使用多尺度Harris算子检测图像的角点作为初始兴趣点。针对自适应非极大值抑制排除了大量潜在匹配点的缺陷,引入条件理论对初始兴趣点进行控制,排除病态点,减少后续过程的计算量,提高算法效率,同时最大限度的保留了匹配点。满足条件理论控制下的特征称为良态特征。由于初始Harris角点的位置会发生偏移以及有伪角点产生,所以利用亚像素定位技术进行精确定位,并排除伪角点和不稳定的角点。使用PCA-SIFT对特征点及其邻域进行描述获得特征向量。最后通过向量之间的欧几里德距离判断特征点是否匹配。实验结果表明算法效率改进明显,匹配效果良好,对图像的几何变换、噪声及光照变化等具有较强的鲁棒性。

Corners in an image were detected by multi-scale Harris operator,and they were taken as initial interest points. Since adaptive non-maximal suppression eliminated lots of potential matching points,condition theory was applied to control the number of initial interest points. The bad conditioned points were eliminated,so the computational complexity of the following process was decreased and the efficiency of the algorithm was improved. Meanwhile,matching points were mostly kept. Features under the control of condition theory were called well conditioned features. Since the location of initial Harris corner had offset and false corners were produced,sub-pixel localization technique was used to determine the location of corners,and the false and unstable corners were eliminated in this process. PCA-SIFT was applied to describe the feature points and their neighbors to get vectors. Finally,Euclidean distance between vectors was used to determine whether two points match or not. Experimental results show that the efficiency is improved significantly,the proposed algorithm is good at feature matching,and has good robustness to geometrical transformations,image noise and illumination change.