100710 20230519145407.0 doi 10.1167/jov.21.2.2 sideral 122751 ART-2021-122751 eng Lagunas, Manuel Universidad de Zaragoza The joint role of geometry and illumination on material recognition 2021 Access copy available to the general public Unrestricted Observing and recognizing materials is a fundamental part of our daily life. Under typical viewing conditions, we are capable of effortlessly identifying the objects that surround us and recognizing the materials they are made of. Nevertheless, understanding the underlying perceptual processes that take place to accurately discern the visual properties of an object is a long-standing problem. In this work, we perform a comprehensive and systematic analysis of how the interplay of geometry, illumination, and their spatial frequencies affects human performance on material recognition tasks. We carry out large-scale behavioral experiments where participants are asked to recognize different reference materials among a pool of candidate samples. In the different experiments, we carefully sample the information in the frequency domain of the stimuli. From our analysis, we find significant first-order interactions between the geometry and the illumination, of both the reference and the candidates. In addition, we observe that simple image statistics and higher-order image histograms do not correlate with human performance. Therefore, we perform a high-level comparison of highly nonlinear statistics by training a deep neural network on material recognition tasks. Our results show that such models can accurately classify materials, which suggests that they are capable of defining a meaningful representation of material appearance from labeled proximal image data. Last, we find preliminary evidence that these highly nonlinear models and humans may use similar high-level factors for material recognition tasks. info:eu-repo/grantAgreement/EC/H2020/682080/EU/Intuitive editing of visual appearance from real-world datasets/CHAMELEON This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 682080-CHAMELEON info:eu-repo/grantAgreement/EC/H2020/765121/EU/DyViTo: Dynamics in Vision and Touch - the look and feel of stuff/DyViTo This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 765121-DyViTo info:eu-repo/grantAgreement/EC/H2020/956585/EU/Predictive Rendering In Manufacture and Engineering/PRIME This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 956585-PRIME info:eu-repo/grantAgreement/ES/MINECO/PID2019-105004GB-I00 info:eu-repo/grantAgreement/ES/MINECO/TIN2016-78753-P info:eu-repo/semantics/openAccess by-nc-nd http://creativecommons.org/licenses/by-nc-nd/3.0/es/ 2.004 2021 OPHTHALMOLOGY 44 / 62 = 0.71 2021 Q3 T3 0.79 2021 Sensory Systems 2021 Q2 Ophthalmology 2021 Q2 3.2 2021 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Serrano, Ana Universidad de Zaragoza (orcid)0000-0002-7796-3177 Gutiérrez, Diego Universidad de Zaragoza (orcid)0000-0002-7503-7022 Masiá, Belén Universidad de Zaragoza (orcid)0000-0003-0060-7278 5007 570 Universidad de Zaragoza Dpto. Informát.Ingenie.Sistms. Área Lenguajes y Sistemas Inf. 21, 2 (2021), 2[18 pp.] J. Vision Journal of Vision 1534-7362 2670449 http://zaguan.unizar.es/record/100710/files/texto_completo.pdf Versión publicada 3236255 http://zaguan.unizar.es/record/100710/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:100710 articulos driver 2023-05-18-13:48:35 ARTICLE