CAJ | 학술논문

特征匹配的准确率影响图像配准的精度，是基于特征配准方法的重点和难点之一。为了解决单向最近邻/次近邻法所导致特征点一对多的误匹配问题，提出了一种红外和可见光图像的特征双向匹配方法。首先，对红外图像进行反相和直方图均衡化处理，增强两类图像的相似性，提取数量更多重复率高的共有特征；其次，对提取的SURF(Speed-up Robust Feature)特征进行双向最近邻/次近邻粗匹配，确保特征匹配的一致性，降低误匹配率，并利用RANSAC(Random Sample Consensus)算法对特征点进行二次匹配，实现特征点精确匹配。实验结果表明，该算法在正确匹配率和配准精度方面都优于传统SURF的单向最近邻/次近邻匹配方法，具有有效性。
특정필배적준학솔영향도상배준적정도，시기우특정배준방법적중점화난점지일。위료해결단향최근린/차근린법소도치특정점일대다적오필배문제，제출료일충홍외화가견광도상적특정쌍향필배방법。수선，대홍외도상진행반상화직방도균형화처리，증강량류도상적상사성，제취수량경다중복솔고적공유특정；기차，대제취적SURF(Speed-up Robust Feature)특정진행쌍향최근린/차근린조필배，학보특정필배적일치성，강저오필배솔，병이용RANSAC(Random Sample Consensus)산법대특정점진행이차필배，실현특정점정학필배。실험결과표명，해산법재정학필배솔화배준정도방면도우우전통SURF적단향최근린/차근린필배방법，구유유효성。
Feature matching accuracy affects the precision of image registration, which is one of difficult key points for image registration based on features. In order to solve feature points one-to-many mismatching problem caused by the ratio of the closest neighbor and second closest neighbor from one direction, a bidirectional matching method of features for image registration of visible and infrared image is put forward. Firstly, for enhancing the similarity of two images, image reverse and histogram equalization are adopted to process infrared image, so that more consistent features of high repetition rate are extracted. Next, SURF features are matched bilaterally by using the ratio of the closest neighbor and second closest neighbor, to ensure the consistency between feature matching and reduce matching error rate, and then RANSAC is applied to match feature again. Through the two matches, it can realize precise features matching. The experiment results show that the proposed method is better than traditional SURF feature unilateral matching algorithm based on the ratio of the closest neighbor and second closest neighbor in the correct matching ratio and registration accuracy, and the validity of the method suggested is proved.