Open AccessSingle‐image shadow detection and removal using local colour constancy computation
Author(s) -
Yuan Xingsheng,
Ebner Marc,
Wang Zhengzhi
Publication year - 2015
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2014.0242
Subject(s) - shadow (psychology) , standard illuminant , artificial intelligence , computer vision , shadow mapping , color constancy , computation , computer science , pixel , anisotropic diffusion , image (mathematics) , pattern recognition (psychology) , algorithm , psychology , psychotherapist
This study is concerned with the problem of shadow detection and removal from single images of natural scenes. In this work, the authors propose a shadow detection method with a surface descriptor, termed colour‐shade, which allows them to include the physical considerations derived from the image formation model capturing gradual colour surface variations. The authors incorporate a colour‐shade descriptor into the condition random field model to find same illumination pairs and to obtain coherent shadow regions. The authors propose a shadow removal method using an improved local colour constancy computation, which uses anisotropic diffusion to estimate the illuminant locally for each image pixel in shadow. The authors evaluate their method on two shadow detection databases. The experimental results demonstrate that their shadow detection and removal method is state of the art.