  It is well-known that modern computer vision systems often exhibit behaviors
misaligned with those of humans: from adversarial attacks to image corruptions,
deep learning vision models suffer in a variety of settings that humans capably
handle. In light of these phenomena, here we introduce another, orthogonal
perspective studying the human-machine vision gap. We revisit the task of
recovering images under degradation, first introduced over 30 years ago in the
Recognition-by-Components theory of human vision. Specifically, we study the
performance and behavior of neural networks on the seemingly simple task of
classifying regular polygons at varying orders of degradation along their
perimeters. To this end, we implement the Automated Shape Recoverability Test
for rapidly generating large-scale datasets of perimeter-degraded regular
polygons, modernizing the historically manual creation of image recoverability
experiments. We then investigate the capacity of neural networks to recognize
and recover such degraded shapes when initialized with different priors.
Ultimately, we find that neural networks' behavior on this simple task
conflicts with human behavior, raising a fundamental question of the robustness
and learning capabilities of modern computer vision models.
