【摘要】 目的 基于初级视觉皮层非经典感受野亚区外周抑制特性机理，提出一种新型轮廓检测方法。方法 模拟初级视皮层经典感受野受到外部刺激的响应特性，构建多方位二维Gabor滤波器模型，实现初级轮廓的提取。之后基于初级皮层非经典感受野亚区的结构特性，提出非经典感受野亚区侧抑制模型，实现纹理抑制。对于视觉信息传递的前馈机制，采用二维Gaussian函数模拟神经节细胞对信息的处理，信息跨层级传递，提高响应速率。模拟人眼捕捉全局信息的特性，进行轮廓修正，最终得到轮廓图。结果 针对RUG40图像库的测试，与其他轮廓检测算法进行定性和定量分析比较；BSDS500中任意200张图像的平均准确度（AP）达0.703。结论 新型轮廓检测方法能更有效突显主体轮廓并且抑制纹理背景。更多还原

【Abstract】 Objective This paper proposes a novel contour detection method inspired by the surround inhibition mechanism of the primary visual cortex. Methods The method involves simulating the response characteristics of the classical receptive field in the primary visual cortex to external stimuli and constructing a multi-directional two-dimensional Gabor filter model for extracting primary contours. A non-classical receptive subfield surround suppression model is proposed based on the structural characteristics of the non-classical receptive subfield for texture suppression. Additionally,a two-dimensional Gaussian function is used to simulate information processing by ganglion cells, and information is transmitted across levels to improve the response rate. Finally, the characteristics of capturing global information by the human eye are simulated to correct the contours and obtain the final contour map. Results Qualitative and quantitative analysis compared with other existing contour detection algorithms; The average accuracy(AP) of any 200 images in the BSDS500 reached 0.703. Conclusion the results show that the proposed algorithm can more effectively highlight the contour of the subject and suppress the texture background.更多还原