Image-Based Quantification of Color and Its Machine Vision and Offline Applications

• Published in: Technologies (Basel) Vol. 11; no. 2; p. 49
• Main Authors: Yoo, Woo Sik, Kang, Kitaek, Kim, Jung Gon, Yoo, Yeongsik
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.03.2023
• Subjects: Agricultural engineering; Chinese medicine; Color; color sensing; Colorimetry; Cultural heritage; Data acquisition; Digital cameras; Digital imaging; Environmental engineering; Food processing; Image analysis; Image processing; Information retrieval; Light; Machine vision; Medical imaging; Medical science; Methods; offline analysis; Pixels; Quality control; Sensors; Smart phones; Smartphones; Software; Statistical analysis; Vision systems; United States; United States > US
• ISSN: 2227-7080; 2227-7080
• DOI: 10.3390/technologies11020049

Image-based colorimetry has been gaining relevance due to the wide availability of smart phones with image sensors and increasing computational power. The low cost and portable designs with user-friendly interfaces, and their compatibility with data acquisition and processing, are very attractive for interdisciplinary applications from art, the fashion industry, food science, medical science, oriental medicine, agriculture, geology, chemistry, biology, material science, environmental engineering, and many other applications. This work describes the image-based quantification of color and its machine vision and offline applications in interdisciplinary fields using specifically developed image analysis software. Examples of color information extraction from a single pixel to predetermined sizes/shapes of areas, including customized regions of interest (ROIs) from various digital images of dyed T-shirts, tongues, and assays, are demonstrated. Corresponding RGB, HSV, CIELAB, Munsell color, and hexadecimal color codes, from a single pixel to ROIs, are extracted for machine vision and offline applications in various fields. Histograms and statistical analyses of colors from a single pixel to ROIs are successfully demonstrated. Reliable image-based quantification of color, in a wide range of potential applications, is proposed and the validity is verified using color quantification examples in various fields of applications. The objectivity of color-based diagnosis, judgment and control can be significantly improved by the image-based quantification of color proposed in this study.