针对图像特征提取与匹配的适应性和准确性问题,将尺度不变特征变换算法(SIFT)应用到图像的特征点提取与匹配中。SIFT算法可在尺度空间寻找极值点,提取对图像尺度和旋转变化具有不变性,对光照变化和图像变形具有较强适应性的特征点及其特征描述。首先,采用SIFT算法提取图像的特征点及其描述,然后,采用基于置信度的匹配算法进行特征点的匹配,以找到图像间准确的匹配点对。最后,对不同光照条件、焦距、拍摄角度获取的图像进行特征点的提取及匹配。实验结果显示,本文算法对图像的光照、平移、旋转变换具有很好的适应性和准确性,能够提升匹配的自动化水平和准确度,准确率超过90％。结果表明,提出的算法可以进一步应用到图像识别,图像重建等领域。

With the aim to improve the stability and reliability of image matching,the Scale Invariant Feature Transform algorithm(SIFT) is applied to image feature extraction and image matching. The SIFT can find out those feature vectors in different scale spaces and can extract image features and image description with the invariantce for scale changes and rotations and the flexibility for illumination variation and affine transformation. In this paper,the SIFT method is used to get the special point of an image and its features. Then, the features are matched with the criterion of the nearest neighbor based on confidence. Finally,the features in the image with different illumination conditions,focus lengths and shooting angles are extrated and matched.The experimental results prove that the features extracted by SIFT method have excellent adaptive and accurate characteristics for the illumination,transfer and the rotation transform and have match accuracy more than 90%,which are useful for the fields of image recognition and image reconstruction.