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10.5555/839277.840047guideproceedingsArticle/Chapter ViewAbstractPublication PagesConference Proceedingsacm-pubtype
Article

Determining wet surfaces from dry

Published: 20 June 1995 Publication History

Abstract

Wet surfaces are ubiquitous in our visual experience. Autonomous machines with vision systems will need to identify wet surfaces from dry. Wet surfaces (especially rough, absorbent ones) appear darker when wet. This paper presents the Lekner and Dorf (1988) model for describing the darkening caused by wetting. We explain how to use this optics model to transform intensity values of a region of an image to make that region appear wet. We also show how the model can be reversed in order to make a wet part of an image appear dry. It is also shown that this technique can be used to identify wet regions. This identification is contrasted with darkening caused by shadows. Comparisons of the gray-level histograms of these real images show the validity of this approach for distinguishing wet surfaces from dry.

Cited By

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  • (2019)Effect on the Performance of a Support Vector Machine Based Machine Vision System with Dry and Wet Ore Sample Images in Classification and Grade PredictionPattern Recognition and Image Analysis10.1134/S105466181901009729:2(309-324)Online publication date: 1-Apr-2019
  • (2007)Interactive rendering of optical effects in wet hairProceedings of the 2007 ACM symposium on Virtual reality software and technology10.1145/1315184.1315208(133-140)Online publication date: 5-Nov-2007
  • (2006)Time-varying BRDFsProceedings of the Second Eurographics conference on Natural Phenomena10.5555/2381370.2381373(15-23)Online publication date: 5-Sep-2006
  • Show More Cited By

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cover image Guide Proceedings
ICCV '95: Proceedings of the Fifth International Conference on Computer Vision
June 1995
ISBN:0818670428

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IEEE Computer Society

United States

Publication History

Published: 20 June 1995

Author Tags

  1. brightness
  2. computer vision
  3. darkening
  4. dry surfaces
  5. gray-level histograms
  6. image segmentation
  7. image texture
  8. light intensity values
  9. model reversal
  10. optics model
  11. rough absorbent surfaces
  12. shadows
  13. vision systems
  14. visual experience
  15. wet regions identification
  16. wet surfaces
  17. wetting

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Cited By

View all
  • (2019)Effect on the Performance of a Support Vector Machine Based Machine Vision System with Dry and Wet Ore Sample Images in Classification and Grade PredictionPattern Recognition and Image Analysis10.1134/S105466181901009729:2(309-324)Online publication date: 1-Apr-2019
  • (2007)Interactive rendering of optical effects in wet hairProceedings of the 2007 ACM symposium on Virtual reality software and technology10.1145/1315184.1315208(133-140)Online publication date: 5-Nov-2007
  • (2006)Time-varying BRDFsProceedings of the Second Eurographics conference on Natural Phenomena10.5555/2381370.2381373(15-23)Online publication date: 5-Sep-2006
  • (2006)Synthesis of material drying historyACM SIGGRAPH 2006 Courses10.1145/1185657.1185726(6-es)Online publication date: 30-Jul-2006
  • (2005)Synthesis of material drying historyProceedings of the First Eurographics conference on Natural Phenomena10.5555/2381356.2381358(7-16)Online publication date: 30-Aug-2005

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