Human peripheral blur is optimal for object recognition
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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 acuity in 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 hypothesis by training deep convolutional neural networks on full-resolution and foveated images. 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.
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