Abstract
Image analysis mainly focused on identifying the inherent features inside the image for effective understanding of the images. Image segmentation is an integral part of image analysis where, we try to cluster the data and identify meaningful patterns. In this article, we focus upon presenting a model for effective segmentation using non-normal mixture models. The methodology is tested on various image datasets like medical images, natural images, and birds and animals and the result showcases that the model is exhibiting accuracy about 85%, in case of all the images. The performance evaluation carried out using metrics such as image fidelity (IF), peak signal-to-noise ratio (PSNR), and mean squared error (MSE) supports the argument.
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Vanitha, K., Chandrasekhar Reddy, P. (2017). Toward Segmentation of Images Based on Non-Normal Mixture Models Based on Bivariate Skew Distribution. In: Patnaik, S., Popentiu-Vladicescu, F. (eds) Recent Developments in Intelligent Computing, Communication and Devices. Advances in Intelligent Systems and Computing, vol 555. Springer, Singapore. https://doi.org/10.1007/978-981-10-3779-5_18
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DOI: https://doi.org/10.1007/978-981-10-3779-5_18
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