Distinguishing mirror from glass: A "big data" approach to material perception.

ABSTRACT: Distinguishing mirror from glass is a challenging visual inference, because both materials derive their appearance from their surroundings, yet we rarely experience difficulties in telling them apart. Very few studies have investigated how the visual system distinguishes reflections from refractions and to date, there is no image-computable model that emulates human judgments. Here we sought to develop a deep neural network that reproduces the patterns of visual judgments human observers make. To do this, we trained thousands of convolutional neural networks on more than 750,000 simulated mirror and glass objects, and compared their performance with human judgments, as well as alternative classifiers based on "hand-engineered" image features. For randomly chosen images, all classifiers and

SUBMITTER: Tamura H 

PROVIDER: S-EPMC8934559 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature