Humans can easily perceive illusory contours and complete missing forms in fragmented shapes. This work investigates whether such capability can arise in convolutional neural networks (CNNs) using deep structural priors computed directly from images. In this work, we present a framework that completes disconnected contours and connects fragmented lines and curves. In our framework, we propose a model that does not even need to know which regions of the contour are eliminated. We introduce an iterative process that completes an incomplete image and we propose novel measures that guide this to find regions it needs to complete. Our model trains on a single image and fills in the contours with no additional training data. Our work builds a robust framework to achieve contour completion using deep structural priors and extensively investigate how such a model could be implemented.

翻译：人类能够轻易感知到错觉轮廓，并在碎片化形状中补全缺失的形态。本研究探讨了这种能力是否可以通过直接从图像中计算出的深度结构先验，在卷积神经网络（CNN）中实现。我们提出了一种框架，用于补全断开的轮廓，并连接碎片化的线条与曲线。在该框架中，我们设计的模型甚至无需知道轮廓中哪些区域已被消除。我们引入了一个迭代过程，用于补全不完整的图像，并提出了新颖的度量标准，以引导该过程找到需要补全的区域。我们的模型基于单张图像进行训练，无需额外训练数据即可填充轮廓。本研究构建了一个基于深度结构先验实现轮廓补全的鲁棒框架，并深入探讨了如何实现此类模型。