中文摘要：

针对由于待检测目标局部区域显著性差异过大造成的细微区域检测失败问题,在贝叶斯理论框架下,提出一种基于元胞自动机多尺度优化的显著性检测方法。首先结合暗通道先验信息和区域对比度在同一张图片的5个超像素尺度空间内分别构建原始显著性图;接着,利用元胞自动机建立动态更新机制,通过影响因子矩阵和置信度矩阵优化每个元胞下一状态的影响力,获得对应5个优化显著性图;最后在基于贝叶斯理论的融合算法框架下得到最终的显著性图。在两个复杂度不同的标准图像数据库上将本文方法与10种主流显著性提取方法进行视觉效果和客观定量数据对比,结果显示,本文算法效果优于现有10种显著性提取方法,其中在公认最具挑战的DUT-OMRON数据库的综合指标F-measure值为0.631 4,平均绝对误差（MAE）为0.132 5,ROC曲线下面积（AUC）为0.892 8,表明本文算法具有较高的准确性和鲁棒性。

英文摘要：

Aiming at failure detection problems on subtle region caused by saliency differences of detected target in local region, under the framework of Bayesian theory, the author proposed a novel salient region detection method based on cellular automata multi-scale optimization. Firstly, the prior information about dark channel was integrated with regional contrast to separately construct original salient maps in five superpixel scale spaces on the same picture; and then the cellular automata was used to establish a dynamic updating mechanism and impact factor matrix and confidence matrix were applied to optimize influences of each cellular in next state. As a result, the saliency values of all cells will be renovated simultaneously according to the proposed updating rule, and five optimized salient maps were obtained l finally, under the framework of fusion algorithm in Bayesian theory, the final saliency map was obtained. The experiment on two standard image datasets with different complexity was conducted, and experimental result indicates that the performance of proposed algorithm is superior to other ten existing salient region detection algorithms both in visual effect and in objective quantitative comparison. Especially on the most challenging DUT-OMRON data base, the aggregative indicator F-measure value of proposed algorithm is 0. 631 4, and mean absolute error （MAE） is 0. 132 5 and ROC area under the curve （AUC） is 0. 892 8, indicating that the algorithm has higher accuracy and robustness.