基于局部不变特征的目标自动识别

Automatic Target Recognition Based on Local Invariant Features

为快速、准确地识别图像中的目标,提出一种结合图像熵和加速鲁棒特征算法的目标自动识别方法.首先,分块计算图像的信息熵,根据阈值筛选出纹理丰富区域.然后,结合Hessian矩阵和Harris算法提取纹理丰富区域的局部特征点.接着,计算特征向量并用主成分分析降低向量维数.最后,采用双向最近距离比例匹配算法进行分类,并用随机抽样一致算法剔除误匹配点.实验结果表明:对仿真数据库中带有视角、光照和尺度变化的图像,识别率分别为87.12%、75.31%和84.98%,平均识别时间分别为70.35ms、71.27ms、220.63ms;对含8956×6708像素的航空大面阵图像,正确匹配率为78.13%,识别时间为68.09s.本方法识别率和时间性能均优于加速鲁棒特征算法.

In order to recognize targets in images fast and truly,an automatic target recognition method was proposed based on image entropy and speed up robust feature.First,image entropy was computed in different blocks,and regions full of texture were filtered out by threshold.The local key points in regions of interest were extracted by incorporating the Hessian and Harris detectors.Then,feature descriptors were established and principle component analysis was employed to reduce the dimensionality.Finally,nearest neighbor distance ratio classifier was explored in double directions and wrong matches were eliminated by random sample consensus.The experiment results demonstrate that the recognition rates for images in simulation database with varied view-points,scales and illuminations are 87.12%,75.31%and 84.98%,and the computing time is 70.35 ms,71.27 ms and 220.63 ms,respectively.Moreover,the correct matching rate for an aerial large planar array image of 8 956×6 708 pixels is 78.13% and the computing time is 68.09 s.Compared with speed up robust feature,the proposed method performs better both in recognition rates and computing time.