Image segmentation using maximum entropy method
C.K. Leung, F.K. Lam · 2002
Segmentation of a composite image which contains two simple subimages is described. The a-priori knowledge about the two simple subimages is that they possess the maximum amount of entropy. The probability density functions (pdfs) of these image pixels are shown to be of the quasi-Gaussian form. Parameters for the pdf are estimated and then the maximum likelihood ratio test is applied to segmentation. An iterative algorithm is employed to improve the segmentation accuracy. Extension of this method to the segmentation of images with arbitrary pdfs is discussed.>