基于显著性与脉冲耦合神经网络的图像分割

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。

Image Segmentation Based on Saliency and Pulse Coupled Neural Network

Aiming at the problem that complicated images are interfered by background,an image segmentation method based on saliency and pulse coupled neural network (SPCNN) was proposed.Firstly,with the saliency filtering algorithm and the method of maximum between-class variance,the saliency map and the object image are obtained.Based on this,the interference which comes from the background for the initial seed point selection is eliminated.Secondly,according to saliency values in saliency map,the most saliency region is captured and the initial seed points are produced.Finally,the operations of object image segmentation are achieved via the improved RG-PCNN model.The experimental segmentation results of the gray natural images are obtained from the Berkeley segmentation dataset and ground truth database.There are three evaluating indicators:consistency coefficient(CC),similarity coefficient(SC) and integrate coefficient(IC).The experiment results show that the proposed model has better performance in terms of CC,SC and IC comparing with pulse coupled neural network (PCNN),region growing model (RG) and SPCNN.