The Transformation of RGB Images to Munsell Soil-Color Charts

Authors

DOI:

https://doi.org/10.15359/ru.36-1.36

Keywords:

Munsell color space, RGB color space, transformation, Munsell soil color charts, machine learning, neuronal networks

Abstract

[Objective] The transformation from RGB to Munsell color space is a relevant issue for different tasks, such as the identification of soil taxonomy, organic materials, rock materials, skin types, among others. This research aims to develop alternatives based on feedforward networks and the convolutional neural networks to predict the hue, value, and chroma in the Munsell soil-color charts (MSCCs) from RGB images. [Methodology] We used images of Munsell soil-color charts from 2000 and 2009 versions taken from Millota et al. (2018) to train and test the models. A division of 2856 images in 10% for testing, 20% for validation, and 70% for training was used to build the models. [Results] The best approach was the convolutional neural networks for classification with 93% of total accuracy of hue, value, and chroma combination; it comprises three CNN, one for the hue prediction, another for value prediction, and the last one for chroma prediction. However, the three best models show closeness between the prediction and real values according to the CIEDE2000 distance. The cases classified incorrectly with this approach had a CIEDE2000 average of 0.27 and a standard deviation of 1.06. [Conclusions] The models demonstrated better color recognition in uncontrolled environments than the Transformation of Centore, which is the classical method to transform from RGB to HVC. The results were promising, but the model should be tested with real images at different applications, such as soil real images, to classify the soil color.

References

Afifi, M. & Brown, M. S. (2019). Sensor-independent illumination estimation for DNN models. arXiv preprint arXiv:1912.06888

Centore, P. (2011). An open‐source inversion algorithm for the Munsell renotation. Color Research & Application, 37(6), 455-464. https://doi.org/10.1002/col.20715

Domínguez Soto, J. M., Román Gutiérrez, A. D., Prieto García, F. & Acevedo Sandoval, O. (2018). Sistema de Notación Munsell y CIELab como herramienta para evaluación de color en suelos. Revista Mexicana de Ciencias Agrícolas, 3(1), 141–155. https://doi.org/10.29312/remexca.v3i1.1489

Gómez-Robledo, L., López-Ruiz, N., Melgosa, M., Palma, A. J., Capitán-Vallvey, L. F. & Sánchez-Marañón, M. (2013). Using the mobile phone as Munsell soil-colour sensor: An experiment under controlled illumination conditions. Computers and Electronics in Agriculture, 99, 200–208. https://doi.org/10.1016/j.compag.2013.10.002

Hernandez-Juarez, D., Parisot, S., Busam, B., Leonardis, A., Slabaugh, G. & McDonagh, S. (2020). A Multi-Hypothesis Approach to Color Constancy. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr42600.2020.00234

Ibáñez-Asensio, S., Marqués-Mateu, A., Moreno-Ramón, H. & Balasch, S. (2013). Statistical relationships between soil colour and soil attributes in semiarid areas. Biosystems Engineering, 116(2), 120–129. https://doi.org/10.1016/j.biosystemseng.2013.07.013

León, K., Mery, D., Pedreschi, F. & León, J. (2006). Color measurement in L∗a∗b∗ units from RGB digital images. Food Research International, 39(10), 1084–1091. https://doi.org/10.1016/j.foodres.2006.03.006

Marquardt, D. W. (1963). An Algorithm for Least-Squares Estimation of Nonlinear Parameters. Journal of the Society for Industrial and Applied Mathematics, 11(2), 431–441. https://doi.org/10.1137/0111030

Milotta, F. L. M., Stanco, F. & Tanasi, D. (2017). ARCA (Automatic Recognition of Color for Archaeology): A Desktop Application for Munsell Estimation. Lecture Notes in Computer Science, 661–671. https://doi.org/10.1007/978-3-319-68548-9_60

Milotta, F. L. M., Stanco, F., Tanasi, D. & Gueli, A. M. (2018a). Munsell Color Specification using ARCA (Automatic Recognition of Color for Archaeology). Journal on Computing and Cultural Heritage, 11(4), 1–15. https://doi.org/10.1145/3216463

Milotta, F. L. M., Quattrocchi, C., Stanco, F., Tanasi, D., Pasquale, S. & Gueli, A. M. (2018b). ARCA 2.0: Automatic Recognition of Color for Archaeology through a Web-Application. 2018 Metrology for Archaeology and Cultural Heritage (MetroArchaeo). https://doi.org/10.1109/metroarchaeo43810.2018.9089781

Milotta, F. L. M., Furnari, G., Quattrocchi, C., Pasquale, S., Allegra, D., Gueli, A. M., … Tanasi, D. (2020). Challenges in automatic Munsell color profiling for cultural heritage. Pattern Recognition Letters, 131, 135–141. https://doi.org/10.1016/j.patrec.2019.12.008

Munsell Soil Color Charts. (2000). The Year 2000 revised washable edition. Michigan, USA: Munsell Color 4300 44th Street SE, GrandRapids, MI 49512, USA; 2000.

Pegalajar, M. C., Sánchez-Marañón, M., Baca Ruíz, L. G., Mansilla, L. & Delgado, M. (2018). Artificial Neural Networks and Fuzzy Logic for Specifying the Color of an Image Using Munsell Soil-Color Charts. Information Processing and Management of Uncertainty in Knowledge-Based Systems. Theory and Foundations, 699–709. doi:10.1007/978-3-319-91473-2_59

Sánchez-Marañón, M., Huertas, R. & Melgosa, M. (2005). Colour variation in standard soil-colour charts. Soil Research, 43(7), 827. https://doi.org/10.1071/sr04169

Stanco, F., Tanasi, D., Bruna, A. & Maugeri, V. (2011). Automatic Color Detection of Archaeological Pottery with Munsell System. Lecture Notes in Computer Science, 337–346. https://doi.org/10.1007/978-3-642-24085-0_35

Viscarra-Rossel, R. A., Minasny, B., Roudier, P. & McBratney, A. B. (2006). Colour space models for soil science. Geoderma, 133(3-4), 320–337. https://doi.org/10.1016/j.geoderma.2005.07.017

Yang, Y., Ming, J., & Yu, N. (2012). Color Image Quality Assessment Based on CIEDE2000. Advances in Multimedia, 2012, 1–6. https://doi.org/10.1155/2012/273723

Published

2022-06-01

How to Cite

The Transformation of RGB Images to Munsell Soil-Color Charts. (2022). Uniciencia, 36(1), 1-10. https://doi.org/10.15359/ru.36-1.36

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Section

Original scientific papers (evaluated by academic peers)

How to Cite

The Transformation of RGB Images to Munsell Soil-Color Charts. (2022). Uniciencia, 36(1), 1-10. https://doi.org/10.15359/ru.36-1.36

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