論文

査読有り
2015年10月

Seeing liquids from static snapshots

VISION RESEARCH
  • Vivian C. Paulun
  • ,
  • Takahiro Kawabe
  • ,
  • Shin'ya Nishida
  • ,
  • Roland W. Fleming

115
Pt B
開始ページ
163
終了ページ
174
記述言語
英語
掲載種別
研究論文(学術雑誌)
DOI
10.1016/j.visres.2015.01.023
出版者・発行元
PERGAMON-ELSEVIER SCIENCE LTD

Perceiving material properties can be crucial for many tasks such as determining food edibility, or avoiding getting splashed yet the visual perception of materials remains poorly understood. Most previous research has focussed on optical characteristics (e.g., gloss, translucency). Here, however, we show that shape also provides powerful visual cues to material properties. When liquids pour, splash or ooze, they organize themselves into characteristic shapes, which are highly diagnostic of the material's properties. Subjects viewed snapshots of simulated liquids of different viscosities, and rated their similarity. Using maximum likelihood difference scaling (Maloney & Yang, 2003), we reconstructed perceptual scales for perceived viscosity as a function of the physical viscosity of the simulated fluids. The resulting psychometric function revealed a distinct sigmoidal shape, distinguishing runny liquids that flow easily from viscous gels that clump up into piles. A parameter-free model based on 20 simple shape statistics predicted the subjects' data surprisingly well. This suggests that when subjects are asked to compare the viscosity of static snapshots of liquids that differ only in terms of viscosity, they rely primarily on relatively simple measures of shape similarity. (C) 2015 The Authors. Published by Elsevier Ltd.

リンク情報
DOI
https://doi.org/10.1016/j.visres.2015.01.023
PubMed
https://www.ncbi.nlm.nih.gov/pubmed/25676882
Web of Science
https://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=JSTA_CEL&SrcApp=J_Gate_JST&DestLinkType=FullRecord&KeyUT=WOS:000362861500002&DestApp=WOS_CPL
ID情報
  • DOI : 10.1016/j.visres.2015.01.023
  • ISSN : 0042-6989
  • eISSN : 1878-5646
  • PubMed ID : 25676882
  • Web of Science ID : WOS:000362861500002

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