光学精密工程2017，Vol.25Issue(5)：1312-1321,10.DOI:10.3788/OPE.20172505.1312
元胞自动机多尺度优化的显著性细微区域检测
Salient subtle region accurate detection via cellular automata multi-scale optimization
摘要
Abstract
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;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.关键词
元胞自动机/视觉显著性/多尺度超像素/暗通道/贝叶斯理论Key words
cellular automation/visual saliency/multi-scale superpixel/dark channel/Bayesian theory分类
信息技术与安全科学引用本文复制引用
徐涛,贾松敏,张国梁..元胞自动机多尺度优化的显著性细微区域检测[J].光学精密工程,2017,25(5):1312-1321,10.基金项目
国家自然科学基金资助项目(No.61175087) （No.61175087）
北京工业大学2017智能制造领域大科研推进计划"助老智能轮椅床自主测控系统的研究与实现"资助项目 （）
