Gloss perception: Searching for a deep neural network that behaves like humans.

ABSTRACT: The visual computations underlying human gloss perception remain poorly understood, and to date there is no image-computable model that reproduces human gloss judgments independent of shape and viewing conditions. Such a model could provide a powerful platform for testing hypotheses about the detailed workings of surface perception. Here, we made use of recent developments in artificial neural networks to test how well we could recreate human responses in a high-gloss versus low-gloss discrimination task. We rendered >70,000 scenes depicting familiar objects made of either mirror-like or near-matte textured materials. We trained numerous classifiers to distinguish the two materials in our images-ranging from linear classifiers using simple pixel statistics to convolutional neural networks

SUBMITTER: Prokott KE 

PROVIDER: S-EPMC8626854 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature