Year: 2025
Type: conference-abstract
Abstract: Color vision deficiency (CVD) is a visual condition caused by abnormalities in the retina's cone cells, impacting the ability to distinguish certain colors.This condition affects approximately 8% of males and 0.5% of females globally, with significant implications for interpreting visual information.Maps, as vital tools for spatial exploration, rely heavily on color differentiation to convey information such as land use, elevation, and geographic features.However, CVD users often struggle to discern these distinctions, reducing the usability of maps and increasing the risk of misinterpretation.This study addresses this gap by developing a novel map color transformation system tailored to CVD users.Using Daltonization techniques within the CIE Luv color space, the study enhances color differentiation for individuals with different types and degrees of CVD.The CIE Luv space, known for its perceptual accuracy, allows for precise manipulation of color distances, enabling transformations that maximize distinguishability while preserving naturalness.The method begins by clustering map colors using K-means clustering and calculating cluster centroids.These centroids are then adjusted based on Confusion Points and Lines unique to each type of CVD (protanopia, deuteranopia, and tritanopia).By optimizing the angular separation (θ) between clusters in polar coordinates, the system ensures improved color differentiation for CVD users.
Source: Abstracts of the ICA
Authors: Jeon-Young Kang, Seungmin Cha, Nan-Ju Jung, Woonho Park, Sung Jun Moon
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4417325878
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