14 March 2013 Accelerated Gaussian mixture model and its application on image segmentation
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Proceedings Volume 8768, International Conference on Graphic and Image Processing (ICGIP 2012); 876831 (2013) https://doi.org/10.1117/12.2010927
Event: 2012 International Conference on Graphic and Image Processing, 2012, Singapore, Singapore
Abstract
Gaussian mixture model (GMM) has been widely used for image segmentation in recent years due to its superior adaptability and simplicity of implementation. However, traditional GMM has the disadvantage of high computational complexity. In this paper an accelerated GMM is designed, for which the following approaches are adopted: establish the lookup table for Gaussian probability matrix to avoid the repetitive probability calculations on all pixels, employ the blocking detection method on each block of pixels to further decrease the complexity, change the structure of lookup table from 3D to 1D with more simple data type to reduce the space requirement. The accelerated GMM is applied on image segmentation with the help of OTSU method to decide the threshold value automatically. Our algorithm has been tested through image segmenting of flames and faces from a set of real pictures, and the experimental results prove its efficiency in segmentation precision and computational cost.
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Jianhui Zhao, Yuanyuan Zhang, Yihua Ding, Chengjiang Long, Zhiyong Yuan, Dengyi Zhang, "Accelerated Gaussian mixture model and its application on image segmentation", Proc. SPIE 8768, International Conference on Graphic and Image Processing (ICGIP 2012), 876831 (14 March 2013); doi: 10.1117/12.2010927; https://doi.org/10.1117/12.2010927
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