计算机科学 ›› 2018, Vol. 45 ›› Issue (7): 259-263.doi: 10.11896/j.issn.1002-137X.2018.07.045
王燕,许宪法
WANG Yan ,XU Xian-fa
摘要: 针对复杂图像易受背景干扰的问题,提出一种基于显著性与脉冲耦合神经网络(Saliency and Pulse Coupled Neural Network,SPCNN)的图像分割方法。首先,利用显著性检测算法和最大类间方差法获得显著性图以及目标图像,排除了背景对初始种子点选取的干扰;然后,计算出显著性图的质心,并将其作为初始种子点;最后,采用改进的基于区域生长的脉冲耦合神经网络对目标图像进行分割。在Berkeley图像库和Ground truth Database图像库上对SPCNN模型进行了验证。实验结果表明,在一致性系数CC、相似性系数SC、综合指标IC 3个方面,SPCNN模型均优于所对比的PCNN模型、区域生长模型和RG-PCNN模型。
中图分类号:
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