混合模型
图像分割
计算机科学
期望最大化算法
人工智能
分割
概率逻辑
模式识别(心理学)
图像(数学)
人工神经网络
最大化
尺度空间分割
算法
数学优化
数学
最大似然
统计
作者
Konstantinos Blekas,Aristidis Likas,Nikolas P. Galatsanos,Isaac E. Lagaris
出处
期刊:IEEE Transactions on Neural Networks
[Institute of Electrical and Electronics Engineers]
日期:2005-03-01
卷期号:16 (2): 494-498
被引量:195
标识
DOI:10.1109/tnn.2004.841773
摘要
Gaussian mixture models (GMMs) constitute a well-known type of probabilistic neural networks. One of their many successful applications is in image segmentation, where spatially constrained mixture models have been trained using the expectation-maximization (EM) framework. In this letter, we elaborate on this method and propose a new methodology for the M-step of the EM algorithm that is based on a novel constrained optimization formulation. Numerical experiments using simulated images illustrate the superior performance of our method in terms of the attained maximum value of the objective function and segmentation accuracy compared to previous implementations of this approach.
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