Improved Entropic Threshold based on GLSC Histogram with Varying Similarity Measure

Image segmentation is a necessary task in computer vision and digital image processing applications, where foreground objects are to be separated from background. Many thresholding techniques are found in literature with their own limitations. The Gray level Spatial Correlation (GLSC) Histogram is used in entropic techniques to decide the threshold. In this paper we propose an improved GLSC Histogram, computed with varying similarity measure ( ) by considering local and global characteristics, because Yang Xiao et. al. used a constant 4 as the similarity measure by considering the image local properties only, which does not suits for all types of images and probability error is minimized by redistributing the missing probability amount in floating precisions. For low contrast images contrast enhancement is assumed. Experimental results demonstrate a quantitative improvement against existing techniques by calculating the parameter efficiency η based on the misclassification error and variations in various yielding towards ground truth threshold on two dimensional histogram of image. General Terms Image processing, entropy

[1]  和田英一.  20世紀の名著名論：C.E.. Shannon: A Mathematical Theory of Communications , 2002 .

[2]  Prasanna K. Sahoo,et al.  Image thresholding using two-dimensional Tsallis-Havrda-Charvát entropy , 2006, Pattern Recognit. Lett..

[3]  Thierry Pun,et al.  A new method for grey-level picture thresholding using the entropy of the histogram , 1980 .

[4]  Prasanna K. Sahoo,et al.  Threshold selection using Renyi's entropy , 1997, Pattern Recognit..

[5]  Marco Furini,et al.  International Journal of Computer and Applications , 2010 .

[6]  Gurdial Arora,et al.  A thresholding method based on two-dimensional Renyi's entropy , 2004, Pattern Recognit..

[7]  Sankar K. Pal,et al.  A review on image segmentation techniques , 1993, Pattern Recognit..

[8]  Márcio Portes de Albuquerque,et al.  Image thresholding using Tsallis entropy , 2004, Pattern Recognit. Lett..

[9]  Ahmed S. Abutaleb,et al.  Automatic thresholding of gray-level pictures using two-dimensional entropy , 1989, Comput. Vis. Graph. Image Process..

[10]  Claude E. Shannon,et al.  A Mathematical Theory of Communications , 1948 .

[11]  Bülent Sankur,et al.  Survey over image thresholding techniques and quantitative performance evaluation , 2004, J. Electronic Imaging.

[12]  Andrew K. C. Wong,et al.  A new method for gray-level picture thresholding using the entropy of the histogram , 1985, Comput. Vis. Graph. Image Process..

[13]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[14]  Zhiguo Cao,et al.  Entropic thresholding based on gray-level spatial correlation histogram , 2008, 2008 19th International Conference on Pattern Recognition.

[15]  Ahmed S. Abutableb.  Automatic thresholding of gray-level pictures using two-dimensional entropy , 1989 .