微型机与应用
微型機與應用
미형궤여응용
MICROCOMPUTER & ITS APPLICATIONS
2014年
7期
71-73
,共3页
阈值分割%指数交叉熵%混沌粒子群
閾值分割%指數交扠熵%混沌粒子群
역치분할%지수교차적%혼돈입자군
image registration%exponential cross entropy%chaotic particle swarm
为了提高指数交叉熵的阈值选取效率，提出了一种混沌粒子群优化指数交叉熵的阈值分割方法。首先导出指数交叉熵阈值选取方法，然后利用混沌粒子群算法对其进行优化。实验结果表明，相对于最大熵法和指数熵法，混沌粒子群优化指数交叉熵的阈值分割方法不仅分割结果精确，而且运行时间也相应缩短。
為瞭提高指數交扠熵的閾值選取效率，提齣瞭一種混沌粒子群優化指數交扠熵的閾值分割方法。首先導齣指數交扠熵閾值選取方法，然後利用混沌粒子群算法對其進行優化。實驗結果錶明，相對于最大熵法和指數熵法，混沌粒子群優化指數交扠熵的閾值分割方法不僅分割結果精確，而且運行時間也相應縮短。
위료제고지수교차적적역치선취효솔，제출료일충혼돈입자군우화지수교차적적역치분할방법。수선도출지수교차적역치선취방법，연후이용혼돈입자군산법대기진행우화。실험결과표명，상대우최대적법화지수적법，혼돈입자군우화지수교차적적역치분할방법불부분할결과정학，이차운행시간야상응축단。
In order to improve exponential cross entropy threshold selection efficiency, exponential cross entropy thresholding based on chaotic particle swarm optimization is proposed. Firstly, exponential cross entropy threshold selection is derived, then chaotic particle swarm optimization is used to search for the best thresholds. A large number of experimental results show that exponential cross entropy thresholding based on chaotic particle swarm optimization can achieve superior segmented results and greatly reduce the running time, in contrast with the maximum entropy method and the exponential entropy method.
