The edges of an image contains rich visual cognitive cues. However, the edge information of a natural scene usually is only a set of disorganized unorganized pixels for a computer. In psychology, the phenomenon of quickly perceiving global information from a complex pattern is called the global precedence effect (GPE). For example, when one observes the edge map of an image, some contours seem to automatically “pop out” from the complex background. This is a manifestation of GPE on edge information and is called global contour precedence (GCP). The primary visual cortex (V1) is closely related to the processing of edges. In this article, a neural computational model to simulate GCP based on the mechanisms of V1 is presented. There are three layers in the proposed model: the representation of line segments, organization of edges, and perception of global contours. In experiments, the ability to group edges is tested on the public dataset BSDS500. The results show that the grouping performance, robustness, and time cost of the proposed model are superior to those of other methods. In addition, the outputs of the proposed model can also be applied to the generation of object proposals, which indicates that the proposed model can contribute significantly to high-level visual tasks.

中文翻译：

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基于初级视觉皮层机制的全局轮廓优先计算模型

图像的边缘包含丰富的视觉认知线索。然而，对于计算机来说，自然场景的边缘信息通常只是一组杂乱无章的像素。在心理学中，从复杂模式中快速感知全局信息的现象称为全局优先效应（GPE）。例如，当人们观察图像的边缘图时，一些轮廓似乎会自动从复杂的背景中“弹出”。这是 GPE 在边缘信息上的一种表现形式，称为全局轮廓优先级（GCP）。初级视觉皮层（V1）与边缘的处理密切相关。在本文中，提出了一种基于 V1 机制来模拟 GCP 的神经计算模型。所提出的模型中有三层：线段的表示，边缘的组织，和全局轮廓的感知。在实验中，在公共数据集 BSDS500 上测试了对边进行分组的能力。结果表明，该模型的分组性能、鲁棒性和时间成本均优于其他方法。此外，所提出模型的输出也可以应用于对象建议的生成，这表明所提出的模型可以对高级视觉任务做出重大贡献。