Human peripheral blur is optimal for object recognition.

ABSTRACT: Our vision is sharpest at the centre of our gaze and becomes progressively blurry into the periphery. It is widely believed that this high foveal resolution evolved at the expense of peripheral acuity. But what if this sampling scheme is actually optimal for object recognition? To test this hypothesis, we trained deep neural networks on "foveated" images mimicking how our eyes sample the visual field: objects (wherever they were in the image) were sampled at high resolution, and their surroundings were sampled with decreasing resolution away from the objects. Remarkably, networks trained with the known human peripheral blur profile yielded the best performance compared to networks trained on shallower and steeper blur profiles, and compared to baseline state-of-the-art networks trained on

SUBMITTER: Pramod RT 

PROVIDER: S-EPMC7614542 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature