Color Research and Application最新文献

Hyperspectral imaging has become popular for a vast number of applications. It allows the analysis of the spectral radiometric, photometric, and colorimetric properties of images on a pixel-by-pixel level. To measure the pixel-level absolute spectral radiance of images, researchers have proposed several absolute radiometric calibration methods of hyperspectral cameras, but these methods are challenging to apply. This paper demonstrates a method to characterize and calibrate an economical commercial hyperspectral camera (Specim IQ) using affordable instruments: a factory-calibrated spectroradiometer, a Helium lamp, and a stable broadband light source. The dark signal is stable and uniform; no flat-field correction is needed if a 2% error can be tolerated. However, a 1.88 nm wavelength correction must be applied. Through this method, the camera, which is designed to measure spectral reflectance, can be used to measure the absolute spectral radiance of a scene by a simple equation. The calibration result was validated by comparing the reconstructed spectra of an OLED panel and a Macbeth ColorChecker with the spectroradiometer. The camera can achieve an average accuracy of about 4% in radiance, 4% in luminance, Δ_{u′, v′} 0.005 in chromaticity, and a total color difference of 2.5 ΔE*_ab. This performance is comparable with a factory-calibrated colorimetric camera. The paper demonstrates that hyperspectral imaging can be done with reasonable accuracy and affordability, and it can be a powerful tool in lighting and visual perception research.

The biannual Sphere Award recognizes individuals, teams, or groups for their significant and long-lasting dedication to and impact on the success of the International Colour Association (AIC). This newly introduced award pays tribute to those who have gone above and beyond to advance the AIC's mission and the interests of its members, reflecting the association's core values of collaboration, innovation, and service. The first recipient of the AIC Sphere Award is José Luis Caivano, Ph.D., a professor at the University of Buenos Aires in Argentina, who was honored at the AIC Congress in Taipei in 2025.

Color is a crucial factor in the textile industry, directly affecting product quality and customer satisfaction. Traditionally, fabric color measurement has primarily relied on spectroscopic methods and colorimeters, which require direct contact with the fabric samples and often involve high costs. With the innovations of image processing technology, image-based color measurement (IBCM) methods have provided new possibilities thanks to their flexibility and automation potential. This paper provides an overview of IBCM approaches, including multispectral imaging systems, hyperspectral imaging, digital cameras, and scanners. Recent studies have shown that the application of artificial intelligence (AI) and computer vision in color measurement can enhance measurement accuracy and stability. However, this method still faces many challenges, such as the influence of ambient lighting, camera angles, fabric materials, and discrepancies among measuring devices. In addition, the work discusses color correction techniques aimed at improving the IBCM's accuracy, including spectral reflectance reconstruction, color balancing, and machine learning algorithms. Furthermore, the paper analyzes the applicability of IBCM technology in industrial textile quality control, and proposes future research directions, such as developing advanced AI algorithms, integrating Internet of Things (IoT) technology, and establishing standards for IBCM. The findings suggest that, despite remaining challenges, IBCM is emerging as a promising solution to replace or complement traditional color measurement methods in the textile industry.

Color is a crucial factor in the textile industry, directly affecting product quality and customer satisfaction. Traditionally, fabric color measurement has primarily relied on spectroscopic methods and colorimeters, which require direct contact with the fabric samples and often involve high costs. With the innovations of image processing technology, image-based color measurement (IBCM) methods have provided new possibilities thanks to their flexibility and automation potential. This paper provides an overview of IBCM approaches, including multispectral imaging systems, hyperspectral imaging, digital cameras, and scanners. Recent studies have shown that the application of artificial intelligence (AI) and computer vision in color measurement can enhance measurement accuracy and stability. However, this method still faces many challenges, such as the influence of ambient lighting, camera angles, fabric materials, and discrepancies among measuring devices. In addition, the work discusses color correction techniques aimed at improving the IBCM's accuracy, including spectral reflectance reconstruction, color balancing, and machine learning algorithms. Furthermore, the paper analyzes the applicability of IBCM technology in industrial textile quality control, and proposes future research directions, such as developing advanced AI algorithms, integrating Internet of Things (IoT) technology, and establishing standards for IBCM. The findings suggest that, despite remaining challenges, IBCM is emerging as a promising solution to replace or complement traditional color measurement methods in the textile industry.