Affiliation:
1. Faculty of Materials Science and Ceramics, AGH University of Krakow, al. Mickiewicza 30, 30-059 Kraków, Poland
Abstract
Color distortion in an image presents a challenge for machine learning classification and regression when the input data consists of pictures. As a result, a new algorithm for color standardization of photos is proposed, forming the foundation for a deep neural network regression model. This approach utilizes a self-designed color template that was developed based on an initial series of studies and digital imaging. Using the equalized histogram of the R, G, B channels of the digital template and its photo, a color mapping strategy was computed. By applying this approach, the histograms were adjusted and the colors of photos taken with a smartphone were standardized. The proposed algorithm was developed for a series of images where the entire surface roughly maintained a uniform color and the differences in color between the photographs of individual objects were minor. This optimized approach was validated in the colorimetric determination procedure of vitamin C. The dataset for the deep neural network in the regression variant was formed from photos of samples under two separate lighting conditions. For the vitamin C concentration range from 0 to 87.72 µg·mL−1, the RMSE for the test set ranged between 0.75 and 1.95 µg·mL−1, in comparison to the non-standardized variant, where this indicator was at the level of 1.48–2.29 µg·mL−1. The consistency of the predicted concentration results with actual data, expressed as R2, ranged between 0.9956 and 0.9999 for each of the standardized variants. This approach allows for the removal of light reflections on the shiny surfaces of solutions, which is a common problem in liquid samples. This color-matching algorithm has universal character, and its scope of application is not limited.
Reference49 articles.
1. Fairchild, M.D. (2013). Color Appearance Models, John Wiley & Sons, Ltd.. [3rd ed.].
2. Evaluation of RGB Cube Calibration Framework and Effect of Calibration Charts on Color Measurement of Mozzarella Cheese;Minz;J. Food Meas. Charact.,2019
3. Ernst, A., Papst, A., Ruf, T., and Garbas, J.U. (2013, January 6–7). Check My Chart: A Robust Color Chart Tracker for Colorimetric Camera Calibration. Proceedings of the 6th International Conference on Computer Vision/Computer Graphics Collaboration Techniques and Applications, MIRAGE’13, Berlin, Germany.
4. Color-Rendition Chart;McCamy;J. Appl. Photogr. Eng.,1976
5. Color Calibration of Digital Images for Agriculture and Other Applications;Sunoj;ISPRS J. Photogramm. Remote Sens.,2018