Quantitative evaluation of color difference between actual specimens and whole-slide imaging-scanned images calibrated with commercial color charts

Murayama, Yusuke; Sugiyama, Tohru; Ogino, Yoshihiko; Furuta, Hiroki; Kambara, Takaaki; Kamisono, Koi; Abe, Tokiya; Emoto, Katsura; Hashiguchi, Akinori; Kajimura, Yoichi; Sakamoto, Michiie

doi:10.1117/12.2611413

Cited by 1 publication

(2 citation statements)

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“…It could be fairly expensive regarding colorimetric accuracy. Even utilizing complex models like polynomial regression and neural networks [45,46,[48][49][50][51][52][53][54][55][56][57], some recent research has reported mediocre color accuracy [26][27][28][29][30]. In particular, a recent thesis by Yao in 2022 disclosed the typical color difference mistakes for various textile materials [58].…”

Section: Related Workmentioning

confidence: 99%

“…An up-to-date review of the challenge of spectral reconstruction from RGB images, in other words, the recovery of whole-scene hyperspectral (HS) information from a threechannel RGB image, is reported by Boaz Arad et al (2022).No doubt, hyperspectral imaging evolved for spectral proximity only and is too expensive, just as their processing algorithms are [26][27][28][29][30][31][32].…”

Section: Introductionmentioning

confidence: 99%

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Color and Texture Analysis of Textiles Using Image Acquisition and Spectral Analysis in Calibrated Sphere Imaging System-I

et al. 2022

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Numerous imaging applications and analyses demand human perception, and color space transformation of device-dependent tri-band color interpretation (RGB) to device-independent CIE color space standards needs human intervention. The imaging acquisition environment, theoretical conversion errors, viewing geometry, well-defined illumination uniformity, and calibration protocols limit their precision and applicability. It is unfortunate that in most image processing applications, the spectral data are either unavailable or immeasurable. This study is based on developing a novel integrating sphere imaging system and experimentation with textiles’ controlled variation of texture and color. It proposes a simple calibration technique and describes how unique digital color signatures can be derived from calibrated RGB derivatives to extract the best features for color and texture. Additionally, an alter-ego of reflectance function, missing in the imaging domain, is suggested that could be helpful for visualization, identification, and application for qualitative and quantitative color-texture analysis. Our further investigation revealed promising colorimetric results while validating color characterization and different color combinations over three textures.

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