摘要 为了应对由复杂场景和目标形变所造成的目标难以检测的问题，提出一种基于图像显著性轮廓的目标检测方法.该方法首先利用全局概率边界算法（Globalized probability of boundary，gPb）提取图像轮廓，然后利用改进的最大类间方差法（Otsu）自适应地阈值处理获得图像的显著性轮廓；再通过检测并移除目标不稳定轮廓部分构造目标的鲁棒扇形模型；最后联合轮廓的多种局部及全局特征提出三种相似且基于特征概率密度分布的匹配策略，分别检测目标、目标镜面翻转以及发生旋转的目标.通过对多个数据库的实验分析，该方法能够有效地检测出目标及目标镜面翻转，同时在小偏转角范围有效检测旋转后的目标.
Abstract：To address detection difficulty caused by complex scene and the deformation of the object,the salient contour of image based method is proposed in this work.Firstly,the outline of image is extracted by Globalized probability of boundary,and then the improvement of the Otsu for adaptive threshold processing is used to attain the salient contour.Next,the robust fan shape model of the object is constructed by detecting and removing the unstable contour of the object.Finally,three similar matching strategies are proposed by jointing several local and global features of contour.They are based on the features probability density distribution and can detect object,its mirror flip and rotated object,respectively.Through the experimental analysis of multiple databases,the proposed method can effectively detect object and its mirror flip,and at the same time,it can also effectively detect the rotated object in the small range of rotation angle.
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2017, Vol. 45 
