Abstract
To increase performance of Radial Basis Functions (RBFs) in Artificial Neural Network q-Gaussian Radial Basis Function (q-GRBF) is introduced to optimize singular value of Y, Cb and Cr color image components. Various radial basis functions such as Gaussian Radial Basis Function (GRBF), Multi Quadratic Radial Basis Function (MCRBF), Inverse Multi Quadratic Radial Basis Function (IMCRBF) and Cosine Radial Basis Function (CRBF) are also introduced and compared with singular values of Y, Cb and Cr component of color images. Simulation and analysis shows that q-Gaussian Radial Basis Function gives lesser error and better result compared to the other radial basis functions in artificial neural network.
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© 2014 Springer International Publishing Switzerland
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Paul, A., Bhattacharya, P., Maity, S.P. (2014). An Approach of Optimizing Singular Value of YCbCr Color Space with q-Gaussian Function in Image Processing. In: Kumar Kundu, M., Mohapatra, D., Konar, A., Chakraborty, A. (eds) Advanced Computing, Networking and Informatics- Volume 1. Smart Innovation, Systems and Technologies, vol 27. Springer, Cham. https://doi.org/10.1007/978-3-319-07353-8_29
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DOI: https://doi.org/10.1007/978-3-319-07353-8_29
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-07352-1
Online ISBN: 978-3-319-07353-8
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