Abstract
Sight is the most critical sense because of its worth in human life; humans use it to guarantee their safeness, to move around, to identify persons, objects, among other activities. The eyes use two kinds of cells in visual perception, rods for luminosity and cones for color. Colorblindness is a mild disability that affects color perception in close to 10% of the world population. Partial solutions for the colorblind include glasses that increase the distance between colors, avoiding confusing regions when suffering mild colorblindness. Other alternatives include special symbols for labeling objects and text descriptions, but there is not a definitive solution. Alternatively, computer vision has developed some assistants for the colorblind based on color classification, including applications that highlight confusing regions or identify colors selected by the user. Recently, artificial intelligence, together with parallel computing, has become a good alternative in vision assistance, but there are several alternatives with different schemes for performing color classification, those include heuristically tuned human proposed equations, computer-generated equations, and Artificial Neural Networks (ANN’s), among others. In this paper, a labeling color assistant for the colorblind is developed using color classification with heuristically (GA and PSO algorithms) tuned proposed equations, genetic programming equations, and ANN’s. As a result of this research, is determined the best structure for color classification, based on the accuracy and processing time in a CUDA kernel, so that be possible a real-time labeling system for the colorblind with full high definition images.
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Rivera, M.M., Padilla, A., Gallegos, J.C.P., Canul-Reich, J., Zezzatti, A.O., de Luna, M.A.M. (2019). Performance of Human Proposed Equations, Genetic Programming Equations, and Artificial Neural Networks in a Real-Time Color Labeling Assistant for the Colorblind. In: Martínez-Villaseñor, L., Batyrshin, I., Marín-Hernández, A. (eds) Advances in Soft Computing. MICAI 2019. Lecture Notes in Computer Science(), vol 11835. Springer, Cham. https://doi.org/10.1007/978-3-030-33749-0_45
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