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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
García-Porrero, J.A., Hurlé, J.M., García-Porrero Alonso, J.: Anatomía humana. McGraw-Hill/Interamericana de España (2005)
Rivera, M.M., Díaz, A.P., Reich, J.C., et al.: Augmented reality labels for security signs based on color segmentation with PSO for assisting colorblind people. Int. J. Comb. Optim. Probl. Inform. 10, 7–20 (2019)
Kato, C.: Comprehending color images for color barrier-free via factor analysis technique. In: 2013 14th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, pp. 478–483. IEEE (2013)
Tanaka, K.D.: A colour to birds and to humans: why is it so different? J. Ornithol. 156, 433–440 (2015). https://doi.org/10.1007/s10336-015-1234-1
Bailey, J.D.: Color Vision Deficiency: A Concise Tutorial for Optometry and Ophthalmology, 1st edn. Richmond Products (2012)
Montes Rivera, M., Padilla Díaz, A., Ponce Gallegos, J.C., et al.: Recoloring Ishihara Plates with PSO algorithm and Proposed Equations. In: Robótica y Computación. Investigación y Desarrollo., 1st edn. Tecnológico Nacional de México, La Paz Baja California Sur, México, pp. 174–180 (2019)
Neiva, M.: ColorADD, color identification system (2018). http://www.coloradd.net/imgs/ColorADDAboutUs_2015V1.pdf
Liu, B., Wang, M., Yang, L., et al.: Efficient image and video re-coloring for colorblindness. In: 2009 IEEE International Conference on Multimedia and Expo, pp. 906–909. IEEE (2009)
Huang, J.-B., Chen, C.-S., Jen, T.-C., Wang, S.-J.: Image recolorization for the colorblind. In: 2009 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 1161–1164. IEEE (2009)
Huang, C.-R., Chiu, K.-C., Chen, C.-S.: Temporal color consistency-based video reproduction for dichromats. IEEE Trans. Multimed. 13, 950–960 (2011). https://doi.org/10.1109/TMM.2011.2135844
Lai, C.-L., Chang, S.-W., Sheen, J.: An integrated portable vision assistant agency for the visual impaired people. In: 2009 IEEE International Conference on Control and Automation, pp. 2311–2316. IEEE (2009)
Ohkubo, T., Kobayashi, K., Watanabe, K., Kurihara, Y.: Development of a time-sharing-based color-assisted vision system for persons with color-vision deficiency. In: Proceedings of SICE Annual Conference 2010, pp. 2499–2503 (2010)
Tanuwidjaja, E., Huynh, D., Koa, K., et al.: Chroma. In: Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing - UbiComp 2014 Adjunct, pp. 799–810. ACM Press, New York (2014)
Chung, M., Choo, H.: A real-time color-matching method based on smartphones for color-blind people. In: Eighth International Conference on Mob Mobile Ubiquitous Computing, Systems, Services Technologies, UBICOMM 2014, pp. 184–188 (2014)
Brettel, H., Viénot, F., Mollon, J.D.: Computerized simulation of color appearance for dichromats. J. Opt. Soc. Am. A 14, 2647 (1997). https://doi.org/10.1364/JOSAA.14.002647
Goswami, T.: Impact of deep learning in image processing and computer vision. In: Anguera, J., Satapathy, S.C., Bhateja, V., Sunitha, K.V.N. (eds.) Microelectronics, Electromagnetics and Telecommunications. LNEE, vol. 471, pp. 475–485. Springer, Singapore (2018). https://doi.org/10.1007/978-981-10-7329-8_48
Ku, J., Harakeh, A., Waslander, S.L.: In Defense of Classical Image Processing: Fast Depth Completion on the CPU (2018)
Rivera Montes, M., Padilla Díaz, A., Ponce Gallegos, J.C.: Comparative between RGB and HSV color representations for color segmentation when it is applied with artificial neural networks. In: en C. Ma. de Lourdes Sánchez Guerrero Dra. Alma Rosa García Gaona DFJÁR (eds.) Avances en las Tecnologías de la Información. ALFA-OMEGA, pp. 620–638 (2016)
Montes, M., Padilla, A., Canul, J., Ponce, J., Ochoa, A.: Comparative of effectiveness when classifying colors using RGB image representation with PSO with time decreasing inertial coefficient and GA algorithms as classifiers. In: Castillo, O., Melin, P., Kacprzyk, J. (eds.) Fuzzy Logic Augmentation of Neural and Optimization Algorithms: Theoretical Aspects and Real Applications. SCI, vol. 749, pp. 527–546. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-71008-2_38
Maučec, M.S., Brest, J.: A review of the recent use of Differential Evolution for Large-Scale Global Optimization: An analysis of selected algorithms on the CEC 2013 LSGO benchmark suite. Swarm Evol. Comput. (2018). https://doi.org/10.1016/j.swevo.2018.08.005
Nasiri, J.A., Yazdi, H.S., Moulavi, M.A., et al.: A PSO tuning approach for lip detection on color images. In: Proceedings - EMS 2008, European Modelling Symposium, 2nd UKSim European Symposium on Computer Modelling and Simulation, pp. 278–282. IEEE (2008)
