Design of Mobile Application for Assisting Color Blind People to Identify Information on Sign Boards

Bhagya R. Navada, Santhosh Krishna Venkata

Abstract


Color blindness is a condition where a person cannot distinguish colors that are of similar contrast. This paper reports an attempt to develop a mobile phone application that can run on any Android or Windows smart phone. The developed application/software tool is able to assist color blind people by converting an image with low contrast to an image with high contrast. The objective of the proposed work was to develop a program on the LabVIEW platform to i) acquire the image whose information should be processed, ii) develop an algorithm to display a high-contrast crisp image of the actual dull image, and iii) identify the colors and characters present in the dull image for messaging to the user’s phone. The work was implemented on the LabVIEW platform making use of various image processing tools to identify the color and text from the sign board that otherwise cannot be identified by color blind persons. The implementation was tested with several inputs to validate the performance of the proposed method. It was able to produce accurate results for more than 97.3% of the test inputs.

Keywords


color blindness; image processing; LabVIEW; mobile application; rehabilitation.

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References


Sharpe, L.T., Stockman, A., Jägle, H. & Nathans, J., Opsin Genes, Cone Photopigments, Color Vision, and Color Blindness, in Color Vision: From Genes to Perception, Gegenfurtner, K. & Sharpe, L.T. (Eds.), Cambridge University Press, Cambridge, pp. 3-51, 1999.

Judd, D.B., Facts of Color-blindness, Journal of the Optical Society of America, 33(6), pp. 294-307, 1943.

Pearlman, A.L., Birch, J. & Meadows, J.C., Cerebral Color Blindness: An Acquired Defect in Hue Discrimination, Annals of Neurology 5(3), pp. 253-261, 1979.

McDaniel, T.L., Kahol, K. & Panchanathan, S., An Interactive Wearable Assistive Device for Individuals who are Blind for Color Perception, In Proceedings of the 4th International Conference on Universal Access in Human-Computer Interaction, pp. 751-760, Springer Berlin Heidelberg, 2007.

Ananto, B.S., Sari, R.F. & Harwahyu, R., Color Transformation for Color Blind Compensation on Augmented Reality System, In Proceeding of the 2011 International Conference on User Science and Engineering (i-USEr), pp. 129-134. IEEE, 2011.

Harwahyu, R., Manaf, A.S., Ananto, B.S., Wicaksana, B.A. & Sari, R.F, Implementation of Color-blind AID System, Journal of Computer Science, 9(6), pp. 794-810, 2013.

Patel, I. & Goud, J., Colour Recognition for Blind and Colour Blind People, Int J. Eng Innovat Technol., 2(6), pp. 38-42, Dec. 2012.

McDowell, J., Design of a Color Sensing System to Aid the Color Blind, IEEE Potentials, 27(4), pp. 34-39, 2008.

Harish, M., Gudipalli, A. & Tirumala, R., Enhanced Road Security using Edge Detection and Infrared Imagery, Journal of Theoretical & Applied Information Technology, 58(1), pp. 55-59, 2013.

Tian, Y. & Yuan, S., Clothes Matching for Blind and Color Blind People, Proceedings of the 12th International Conference on Computers Helping People with Special Needs, Vienna, Austria, pp. 324-331. Springer Berlin Heidelberg, 2010.

Banerji, S., Sinha, A. & Liu, C., New Image Descriptors based on Color, Texture, Shape, and Wavelets for Object and Scene Image Classification, Neurocomputing, 117(14), pp. 173-185, 2013.

Belmamoun, A., El Hassouni, M. & Hammouch, A., On Selection and Combination of Relevant Color Components for Edge Detection, Procedia Technology, 17, pp.764-771, 2014.

Zareizadeh, Z., Hasanzadeh, R.P. & Baghersalimi, G., A Recursive Color Image Edge Detection Method using Green’s Function Approach, Optik-International Journal for Light and Electron Optics, 124(21), pp. 4847-4854, 2013.

Ou, Y. & GuangZhi, D., Color Edge Detection based on Data Fusion Technology in Presence of Gaussian Noise, Procedia Engineering, 15, pp. 2439-2443, 2011.

Mansor, M.N., Hariharan, M., Basah, S.N. & Yaacob, S., New Newborn Jaundice Monitoring Scheme based on Combination of Pre-processing and Color Detection Method, Neurocomputing, 120, pp. 258-261, 2013.

Goh, H.N., Soon, L.K. & Haw, S.C., Automatic Dominant Character Identification in Fables based on Verb analysis – Empirical Study on the Impact of Anaphora Resolution, Knowledge-Based Systems, 54, pp. 147-162, 2013.

Lin, W.C. & Scully, T.L., Computer Identification of Constrained Handprinted Characters with a High Recognition Rate, IEEE Transactions on Systems, Man, and Cybernetics, 6, pp. 497-504, 1974.

Namane, A., Guessoum, A., Soubari, E.H. & Meyrueis, P., CSM Neural Network for Degraded Printed Character Optical Recognition, Journal of Visual Communication and Image Representation, 25(5), pp. 1171-1186, 2014.

Niranjan, M., Ajtih, J. & Padmavathi, S., Dynamic Indian Sign Language Character Recognition: Using HOG Descriptor and a Customized Neural Network, International Journal of Imaging and Robotics™, 14(3), pp. 35-46, 2014.

Saoud, S., Mahani, Z., El Rhabi, M. & Hakim, A., A Document Scanning in a Tough Environment: Application to Cameraphones, International Journal of Imaging and Robotics, 9(1), pp. 1-16, 2013.

Surinta, O., Karaaba, M.F., Schomaker, L.R. & Wiering, M.A., Recognition of Handwritten Characters using Local Gradient Feature descriptors, Engineering Applications of Artificial Intelligence, 45, pp. 405-414, 2015.

Yi, C. & Tian, Y., Text Extraction from Scene Images by Character Appearance and Structure Modeling, Computer Vision and Image Understanding, 117(2), pp. 182-194, 2013.

Otsu, N., A Threshold Selection Method from Gray-Level Histograms. IEEE Transactions on Systems, Man, and Cybernetics, 9(1), pp. 62-66, 1979.

Gonzalez, R.C. & Woods, R.E., Digital Image Processing, 2nd edition, Prentice Hall, 2002.

Davis, L.S., A Survey of Edge Detection Techniques, Computer Graphics and Image Processing, 4(3), pp. 248-260, 1975.




DOI: http://dx.doi.org/10.5614%2Fj.eng.technol.sci.2017.49.5.8

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