Edges are key components of any visual scene to the extent that we can recognise objects merely by their silhouettes. The human visual system captures edge information through neurons in the visual cortex that are sensitive to both intensity discontinuities and particular orientations. The “classical approach” assumes that these cells are only responsive to the stimulus present within their receptive fields, however, recent studies demonstrate that surrounding regions and inter-areal feedback connections influence their responses significantly. In this work we propose a biologically-inspired edge detection model in which orientation selective neurons are represented through the first derivative of a Gaussian function resembling double-opponent cells in the primary visual cortex (V1). In our model we account for four kinds of receptive field surround, i.e. full, far, iso- and orthogonal-orientation, whose contributions are contrast-dependant. The output signal from V1 is pooled in its perpendicular direction by larger V2 neurons employing a contrast-variant centre-surround kernel. We further introduce a feedback connection from higher-level visual areas to the lower ones. The results of our model on three benchmark datasets show a big improvement compared to the current non-learning and biologically-inspired state-of-the-art algorithms while being competitive to the learning-based methods.

中文翻译：

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反馈和环绕调制边界检测

边缘是任何视觉场景的关键组成部分，因为我们可以仅通过它们的轮廓来识别物体。人类视觉系统通过视觉皮层中的神经元捕获边缘信息，这些神经元对强度不连续性和特定方向都很敏感。“经典方法”假设这些细胞仅对其感受野中存在的刺激做出反应，然而，最近的研究表明，周围区域和区域间的反馈连接会显着影响它们的反应。在这项工作中，我们提出了一种受生物学启发的边缘检测模型，其中方向选择性神经元通过类似于初级视觉皮层 (V1) 中的双对手细胞的高斯函数的一阶导数来表示。在我们的模型中，我们考虑了四种感受野环绕，即全方向、远方向、等向和正交方向，它们的贡献与对比度有关。来自 V1 的输出信号在其垂直方向上由较大的 V2 神经元汇集，采用对比度变化的中心环绕内核。我们进一步引入了从高级视觉区域到低级视觉区域的反馈连接。我们的模型在三个基准数据集上的结果显示，与当前的非学习和生物启发的最先进算法相比，有了很大的改进，同时与基于学习的方法具有竞争力。我们进一步引入了从高级视觉区域到低级视觉区域的反馈连接。我们的模型在三个基准数据集上的结果显示，与当前的非学习和生物启发的最先进算法相比，有了很大的改进，同时与基于学习的方法具有竞争力。我们进一步引入了从高级视觉区域到低级视觉区域的反馈连接。我们的模型在三个基准数据集上的结果显示，与当前的非学习和生物启发的最先进算法相比，有了很大的改进，同时与基于学习的方法具有竞争力。