基于V1细胞特性的边缘检测

针对均衡边缘检测精度和抗噪性能难度大的问题，借鉴初级视皮层（V1）细胞的动静态感知特性，建立具有方位选择性的V1细胞模型应用于图像边缘检测。采用时空滤波器来模拟简单细胞的感受野，通过使用能量模型和归一化来整合简单细胞的响应得到V1细胞模型，从而利用V1细胞静态感知特性来检测自然图像边缘。仿真结果表明，所提V1细胞模型能够基本拟合生物数据，具有生物上的普适性；与传统的边缘检测算子相比，该模型的性能更优，鲁棒性更强。依据生物实验结论来构建生物视觉模型并用于图像处理，对生物视觉和计算机视觉的融合进行了有益的探索。

Edge detection based on characteristic of V1 cells

As for balancing the accuracy of edge detection and the difficulty of anti-noise performance, and referring to the dynamic and static perceptions of the primary Visual cortex（V1） cells, this paper proposes an orientation selectivity V1 model which is applied to image edge detection. Spatio temporal filters are adopted to simulate the receptive fields of the V1 simple cell. The response of simple cells is obtained by using the energy model and normalization to get the V1 cells model. Thus the natural image edge is detected by using static perception of V1 cells. Simulation results show that the V1 model can basically fit the biological data and has the universality of biology. What’s more, compared with the traditional edge detection operators, the proposed model is more effective and has better robustness. Based on the biological experimental results, this paper constructs a biological vision model and applies it to image processing, which makes a helpful exploration to the fusion of biological vision and computer vision.