A feedforward mechanism for human-like contour integration

Abstract

Deep neural network models provide a powerful experimental platform for exploring core mechanisms underlying human visual perception, such as perceptual grouping and contour integration -- the process of linking local edge elements to arrive at a unified perceptual representation of a complete contour. Here, we demonstrate that feedforward, nonlinear convolutional neural networks (CNNs) can emulate this aspect of human vision without relying on mechanisms proposed in prior work, such as lateral connections, recurrence, or top-down feedback. We identify two key inductive biases that give rise to human-like contour integration in purely feedforward CNNs: a gradual progression of receptive field sizes with increasing layer depth, and a bias towards relatively straight (gradually curved) contours. While lateral connections, recurrence, and feedback are ubiquitous and important visual processing mechanisms, these results provide a computational existence proof that a feedforward hierarchy is sufficient to implement gestalt "good continuation" mechanisms that detect extended contours in a manner that is consistent with human perception.