Our eyes sample a disproportionately large amount of information at the centre of gaze with increasingly sparse sampling into the periphery. This sampling scheme is widely believed to be a wiring constraint whereby high resolution at the centre is achieved by sacrificing spatial acuityin the periphery. Here we propose that this sampling scheme may be optimal for object recognition because the relevant spatial content is dense near an object and sparse in the surrounding vicinity. We tested this hypothesisby training deep convolutional neural networks on full-resolution and foveatedimages. Our main finding is that networks trained on images with foveated sampling show better object classification compared to networks trained on full resolution images. Importantly, blurring images according to the human blur function yielded the best performance compared to images with shallower or steeper blurring. Taken together our results suggest that, peripheral blurring in our eyes may have evolved for optimal object recognition , rather than merely to satisfy wiring constraints.
Our vision is sharpest at the center 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 with high resolution near objects and increasingly sparse sampling into the periphery. Neural networks trained using a blur profile matching the human eye yielded the best performance compared to shallower and steeper blur profiles. Even in humans, categorization accuracy deteriorated only for steeper blur profiles. Thus, our blurry peripheral vision may have evolved to optimize object recognition rather than merely due to wiring constraints.
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full-resolution and foveatedimages. Our main finding is that networks trained on images with foveated sampling show better object classification compared to networks trained on full resolution images. Importantly, blurring images according to the human blur function
'foveated' images with high resolution near objects and increasingly sparse sampling into the periphery. Neural networks trained using a blur profile matching the human eye
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images with shallower or steeper blurring. Taken together our results suggest that, peripheral blurring in our eyes
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