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中文摘要：
为了提升二维交叉熵阈值分割法运行速度,提出了基于混沌弹性粒子群优化（CRPSO）和基于分解的2种二维交叉熵阈值分割算法.前者利用CRPSO算法寻找二维交叉熵法的最佳分割阈值,并采用递推方式避免迭代过程中适应度函数的重复计算,使运算速度大大提高;后者将二维交叉熵法的运算转换到2个一维空间上,计算复杂度由O（L2）进一步降为O（L）.实验结果表明,2种算法能够在保证分割效果达到或优于现有二维交叉熵阈值分割法的前提下,运行时间大幅减少.
英文摘要：
A two-dimensional cross entropy image thresholding method based on chaotic resilient particle swarm optimization（CRPSO） or decomposition was proposed. Firstly,chaotic resilient particle swarm optimization was used to find the optimal threshold of two-dimensional cross entropy method.The recursive algorithm was adopted to avoid the repetitive computation of fitness function in iterative procedure.As a result,the computing speed was improved greatly.Then,the computation of two-dimensional cross entropy method was converted into two onedimensional spaces,which made the computation complexity further reduce from O（L~2） to O（L）.The experimental results show that,the two methods proposed in this paper can greatly reduce the running time while the segmented result is as good as or better than the existing two-dimensional cross entropy thresholding method.
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