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一种新的基于局部轮廓特征的目标检测方法

DOI: 10.3724/SP.J.1004.2014.02346, PP. 2346-2355

Keywords: 轮廓提取,局部轮廓特征,阈值处理,目标检测

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Abstract:

?针对复杂场景中背景复杂、目标周围噪声多及目标只占图像中较小部分而难于检测的问题,提出一种新的基于局部轮廓特征的检测目标方法.该方法首先利用改进的全局概率边界算法(Globalizedprobabilityofboundary,gPb)算法提取图像的轮廓,然后应用最大类间方差法(Otsu)进行自动阈值处理得到图像的显著性轮廓;再提取显著性轮廓的k邻近大致直线轮廓段(kconnectedroughlystraightcontoursegments,kAS),并以kAS作为局部特征,用于复杂场景中的目标检测.该算法结合gPb算法和Otsu提取轮廓的显著性轮廓,去除了目标附近的大量噪声边界,有效地提高了检测效率.同时,在检测阶段,测试集与训练集中提取的不相关特征数目也得到较大减少,从而提高了检测的精度.多组实验结果均表明本文方法的有效性.

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