Camouflage assessment: Machine and human

T.N. Volonakis*, O.E. Matthews, Eric Liggins, R.J. Baddeley, Nick Scott-Samuel, Innes Cuthill

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

13 Citations (Scopus)
120 Downloads (Pure)


A vision model is designed using low-level vision principles so that it can perform as a human observer model for camouflage assessment. In a camouflaged-object assessment task, using military patterns in an outdoor environment, human performance at detection and recognition is compared with the human observer model. This involved field data acquisition and subsequent image calibration, a human experiment, and the design of the vision model. Human and machine performance, at recognition and detection, of military patterns in two environments was found to correlate highly. Our model offers an inexpensive, automated, and objective method for the assessment of camouflage where it is impractical, or too expensive, to use human observers to evaluate the conspicuity of a large number of candidate patterns. Furthermore, the method should generalize to the assessment of visual conspicuity in non-military contexts.

Original languageEnglish
Pages (from-to)173-182
Number of pages10
JournalComputers in Industry
Early online date3 Apr 2018
Publication statusPublished - 1 Aug 2018

Structured keywords

  • Cognitive Science
  • Visual Perception


  • Camouflage Assessment
  • Observer Modelling
  • Visual Search


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