“Perceptual-Based CNN Model for Watercolor Mixing Prediction” by Huang, Chen and Ouhyoung

  • ©Ya-Bo Huang, Mei-Yun Chen, and Ming Ouhyoung

  • ©Ya-Bo Huang, Mei-Yun Chen, and Ming Ouhyoung

  • ©Ya-Bo Huang, Mei-Yun Chen, and Ming Ouhyoung

Conference:


Entry Number: 14

Title:

    Perceptual-Based CNN Model for Watercolor Mixing Prediction

Presenter(s):



Abstract:


    In the poster, we propose a model to predict the mixture of watercolor pigments using convolutional neural networks (CNN). With a watercolor dataset, we train our model to minimize the loss function of sRGB differences. In metric of color difference ELab , our model achieves 88.7 % of data that ELab on the test set, which means the difference can not easily be detected by human eye. In addition, an interesting phenomenon is found; Even if the reflectance curve of the predicted color is not as smooth as the ground truth curve, the RGB color is still close to the ground truth.

References:


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    • Mei-Yun Chen, Ci-Syuan Yang, and Ming Ouhyoung. 2018. A Smart Palette for Helping Novice Painters to Mix Physical Watercolor Pigments. In Proc. of EuroGraphics 2018, Posters,, Eakta Jain and Jirí Kosinka (Eds.). The Eurographics Association, April 2018. https://doi.org/10.2312/egp.20181008
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Keyword(s):



Acknowledgements:


    This project was partially supported by Meidatek Inc. under Grant No.:˜MTKC-2018-0167 , Ministry of Science and Technology(MOST), Taiwan under Grant No.:˜106-3114-E-002-012 and˜105-2221-E-002- 128-MY2.


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