2004
@article{XiT2004,
vgclass = {refpap},
author = {J. Xie and H. T. Tsui},
title = {Image segmentation based on maximum-likelihood estimation
and optimum entropy-distribution ({MLE-OED})},
journal = {Pattern Recognition Letters},
volume = {25},
number = {10},
pages = {1133--1141},
month = {July},
year = {2004},
url = {http://dx.doi.org/10.1016/j.patrec.2004.03.013},
abstract = {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 likelihood of the parameters for the given data. The proposed
segmentation method is validated by experiments on both synthetic and
real images. The experimental results show that the proposed method
outperformed two popular methods.},
}