计算机科学 ›› 2016, Vol. 43 ›› Issue (3): 309-312.doi: 10.11896/j.issn.1002-137X.2016.03.058
刘桂红,赵亮,孙劲光,王星
LIU Gui-hong, ZHAO Liang, SUN Jin-guang and WANG Xing
摘要： 阈值法分割图像时只利用图像的灰度信息,具有直观、实现简单的特点。针对传统的粒子群优化算法(Particle Swarm Optimization,PSO)分割图像易陷入局部最优的缺点,提出一种基于改进粒子群优化算法的Otsu图像阈值分割方法。以Otsu算法的类间方差作为适应度函数,在每次迭代中选取适应度较好的粒子同时加入新的粒子,以提高粒子多样性。实验表明,与Otsu算法和PSO算法相比,改进的粒子群优化算法不仅加快了收敛速度和运算速度,而且提高了图像分割的准确率。
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