An Thresholding Segmentation based on Wavelet-Transform and Two-Dimension Maximum Entropy Sum Method
In this paper,a new segmentation based on wavelet-transform and two-dimension maximum entropy sum method has been proposed.Firstly,the image is processed by using n-level wavelet-transform.The thre- shold of the lowest frequency sub-image is computed by using two-dimensions maximum entropy sum method.Combining other high frequency sub-images which represent the edge of the image,the lowest frequency sub-image is segment into two classes:the object and its background.The percentage of the object's energy in the sub-image is computed.Secondly,the threshold is used as a seed to segement the original image,its value is being adjusted until the two value which are the percentage of energy of the object that segmented from the original image and the percentage that got in the first process are equal in the bound of error.Finally,the best threshold of the original image is got.This algorithm can achieve good result of segmentation and is better than the two-dimension maximum entropy sum method.