Aeronautical Engineering College, Air Force Engineering University, Xi'an 710038, China.
Unmanned System Research Institute, Northwestern Polytechnical University, Xi'an 710072, China.
Sensors (Basel). 2018 Aug 4;18(8):2559. doi: 10.3390/s18082559.
Contour is a very important feature in biological visual cognition and has been extensively investigated as a fundamental vision problem. In connection with the limitations of conventional models in detecting image contours in complex scenes, a hierarchical image contour extraction method is proposed based on the biological vision mechanism that draws on the perceptual characteristics of the early vision for features such as edges, shapes, and colours. By simulating the information processing mechanisms of the cells' receptive fields in the early stages of the biological visual system, we put forward a computational model that combines feedforward, lateral, and feedback neural connections to decode and obtain the image contours. Our model simulations and their results show that the established hierarchical contour detection model can adequately fit the characteristics of the biological experiment, quickly and effectively detect the salient contours in complex scenes, and better suppress the unwanted textures.
轮廓是生物视觉认知中非常重要的特征，作为一个基本的视觉问题已经得到了广泛的研究。针对传统模型在复杂场景中检测图像轮廓的局限性，本文基于生物视觉机制，提出了一种分层图像轮廓提取方法，该方法借鉴了早期视觉对边缘、形状和颜色等特征的感知特性。通过模拟生物视觉系统早期阶段细胞感受野的信息处理机制，我们提出了一种结合前馈、侧部和反馈神经连接的计算模型，用于解码和获取图像轮廓。我们的模型模拟及其结果表明，建立的分层轮廓检测模型能够充分适应生物实验的特点，快速有效地检测复杂场景中的显著轮廓，并更好地抑制不需要的纹理。