Vijayanandh, R., Balakrishnan, G.: Performance measure of human skin region detection based on hybrid particle swarm optimization. Int. J. Comput. Theory Eng. 4, 857 (2012)
Amelio, A., Pizzuti, C.: A genetic algorithm for color image segmentation. In: Esparcia-Alcázar, A.I. (ed.) EvoApplications 2013. LNCS, vol. 7835, pp. 314–323. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-37192-9_32
Bejarbaneh, B.Y., Bejarbaneh, E.Y., Amin, M.F.M., et al.: Intelligent modelling of sandstone deformation behaviour using fuzzy logic and neural network systems. Bull. Eng. Geol. Environ. 77, 345–361 (2018). https://doi.org/10.1007/s10064-016-0983-2
Baykan, N.A., Yılmaz, N., et al.: Case study in effects of color spaces for mineral identification. Sci. Res. Essays 5, 1243–1253 (2010)
Cengiz, C., Köse, E.: Modelling of color perception of different eye colors using artificial neural networks. Neural Comput. Appl. 23, 2323–2332 (2013). https://doi.org/10.1007/s00521-012-1185-x
Al-Mohair, H.K., Mohamad-Saleh, J., Suandi, S.A.: Color space selection for human skin detection using color-texture features and neural networks. In: 2014 International Conference on Computer and Information Sciences (ICCOINS), pp. 1–6. IEEE (2014)
Rivera, M.M., Justo, M.O.A., Zezzatti, A.O.: Equations for describing behavior tables in thermodynamics using genetic programming: synthesizing the saturated water and steam table. Res. Comput. Sci. 1, 9–23 (2016)
Olmo, J.L., Romero, J.R., Ventura, S.: Swarm-based metaheuristics in automatic programming: a survey. Wiley Interdiscip. Rev. Data Min. Knowl. Discov. 4, 445–469 (2014). https://doi.org/10.1002/widm.1138
Ogawa, T., Oshiro, N., Kinjo, H.: Generating function of color information detection using genetic programming. Artif. Life Robot. 14, 480–484 (2009). https://doi.org/10.1007/s10015-009-0704-z
Poli, R., Langdon, W.B., William, B., McPhee, N.F., Koza, J.R.: A field guide to genetic programming. [Lulu Press], lulu.com (2008)
Karimi, K., Dickson, N.G., Hamze, F.: A Performance Comparison of CUDA and OpenCL (2010)
Allusse, Y., Horain, P., Agarwal, A., Saipriyadarshan, C.: GpuCV: a GPU-accelerated framework for image processing and computer vision. In: Bebis, G., et al. (eds.) ISVC 2008. LNCS, vol. 5359, pp. 430–439. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-89646-3_42
Davis, L.: Handbook of genetic algorithms. Van Nostrand Reinhold (1991)
Evers, G.I., Ghalia, M.B.: Regrouping particle swarm optimization: a new global optimization algorithm with improved performance consistency across benchmarks. In: 2009 IEEE International Conference on System, Man and Cybernetics, pp. 3901–3908 (2009). https://doi.org/10.1109/ICSMC.2009.5346625
Jamian, J.J., Abdullah, M.N., Mokhlis, H., et al.: Global particle swarm optimization for high dimension numerical functions analysis. J. Appl. Math. 2014, e329193 (2014). https://doi.org/10.1155/2014/329193
Clerc, M.: Particle Swarm Optimization. ISTE, London (2006)
Palupi Rini, D., Mariyam Shamsuddin, S., Sophiyati Yuhaniz, S.: Particle swarm optimization: technique, system and challenges. Int. J. Comput. Appl. 14, 19–27 (2011). https://doi.org/10.5120/1810-2331
Hagan, M.T., Demuth, H.B., Beale, M.H., De Jesús, O.: Neural network design (1996)
Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. The MIT Press (2016)
Weise, T.: Global optimization algorithms-theory and application. Self-published 2 (2009)
Rivera, M.M., Ramos, M.P., Mora, J.L.O.: Automatic generator of decoupling blocks using genetic programming. In: Elleithy, K., Sobh, T. (eds.) New Trends in Networking, Computing, E-learning, Systems Sciences, and Engineering. LNEE, vol. 312, pp. 281–290. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-06764-3_35
Brameier, M., Banzhaf, W.: A comparison of linear genetic programming and neural networks in medical data mining. IEEE Trans. Evol. Comput. 5, 17–26 (2001). https://doi.org/10.1109/4235.910462
Martín Montes Rivera, M.O.A.J.: Path follower algorithm for a Nao humanoid robot. In: Iliana Castro Liera, M.C.L. (eds.) Investigación y Desarrollo en Robótica y Computación. Instituto Tecnológico de la Paz, pp. 168–174 (2016)
Gonzalez, R.C., Woods, R.E., Richard, E.: Digital Image Processing. Prentice Hall (2008)
Bovik, A.C., Alan, C.: Handbook of Image and Video Processing. Elsevier Academic Press (2005)
Chai, T., Draxler, R.R.: Root mean square error (RMSE) or mean absolute error (MAE)? - arguments against avoiding RMSE in the literature. Geosci. Model. Dev. 7, 1247–1250 (2014). https://doi.org/10.5194/gmd-7-1247-2014
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-030-33749-0_45
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-33748-3
Online ISBN: 978-3-030-33749-0
eBook Packages: Computer ScienceComputer Science (R0)