依据非下采样Contourlet分解系数与其父系数之间的相关性,给出非高斯双变量分布模型, 并基于该模型提出一种新的非下采样Contourlet变换图像分割方法。用合成纹理图像和实际图像进行仿真实验,并与小波域隐马尔可夫树模型分割及Contourlet域隐马尔可夫树模型分割等方法进行了比较,实验结果表明,在大多数情况下,该算法分割结果要好于相比较的方法,在边缘特征方面保持了良好的视觉效果,并且模型的训练简单快速。

Considering the dependencies between the coefficients and their parents, a non Gaussian bivariate distribution model is given in non subsampled Contourlet transform domain. A novel non subsampled Contourlet transform segmentation method based on the bivariate model is proposed. In experiments, synthetic mosaic image and real images were selected to evaluate the performance of the method, and the segmentation results were compared with wavelet domain hidden Markov tree model method and contourlet domain hidden markov tree model segmentation method. The simulation results indicate that the proposed method has better performance, such as keeps better visual result and reserves more information in edges. As a simple model, the time complexity for model training is lower than other models in comparison experiments.