Automatic Image Segmentation using Wavelets
2014-08-14
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Model-Based image segmentation plays a dominant role in image analysis and image retrieval. To analyze the features
of the image, model based segmentation algorithm will be more efficient compared to non-parametric methods. In
this paper, we proposed Automatic Image Segmentation using Wavelets (AISWT) to make segmentation fast and
simpler. The approximation band of image Discrete Wavelet Transform is considered for segmentation which
contains significant information of the input image. The Histogram based algorithm is used to obtain the number of
regions and the initial parameters like mean, variance and mixing factor. The final parameters are obtained by using
the Expectation and Maximization algorithm. The segmentation of the approximation coefficients is determined by Maximum Likelihood function. It is observed that the proposed method is computationally efficient allowing the segmentation of
of the image, model based segmentation algorithm will be more efficient compared to non-parametric methods. In
this paper, we proposed Automatic Image Segmentation using Wavelets (AISWT) to make segmentation fast and
simpler. The approximation band of image Discrete Wavelet Transform is considered for segmentation which
contains significant information of the input image. The Histogram based algorithm is used to obtain the number of
regions and the initial parameters like mean, variance and mixing factor. The final parameters are obtained by using
the Expectation and Maximization algorithm. The segmentation of the approximation coefficients is determined by Maximum Likelihood function. It is observed that the proposed method is computationally efficient allowing the segmentation of
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