CASVFMM改进算法及其在景物分析中的应用

对图像分割中的CASVFMM算法进行了改进.通过对原有势函数的修正,增加了势函数对像素特征的依赖性,使算法既保持了分割的连续性,又增强了分割收敛的稳定性.由此新算法在对不同类别图像的分割处理中,分割结果与原图像区域对应的一致性有明显增强.另外,对势函数的结构作了一定的修正,加快了算法的收敛速度,增强了算法收敛时分割结果的合理性.通过在MIT标准图像集上的景物分析仿真实验,对比说明了新算法较之CASVFMM算法改进的有效性,为其他图像分析应用提供了一种有效的分割方法.

Improved CASVFMM Algorithm and Its Application in Scenery Image Analysis

Introduces a newly improved method into scenery image segmentation,based on the CASVFMM(class-adaptive spatially variant finite mixture model) algorithm.By modifying the original potential function to strengthen the dependence of potential function on image pixel features,the segmentation continuity can be kept on with enhanced convergence stability.Thus,the segmented results obviously further conform to the corresponding image regions when the new algorithm is applied to the segmentation of images of different classes.Moreover,the structure expression of potential function is modified to a certain degree so as to accelerate the convergence rate of the algorithm and enhance the reasonableness of the segmented results when the algorithm comes into convergence.The simulation tests for scenery image analysis of the MIT standard image sets reveal comparatively that the newly improved algorithm is more efficient than the original CASVFMM and it is also available to other image analyses.