【摘 要】
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为了克服边缘流各向异性扩散(EAD,edgeflow-driven anistropic diffusion)过分割和最小生成树(MST,min-imum spanning tree)方法计算复杂度高的缺点,提出了结合边缘流与区域归并的彩色图像分割方法。首先利用EAD方法对图像进行预分割,然后利用MST方法依据全局最优化准则对EAD的过分割区域进行归并,最后进行相应的后处理,得到最终的分割结果。这
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为了克服边缘流各向异性扩散(EAD,edgeflow-driven anistropic diffusion)过分割和最小生成树(MST,min-imum spanning tree)方法计算复杂度高的缺点,提出了结合边缘流与区域归并的彩色图像分割方法。首先利用EAD方法对图像进行预分割,然后利用MST方法依据全局最优化准则对EAD的过分割区域进行归并,最后进行相应的后处理,得到最终的分割结果。这里,由于MST方法是基于EAD的过分割区域而非像素点,因此算法效率得到了很大的提高。另外,EAD方法可以
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