基于快速轮廓转动力矩特征的激光主动成像目标识别  

Target Recognition in laser active imaging based on fast contour torque features

作　　者：王灿进 孙涛[1] 李正炜[1,2] 

机构地区：[1]中国科学院长春光学精密机械与物理研究所激光与物质相互作用国家重点实验室,吉林长春130033 [2]中国科学院大学,北京100049

出　　处：《中国光学》2015年第5期775-784,共10页Chinese Optics

基　　金：吉林省科技发展计划资助项目(No.20126015);长春市科技发展计划资助项目(No.2013270)

摘　　要：针对激光主动成像的图像特性,提出一种基于快速轮廓转动力矩的目标识别方法。将转动力矩的概念引入目标识别中,提出的快速轮廓转动力矩特征(FCTF)不仅包含了轮廓的尺寸、位置、规则度以及目标的亮暗等信息,同时对于旋转、尺度缩放等变换具有不变性。采用转动力矩的快速计算方法,提高了识别算法的计算效率。识别算法首先使用最大稳定极值区域(MSER)算法检测出目标特征区域,并将其变换为圆形区域,然后结合快速转动力矩特征算法提取出目标区域的局部不变特征,最后输入训练好的支持向量机分类器进行识别。实验结果表明相比于已有的激光主动成像目标识别方法,所提算法对于旋转、仿射变换均具有更高的识别率,同时单帧平均运算时间为9.68 ms,满足激光主动成像目标识别系统实时性的要求。Due to the characteristic of images in laser active imaging,a novel target recognition method based on fast contour torque features（ FCTF） is proposed. The concept of torque is introduced into target recognition. The proposed fast contour torque features contain abundant information such as the size,position,shape regularly of the contours and darkness of the target,which are as well invariant to rotation and scaling. Meanwhile the fast calculation method greatly improves the computational efficiency. Firstly feature regions are detected using Maximally Stable Extremal Regions（ MSER） algorithm,and transformed into circular areas. Then local invariant features of the feature regions are extracted by fast contour torque feature descriptor. At last thefeatures are input into the trained Suppor Vector Machine（ SVM） classifier for identification. The experimental results indicate that compared with the existing laser active imaging recognition algorithms,the proposed method acquires higher recognition rate in rotation and affine transformation,and the average computing time of single frame is 9. 68 ms,which meet the real-time requirement in laser active imaging.

关 键 词：激光主动成像 目标识别 最大稳定极值区域 快速轮廓转动力矩特征 支持向量机 

分 类 号：TN957.52[电子电信—信号与信息处理] TH744.5[电子电信—信息与通信工程]