中文摘要：

图像边缘检测所获得的目标轮廓细节质量，对于后续图像分析或理解过程具有重要作用。提出一种基于视觉机制的图像边缘检测新方法：构建抑制性突触连接的多层神经元群，在待检测图像的激励下，分析7像素×7像素视觉感受野窗口内互连接神经元的脉冲放电过程，记录发放时刻进行次序编码；同时考虑神经元之间的侧向抑制作用，在选择注意机制作用下获得增强图像；之后利用Log-Gabor滤波器模拟视觉系统中的方向选择特性，获取8个方向的滤波结果，经过输出层神经元群的融合处理并经灰度映射到0-255的范围后获得边缘图像。对含有丰富边缘细节特性的24幅菌落图像进行处理，其处理结果的ROC评价指标均值为0．698 4，优于PCNN法的0．659 3；从评价指标的均方差来看，该结果具有更好的一致性。另外，从信息熵评价指标来看，该方法同样具有一定的优势，能够有效提取图像的边缘信息，而且也能反映更多层次的图像细节。所提出的方法为利用视觉生理特性进行图像处理提供了崭新而有效的思路。

英文摘要：

Details of objects＇ contour can be acquired from the edge detection, which are considered important for image analysis and understanding correctly. In this paper, a new method for image edge detection was proposed based on vision mechanism. Multilayer neuronal population with inhibitory synapse was constructed to receive the stimuli from an awaited image, and the process of pulse spiking from connected neurons in the 7 x 7 window of visual receptive field was analyzed, spiking times were recorded for rank coding. Considering the effect of lateral inhibition between neurons, images with the mechanism of selective attention were enhanced; then Log-Gabor filter was adopted to stimulate the orientation selectivity of visual system for obtaining the filtering results of 8 orientations, the edge map could be acquired through integration processing of the output neuron layer and gray mapping to the range between 0 and 255. Taken the colony images with abundant edge details for processing, the mean value of ROC index for the results was 0. 698 4, better than that of PCNN, which was 0. 659 3 ; the results of the new method were better in the view of consistency in terms of the mean square deviation. Additionally, the new method also owning certain advantages in terms of information entropy, indicating the method proposed could extract edge information effectively and reflect image details in more levels. The method of edge detection proposed in the paper provides a new and effective idea for the image processing based on visual physiological characteristics.