基于PCA-SIFT特征的目标识别算法

针对尺度不变特征变换(SIFT)算法在匹配时特征向量过多,从而导致耗时过长的问题,提出PCA-SIF]算法,对目标进行匹配与识别.首先,利用SIFT算法提取出原图像中稳健的特征点以及特征向量;其次,利用PCA算法对SIFT特征向量的维数进行约减;最后利用降维后的图像与原始图像进行匹配.实验证明,与原始SIFT算法相比,该算法不仅保持了SIFT算法的鲁棒性和稳定性,同时提高了匹配效率,增强了实时性.

Target recognition algorithm based on PCA-SIFT

In this paper, a target identification and matching algorithm based on PCA-SIFT is proposed to deal with the problem that a long time is taken caused by excessive number of characteristics in the matching with SIFT algorithm. Firstly, we extract the robust and salient feature points and vectors of the image frames according to the local invariant features.Then PCA analytic method is introduced into image matching to reduce the dimensions of SIFT feature vector. Finally, we use the reduced feature points to image matching and analytic methods.Some experimental results show that the algorithm proposed in this paper not only has maintain the robustness and stability , but also improvd the matching efficiency and enhancing the real-time performance,comparing with the original SIFT algorithm.