Development of Cleaning Evaluation Application Using Hue Change as a Parameter




cleaning, color, hue, image analysis, iron dioxide, Kubelka-Munk formula


In the cleaning field, there is a method to evaluate the amount of soiling by processing the color signal of a soiled sample’s image using the Kubelka-Munka formula, but this method could not be adapted to the case where the hue changes drastically during cleaning or washing. In this study, we aimed to construct a system to quantify the amount of soiling corresponding to the hue change. In other word, this is the sturdy for image analysis of individual measuring devices, which is an extension of the cleaning rate analysis, especially in cleaning tests. We conducted a cleaning experiment using soiled fabrics of iron oxide as a representative dirt model of drastic hue changes because its hue changes from yellowish-brown to black by reduction process. Then we calculated the cleaning rate of the dirt on the soiled fabric by the image analysis, conventional method using Kubelka-Munka formula, and X-ray Fluorescence (XRF) analysis, and we evaluated the accuracy of the proposed method of the image analysis by comparing the results of XRF.As a result, first it was found that the hue changes of iron oxides due to reduction could be clearly captured as a color signal, and the degree of reduction of iron oxides could be determined from the Chromaticity Diagram Value, xy value. Using the results, we were able to construct an application that can easily determine the amount of soiling adhesion by recalculating the hue change as a variable. The cleaning rate calculated by the image analysis application showed a significant improvement compared to the one calculated by the conventional appearance evaluation method. This achievement has greatly expanded the range of objects for which the amount of soiling from the exterior can be evaluated.


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How to Cite

Tsukizawa, T., Nakamura, T., & Oya, M. (2022). Development of Cleaning Evaluation Application Using Hue Change as a Parameter. Image Analysis and Stereology, 41(2), 111–120.



Original Research Paper