Abstract
This paper introduces a semantic-segmentation guided image recoloring approach of digitized art paintings to enhance the color perception of color- blind people that suffer from protanopia and deuteranopia. Semantic segmentation using transfer learning between natural images and art paintings is applied to extract annotated color information. By using a standard technique, the annotated colors are transformed to simulate the effects of protanopia and deuteranopia. Then, a specialized objective function is minimized to recolor only the colors that are significantly different from the respective simulated ones, because these colors are perceived as confusing by the color blind. The effectiveness of the proposed method is demonstrated through its comparison with other algorithms in several experimental cases.
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This research is co-financed by Greece and the European Union (European Social Fund-ESF) through the Operational Programme “Human Resources Development, Education and Lifelong Learning 2014–2020” in the context of the project “Color perception enhancement in digitized art paintings for people with Color Vision Deficiency” (MIS 5047115).
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Chatzistamatis, S., Rigos, A., Tsekouras, G.E. (2020). Image Recoloring of Art Paintings for the Color Blind Guided by Semantic Segmentation. In: Iliadis, L., Angelov, P., Jayne, C., Pimenidis, E. (eds) Proceedings of the 21st EANN (Engineering Applications of Neural Networks) 2020 Conference. EANN 2020. Proceedings of the International Neural Networks Society, vol 2. Springer, Cham. https://doi.org/10.1007/978-3-030-48791-1_20
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