A divergence thresholding method in kernel space is proposed.First
a new kind of Bregman divergence in parametric form is defined.Second
the new thresholding method based on Bregman divergence is presented.The new methed can unify cross entropy and Otsu’s thresholding method.Third
a new asymmetric kernel function in reproduced Hilbert space is constructed by means of Bregman divergence in parametric form.Image gray levels in Euclid space are transformed into the reproduced kernel space
and a divergence thresholding method in kernel space is obtained.Finally
the method for choosing parameters for the kernel function in the new thresholding method is analyzed.Experimental results show that the proposed divergence thresholding method in kernel space has a certain widespread adaptability.It can improve the segmentation performance of cross entropy and Otsu’s thresholding methods
and two kinds of classical thresholding methods based on cross entropy and Otsu are also regarded as the special cases of the kernel space divergence thresholding method proposed in this paper.