level histogram. IEEE Trans. Syst. Man Cybern. 9, 62–66 (1979)
Sahoo, P.K., Soltani, S., Wong, A.K.C., Chen, Y.C.: A survey of thresholding techniques. Comp. Vision Graph. Image Proc. 41, 233–260 (1988)
Trier, D., Jain, A.K.: Goal-directed evalution of binarization methods. IEEE Trans. Image Proc. 17(12), 1191–1201 (1995)
Wilkinson, M.H.F.: Rapid automatic segmentation of fluorescent and phasecontrast images of bacteria. In: Slavik, J. (ed.) Fluorescence Microscopy and Fluorescent Probes, pp. 261–266. Plenum Press, New York (1996)
Wilkinson, M.H.F.: Automated and manual segmentation techniques in image analysis of microbes. In: Wilkinson, M.H.F., Schut, F. (eds.) Digital Image Analysis of Microbes, pp. 135–171. John Wiley and Sons, Ltd, Chichester (1998)
Wilkinson, M.H.F.: Optimizing edge detectors for robust automatic threshold selection: coping with edge curvature and noise. Graph. Mod. Image Proc. 60, 385–401 (1998)
Yang, Y., Yan, H.: An adaptive logical method for binarization of degraded document images. Pattern Recognition 33, 787–807 (2000)
Young, I.T., van Vliet, L.J.: Recursive implementation of the Gaussian filter. Signal Processing 44, 139–151 (1995)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wilkinson, M.H.F. (2003). Gaussian-Weighted Moving-Window Robust Automatic Threshold Selection. In: Petkov, N., Westenberg, M.A. (eds) Computer Analysis of Images and Patterns. CAIP 2003. Lecture Notes in Computer Science, vol 2756. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45179-2_46
Download citation
DOI: https://doi.org/10.1007/978-3-540-45179-2_46
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-40730-0
Online ISBN: 978-3-540-45179-2
eBook Packages: Springer Book Archive