Literature DB >> 28697677

Material Perception.

Roland W Fleming1.   

Abstract

Under typical viewing conditions, human observers effortlessly recognize materials and infer their physical, functional, and multisensory properties at a glance. Without touching materials, we can usually tell whether they would feel hard or soft, rough or smooth, wet or dry. We have vivid visual intuitions about how deformable materials like liquids or textiles respond to external forces and how surfaces like chrome, wax, or leather change appearance when formed into different shapes or viewed under different lighting. These achievements are impressive because the retinal image results from complex optical interactions between lighting, shape, and material, which cannot easily be disentangled. Here I argue that because of the diversity, mutability, and complexity of materials, they pose enormous challenges to vision science: What is material appearance, and how do we measure it? How are material properties estimated and represented? Resolving these questions causes us to scrutinize the basic assumptions of mid-level vision.

Entities:  

Keywords:  deep learning; mid-level vision; reflectance; surfaces; texture; transparency

Mesh:

Year:  2017        PMID: 28697677     DOI: 10.1146/annurev-vision-102016-061429

Source DB:  PubMed          Journal:  Annu Rev Vis Sci        ISSN: 2374-4642            Impact factor:   6.422


  14 in total

Review 1.  The perception of colour and material in naturalistic tasks.

Authors:  David H Brainard; Nicolas P Cottaris; Ana Radonjić
Journal:  Interface Focus       Date:  2018-06-15       Impact factor: 3.906

2.  The relative contribution of color and material in object selection.

Authors:  Ana Radonjić; Nicolas P Cottaris; David H Brainard
Journal:  PLoS Comput Biol       Date:  2019-04-12       Impact factor: 4.475

3.  Expectations affect the perception of material properties.

Authors:  Lorilei M Alley; Alexandra C Schmid; Katja Doerschner
Journal:  J Vis       Date:  2020-11-02       Impact factor: 2.240

4.  The role of contextual materials in object recognition.

Authors:  Tim Lauer; Filipp Schmidt; Melissa L-H Võ
Journal:  Sci Rep       Date:  2021-11-09       Impact factor: 4.379

5.  Computational luminance constancy from naturalistic images.

Authors:  Vijay Singh; Nicolas P Cottaris; Benjamin S Heasly; David H Brainard; Johannes Burge
Journal:  J Vis       Date:  2018-12-03       Impact factor: 2.240

6.  Visual assessment of causality in the Poisson effect.

Authors:  Takahiro Kawabe
Journal:  Sci Rep       Date:  2019-10-18       Impact factor: 4.379

Review 7.  Learning to see stuff.

Authors:  Roland W Fleming; Katherine R Storrs
Journal:  Curr Opin Behav Sci       Date:  2019-12

8.  Material constancy in perception and working memory.

Authors:  Hiroyuki Tsuda; Munendo Fujimichi; Mikuho Yokoyama; Jun Saiki
Journal:  J Vis       Date:  2020-10-01       Impact factor: 2.240

9.  Luminosity thresholds of colored surfaces are determined by their upper-limit luminances empirically internalized in the visual system.

Authors:  Takuma Morimoto; Ai Numata; Kazuho Fukuda; Keiji Uchikawa
Journal:  J Vis       Date:  2021-12-01       Impact factor: 2.240

10.  Unsupervised learning predicts human perception and misperception of gloss.

Authors:  Katherine R Storrs; Barton L Anderson; Roland W Fleming
Journal:  Nat Hum Behav       Date:  2021-05-06
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