An image thresholding approach based on Gaussian mixture model

摘要: Image thresholding is an important technique for partitioning the image into foreground and background in processing analysis. It difficult traditional methods to get satisfactory performance on noisy uneven grayscale images. In this paper, we propose approach based Gaussian mixture model (GMM) solve problem. GMM assumes that a of two unknown parameters’ distributions, which corresponds background, respectively. Based assumption, adopt expectation maximization algorithm with simple initialization strategy estimate statistical parameters utilize Bayesian criteria generate binary map. Furthermore, calculate posterior probabilities consideration neighborhood effect achieve good Experimental results conducted synthetic real images demonstrate effectiveness proposed method.