Improved colour‐to‐grey method using image segmentation and colour difference model for colour vision deficiency | Zendy

Zhang Liang | Zendy; Xu Qing | Zendy; Zhu Guangming | Zendy; Song Juan | Zendy; Zhang Xiangdong | Zendy; Shen Peiyi | Zendy; Wei Wei | Zendy; Shah Syed Afaq Ali | Zendy; Bennamoun Mohammed | Zendy

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Improved 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%.

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