A Gauss Mixture Model with Difference of Mutual Information for Image Segmentation 结合互信息熵差测度的高斯混合模型图像分割
Metal Artifact Reduction in CT Based on Maximized the Difference of Mutual Information Segmentation 基于最大互信息量熵差分割的CT金属伪影消除
The objective evaluation of fused image is studied. The objective evaluation measures are root of mean square error, mean error, standard deviation, entropy, entropy difference, cross entropy, mutual information and spatial frequency. 这里研究了多传感器图像融合效果的客观评价方法,其中包括均方根误差、平均误差、灰度标准差、熵、熵差、交叉熵、互信息和空间频率。