Color calibration and characterization of particle images

Apply

Color calibration and characterization of particle images

Journal Title: China Powder Science and Technology - Year 2024, Vol 30, Issue 4

Abstract

Objective Particle color is an important parameter across various sectors， reflecting the composition， purity， and quality of particles. Different particles colors may also have different physical and chemical properties. At present， image-based particle characterization mainly focuses on particle size and shape， and the characterization of particle color has not been systematically studied. The national standard GB/T 38879-2020 Color Image Analysis for Particle Size Analysis and the international standard ISO/PWI TS 19673 Particle characterization - Color image analysis， which is led by China， recognize that particle color is another important parameter in particle image-based analysis besides particle size and shape. However， the spectral distribution of the light source in imaging system， the absorption of lens to the light， and the spectral response of the camera sensor will affect the color properties of the captured particle images. Therefore， it is necessary to reduce the influence caused by the above factors through color correction. Additionally， the paper addressed how to quantitatively and intuitively characterize the color and distribution of particles and particle groups. Methods In this paper， a particle color measurement device was built， using a color card to calibrate the color of the device. We compared and verified the correction effects of 6 common color correction algorithms under the illumination of white LED and halogen lamps. The device was also used to capture particle images from drug capsules， and the color correction algorithm was used to correct the color of the particle images. The color information of particles was extracted and characterized by processing the captured particle images. Results and Discussion Through the verification of the 6 common polynomial color correction algorithms， it was found that the linear color correction algorithm with white balance constraint had lower regression accuracy. After correction， the average color difference of the 24 color blocks in the color card was reduced from 38.67 to 13.61 under the illumination of white LED light source， and reduced from 43.13 to 21.52 under the illumination of halogen lamps. This color correction algorithm maintained good white balance. The third-order polynomial color correction algorithm had the highest regression accuracy. Under the illumination of white LED light source， the average color difference of 24 color blocks in the color card was reduced from 38.67 to 3.82 after correction， and the average color difference was reduced from 43.13 to 3.92 under the illumination of halogen lamps. The third-order root polynomial color correction algorithm ranked the second. Under the illumination of white LED light source， the average color difference of 24 color blocks in the color card was reduced from 38.67 to 4.15 after correction by using this color correction algorithm， and from 43.13 to 4.24 after correction under the illumination of halogen lamps. Experiments showed that， unlike the third-order polynomial color correction algorithm， the third-order root polynomial color correction algorithm demonstrated good exposure invariance with no color deviation across different exposure intensities. Conclusion In this paper， the color card was used to calibrate the imaging system under two kinds of light sources， and 6 kinds of commonly used color correction algorithms were used to correct the captured particle images. The experimental results showed that using color cards to calibrate the imaging system could effectively reduce the color bias in the imaging system， thereby theoretically aligning the particle color more closely to the RGB values under the D65 standard light source. Among the 6 color correction algorithms tested in this paper， the third-order root polynomial color correction algorithm showed good performance in terms of correction efficiency and exposure invariance， making it the recommended choice for color correction. For the characterization of particle colors， this paper transformed the average RGB values of particles to their corresponding chromaticity coordinates. The particle count was used to describe the relationship between the particle color and count in the particle system. Color moments and main colors were used to represent the color characteristics of individual particles， among which， the main color was the area where the color of particles was most concentrated in the Lab color space. This approach is beneficial in mitigating extraction errors of the main color caused by uniform color values appearing locally in the particle image.

Authors and Affiliations

Zuojie HUANG, Wu ZHOU, Xiqing XU, Changrong PEI, Tianyi CAI, Xiaoshu CAI

Keywords

EP ID EP749268
DOI 10.13732/j.issn.1008-5548.2024.04.010
Views 25
Downloads 0