摘要： 尺度不变特征变换(SIFT)和加速鲁棒特征(SURF)方法在进行角点检测和特征点匹配时的时间较长。为此,提出一种改进的图像配准算法。建立参考图像与待配准图像的高斯图像金字塔,在金字塔各层图像进行检测,得到具有不同尺度的加速分割测试特征(FAST)点,采用SURF算法为各特征点分配方向,并计算各特征点的描述向量,使用快速近似最近邻搜索算法获取图像间的初始匹配点对,用随机抽样一致性算法剔除误匹配点对,同时得到2幅图像之间的几何变换矩阵。实验结果表明,与SURF算法和SIFT算法相比,该算法的特征检测速度和匹配速度较快,匹配正确率较高。
关键词:
图像配准,
加速分割测试特征,
加速鲁棒特征,
近似最近邻,
随机抽样一致性
Abstract: For Scale Invariant Feature Transform(SIFT) and Speeded-up Robust Feature(SURF) needing a long time in the corner detecting and feature points matching,an improved image registration algorithm is put forward.A Gaussian scale pyramid of the reference image and the matching image are established.Feature points which have different scale information are detected from each level in the image pyramid.It gets Features from Accelerated Segment Test(FAST) point with different scales.An orientation is assigned to every feature point,and feature vector is calculated by using the same way as SURF.The original matching points which have minimum Euclidean distance under some condition are determined through fast approximate nearest neighbor search.The false matching points are excluded by Randomized Sample Consensus(RANSAC) algorithm,and the transformation matrix is gained.Experimental results show that the algorithm is better than SURF and SIFT in feature detection speed and matching speed,and matching accuracy is higher.
Key words:
image registration,
Features from Accelerated Segment Test(FAST),
Speeded-up Robust Feature (SURF),
Approximate Nearest Neighbor(ANN),
Randomized Sample Consensus(RANSAC)
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