【摘要】 为了克服传统的Hough变换类环状物体检测的局限,提出了1种结合物体形状与外观特征的环状物体识别检测算法.识别算法使用cascade结构,分别使用灰度、纹理以及外观综合特征,按Bagging的方法训练产生一组弱分类器.这些弱分类器串接而成,并结合局部物体分割,逐个处理当前扫描窗口.相比于传统的Hough算法,新方法具有更快的检测速度.选择敏感图像作为实验对象,采集数据进行训练和检测,实验结果表明,新方法具有明显的性能优势.使用更全面的物体外观信息,按Bagging产生弱分类器的组合,能够在提高环状物体的检测性能的同时,获得理想的处理速度.更多还原

【Abstract】 The paper proposes a new method to detect circular objects in images which performs better than Hough-like approaches.The method makes use of the shape and appearance information of objects.The detector is composed of a cascade of weak classifiers constructed by the Bagging algorithm and a local segmentation module.Three groups of local features involving gray value,texture and appearance were used in these classifiers in turn.An image’s window is reported as a circular object when it passes all the weak classifiers.Compared with the Hough algorithm,the detector has a faster detection speed and a better performance on the pornographic test data.Utilizing the information of object’s appearance and integrating it into a cascade of weak classifiers can improve the performance of circular object detection and lower the computational cost.更多还原