Contour-guided Image Completion with Perceptual Grouping
Morteza Rezanejad, Sidharth Gupta, Chandra Gummaluru, Ryan Marten,, John Wilder, Michael Gruninger, Dirk B. Walther

TL;DR
This paper modernizes the Stochastic Completion Fields algorithm to improve contour completion and inpainting in images, mimicking human perceptual grouping, and enhances edge detection in noisy environments.
Contribution
It introduces a modernized SCF model integrated into an image editing framework, demonstrating improved contour completion, inpainting guidance, and noise robustness, aligning with human visual perception.
Findings
SCF-based contours improve inpainting results
Guided inpainting outperforms state-of-the-art models
SCF enhances edge detection in noisy images
Abstract
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,…
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Taxonomy
TopicsGenerative Adversarial Networks and Image Synthesis · Advanced Vision and Imaging · Advanced Image Processing Techniques
