Human Texture Vision as Multi-Order Spectral Analysis - Okada, Kosuke; Motoyoshi, Isamu (Frontiers in Computational Neuroscience, 2021)
Texture information plays a critical role in the rapid perception of scenes, objects, and materials. Here, we propose a novel model in which visual texture perception is essentially determined by the 1st-order (luminance) and 2nd-order (energy) spectra. This model is an extension of the Filter-Rectify-Filter model, and it also corresponds to the frequency representation of the Portilla-Simoncelli statistics. We show that preserving two spectra and randomizing phases of a natural texture image result in a perceptually similar texture, strongly supporting the model. Based on only two single spectral spaces, this model provides a simpler framework to describe and predict texture representations in the primate visual system. The idea of multi-order spectral analysis is consistent with the hierarchical processing principle of the visual cortex, which is approximated by a multi-layer convolutional network.