Open AccessImproved colour‐to‐grey method using image segmentation and colour difference model for colour vision deficiency
Author(s) -
Zhang Liang,
Xu Qing,
Zhu Guangming,
Song Juan,
Zhang Xiangdong,
Shen Peiyi,
Wei Wei,
Shah Syed Afaq Ali,
Bennamoun Mohammed
Publication year - 2018
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.2017.0482
Subject(s) - artificial intelligence , grey level , grayscale , color difference , grey scale , computer vision , segmentation , image segmentation , image (mathematics) , computer science , mathematics , pattern recognition (psychology) , enhanced data rates for gsm evolution
Colour vision deficiency (CVD) is a genetic condition that has troubled people for a long time. This study proposes an improved colour‐to‐grey method for CVD using image segmentation and a colour difference model. In this method, the colour image is first segmented using a region growing method so that each region corresponds to one colour. Next, the colour difference is computed between arbitrary segmented region pairs. Finally, the greyscale image is obtained by minimising a target function. Experimental results show that compared with state‐of‐the‐art colour‐to‐grey methods, the proposed algorithm can improve the E ‐score by about 10.99%.