基于改进PCNN的番茄植株夜间图像分割算法

为实现番茄植株夜间图像分割,设计了一种基于最大类间方差法的改进脉冲耦合神经网络(PCNN)图像分割算法。该算法对传统PCNN模型中的链接输入项进行加权处理,在进行图像分割前,先基于最大类间方差(Otsu)算法获得阈值,再将该阈值赋值给改进PCNN模型中的链接输入项权值、突触链接系数β、链接权放大系数VE和阈值迭代衰减时间常数αE。对849幅番茄植株夜间图像进行试验,结果表明,图像分割正确率平均值为90. 43%,平均每幅图像分割时间为0. 994 4 s;输入链接项的加权处理可减少PCNN的迭代次数,提高算法的实时性;基于Otsu算法可实现改进PCNN模型的网络参数自适应设置。基于视觉效果、最大熵及分割正确率这3项评价指标的对比分析显示,改进PCNN模型的分割效果优于Otsu算法和传统PCNN模型,实时性优于传统PCNN模型。

Image Segmentation for Tomato Plants at Night Based on Improved PCNN

In order to realize the image segmentation for tomato plants at night, an improved pulse coupled neural network (PCNN) image segmentation algorithm was designed based on the maximum inter-group variance method. The algorithm weighted the link input in the traditional PCNN model. Before the image segmentation, the threshold was obtained based on the maximum inter-class variance (Otsu) algorithm, and then the threshold was assigned to the weight of the link input, the synaptic link coefficient , the link weight amplification factor and the threshold iterative decay time constant in the improved PCNN model. The results of 849 images of tomato plants at night showed that the average segmentation accuracy was 90.43% and the average segmentation time of one image was 0.9944s. The weighted processing of the link input could reduce the number of the iterations of improved PCNN and improve the real-time performance of the algorithm. Based on the Otsu algorithm, the network parameters can be set adaptively in the improved PCNN model. The comparative analysis based on the visual evaluation, the maximum entropy and the segmentation accuracy rate showed that the segmentation effect of improved PCNN model was better than those of the Otsu algorithm and the traditional PCNN model, and its real-time performance was also better than that of the traditional PCNN model.