Multiple Objects Segmentation Based on Maximum-Likelihood Estimation and Optimum Entropy-Distribution (MLE-OED)
نویسندگان
چکیده
A new method bused on MLE-OED is proposed for unsupervised image segmentation of multiple objects which have fuzzy edges. It adjusts the parameters of a mixture of Gaussian distributions via minimizing a new loss function proposed to implement image segmentation bused on the image’s local spatial information and global intensity distribution properties . The loss function consists of two terms: a local content Jitting term, which optimizes the entropy distribution, and a global statistical Jitting term, which maximizes the likelihood of the parameters for the given data. The proposed segmentation method was vulidated by simulated and real examples. Its performance in the experiments is better than those of two popular methods.
منابع مشابه
Image segmentation based on maximum-likelihood estimation and optimum entropy-distribution (MLE-OED)
A novel method based on MLE–OED is proposed for unsupervised image segmentation of multiple objects with fuzzy edges. It adjusts the parameters of a mixture of Gaussian distributions via minimizing a new loss function. The loss function consists of two terms: a local content fitting term, which optimizes the entropy distribution, and a global statistical fitting term, which maximizes the likeli...
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