  Humans are excellent at perceiving illusory outlines. We are readily able to
complete contours, shapes, scenes, and even unseen objects when provided with
images that contain broken fragments of a connected appearance. In vision
science, this ability is largely explained by perceptual grouping: a
foundational set of processes in human vision that describes how separated
elements can be grouped. In this paper, we revisit an algorithm called
Stochastic Completion Fields (SCFs) that mechanizes a set of such processes --
good continuity, closure, and proximity -- through contour completion. This
paper implements a modernized model of the SCF algorithm, and uses it in an
image editing framework where we propose novel methods to complete fragmented
contours. We show how the SCF algorithm plausibly mimics results in human
perception. We use the SCF completed contours as guides for inpainting, and
show that our guides improve the performance of state-of-the-art models.
Additionally, we show that the SCF aids in finding edges in high-noise
environments. Overall, our described algorithms resemble an important mechanism
in the human visual system, and offer a novel framework that modern computer
vision models can benefit from.
