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Gray Level Thresholding in Badly Illuminated Images
August 1991 (vol. 13 no. 8)
pp. 813-819

The thresholding method involves first locating objects in an image by using the intensity gradient, then noting the levels that correspond to the objects in various areas of the image, and finally using these levels as initial guesses at a threshold. This method is capable of thresholding images that have been produced in the context of variable illumination. The thresholding method, called the local intensity gradient (LIG) method, was implemented in C using a Sun4 host running UNIX. The LIG method was compared against iterative selection (IS), gray level histograms (GLHs) and two correlation based algorithms on a dozen sample images under three different illumination effects. Overall, the LIG method, while it takes significantly longer, properly thresholds a larger set of images than does any other method examined over the sample images tested.

[1] R. Bracho and A. C. Sanderson "Segmentation of images based on intensity gradient informations," inProc. IEEE Computer Soc. Conf. Computer Vision and Pattern Recognition, San Francisco, CA, June 19-23, 1985, pp. 341-347.
[2] A. D. Brink, "Gray-level thresholding of images using a correlation criterion,"Pattern Recog., Lett., vol. 9, no. 5, pp. 335-341, June 1989.
[3] K. Castleman and R. Wall, "Automatic systems for chomosome identification," inNobel Symp. 23--Chromosome Identification. New York: Academic, 1973.
[4] C. K. Chow and T. Kaneko, "Automatic boundary detection of the left ventricle from cineangiograms,"Comput. Biomed. Res., vol. 5, pp. 388-410, 1972.
[5] R. Haralick, "Image segmentation survey," inFundamentals of Computer Vision, O. D. Faugeras, Ed. London: Cambridge University Press, 1983.
[6] N. Otsu, "A threshold selection method from gray-level histograms,"IEEE Trans. Syst. Man, Cybern., vol. 9, no. 1, pp. 377-393, 1979.
[7] A. Perez and R. C. Gonzalez, "An iterative thresholding algorithm for image processing,"IEEE Trans. Pattern Anal. Machine Intell., vol. PAMI-9, no. 6, Nov. 1987.
[8] W. K. Pratt,Digital Image Processing. New York: Wiley, 1978.
[9] J. M. S. Prewitt, "Object enhancement and extraction," inPicture Processing and Psychopictorics, Lipkin and Rosenfeld, Eds. New York: Academic, 1970.
[10] T. W. Ridler, S. Calvard, "Picture thresholding using an iterative selection method,"IEEE Trans. Syst. Man, Cybern., vol. SMC-8, no. 8, pp. 629-632, Aug. 1978.
[11] T. G. Stockham, "Image processing in the context of a visual model,"Proc. IEEE, vol. 60, pp. 828-842, 1972.
[12] F. M. Wahl, K. Y. Wong, and R. G. Casey, "Block segmentation and text extraction in mixed text/image documents,"Comput. Graphics, Image Processing, vol. 20, pp. 375-390, 1982.
[13] R. J. Wall, "The gray level histogram for threshold boundary determination in image processing with applications to the scene segmentation problem in human chromosome analysis," Ph.D. dissertation, Univ. California, Los Angeles, 1974.
[14] R. Wilson and M. Spann,Image Segmentation and Uncertainty. New York: Wiley (Research Studies Press), 1987.

Index Terms:
gray level thresholding; badly illuminated images; intensity gradient; local intensity gradient; iterative selection; gray level histograms; correlation based algorithms; computerised pattern recognition; computerised picture processing
J.R. Parker, "Gray Level Thresholding in Badly Illuminated Images," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 13, no. 8, pp. 813-819, Aug. 1991, doi:10.1109/34.85672
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