Browsing by Author "Kilic I."

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    A new nonlinear quantizer for image processing within nonextensive statistics
    (2007) Kilic I.; Kayacan O.
    In this study, we introduce a new nonlinear quantizer for image processing by using Tsallis entropy. Lloyd-Max quantizer is commonly used in minimizing the quantization errors. We report that the new introduced technique works better than Lloyd-Max one for selected standard images and could be an alternative way to minimize the quantization errors for image processing. We, therefore, hopefully expect that the new quantizer could be a useful tool for all the remaining process after image quantization, such as coding (lossy and lossless compression). © 2007 Elsevier B.V. All rights reserved.
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    Using Linde Buzo Gray Clustering Neural Networks for Solving the Motion Equations of a Mobile Robot
    (Springer Verlag, 2011) Aydin S.; Kilic I.; Temeltas H.
    In this paper, motion equations for the synchro-drive robot Nomad 200 are solved by using Linde Buzo Gray (LBG) clustering neural networks. The trajectories of the Nomad 200 are assumed to be composed of straight line segments and curves. The structure of the curves is determined by only two parameters, turn angle and translational velocity in the curve. The curves of the trajectories are found by using artificial neural networks (ANN) and the LBG clustered ANN. In this study a clustering method is used to improve the learning and test the performance of the ANN. In general, the LBG algorithm is used in image processing as a quantizer. This is the first publication where the LBG algorithm is successfully used in clustering ANN data sets. Thus, the best training data set of the ANN is achieved and minimum error values are obtained. It is shown that LBG-ANN models are better than the classic ANN models. © 2011 King Fahd University of Petroleum and Minerals.
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    Generalized ICM for image segmentation based on Tsallis statistics
    (Elsevier B.V., 2012) Kilic I.; Kayacan O.
    In this paper, the iterated conditional modes optimization method of a Markov random field technique for image segmentation is generalized based on Tsallis statistics. It is observed that, for some q entropic index values the new algorithm performs better segmentation than the classical one. The proposed algorithm also does not have a local minimum problem and reaches a global minimum energy point although the number of iterations remains the same as ICM. Based on the findings of the new algorithm, it can be expressed that the new technique can be used for the image segmentation processes in which the objects are Gaussian or nearly Gaussian distributed. © 2012 Elsevier B.V. All rights reserved.