参数自适应的可变类FLICM灰度图像分割算法

发布时间：2018-06-24 21:14

  本文选题：图像分割 + 模糊聚类　； 参考：《控制与决策》2017年02期

【摘要】：为解决传统FLICM算法需人为给定图像聚类数的问题,基于该算法通过聚类中心描述聚类的特点,设计以聚类中心为操作对象的分裂合并操作,以实现可变类图像分割.在此基础上定义分裂合并操作的接受率,不但能够有效避免算法陷入局部极值,促进其快速收敛,同时有利于参数阈值的自适应.分别利用所提出算法和传统ISODATA算法分割模拟图像和灰度纹理图像,对其结果的定性定量分析验证了所提出算法的有效性和普适性.
[Abstract]:In order to solve the problem that the traditional FLICM algorithm needs a given number of images, based on the characteristics of the clustering described by the clustering center, the splitting and merging operation based on the clustering center is designed to realize the variable image segmentation. On this basis, the acceptance rate of splitting and merging operations is defined, which can not only effectively avoid the algorithm falling into local extremum, promote its fast convergence, but also facilitate the self-adaptation of parameter threshold. The proposed algorithm and the traditional ISODATA algorithm are used to segment the simulated image and the gray texture image respectively. The results are qualitatively and quantitatively analyzed to verify the effectiveness and universality of the proposed algorithm.
【作者单位】： 辽宁工程技术大学测绘与地理科学学院;
【基金】：国家自然科学基金项目(41271435,41301479) 辽宁省自然科学基金项目(2015020190)
【分类号】：TP391.41
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本文编号：2062984