赵 峰, 黄庆明, 高 文. 一种基于奇异值分解的图像匹配算法[J]. 计算机研究与发展, 2010, 47(1): 23-32.
 引用本文: 赵 峰, 黄庆明, 高 文. 一种基于奇异值分解的图像匹配算法[J]. 计算机研究与发展, 2010, 47(1): 23-32.
Zhao Feng, Huang Qingming, Gao Wen. An Image Matching Algorithm Based on Singular Value Decomposition[J]. Journal of Computer Research and Development, 2010, 47(1): 23-32.
 Citation: Zhao Feng, Huang Qingming, Gao Wen. An Image Matching Algorithm Based on Singular Value Decomposition[J]. Journal of Computer Research and Development, 2010, 47(1): 23-32.

## An Image Matching Algorithm Based on Singular Value Decomposition

• 摘要: 图像匹配技术在计算机视觉、遥感和医学图像分析等领域有着广泛的应用背景.针对传统的相关匹配算法计算量大、对图像旋转敏感等问题，提出一种新的基于奇异值分解的图像匹配算法.首先在待匹配图像中分别提取带主方向的角点作为特征点，通过计算特征点间经旋转补偿的归一化互相关值建立特征点相似度矩阵，然后利用奇异值分解算法生成特征点匹配矩阵并获得特征点间的一一对应关系.在复杂自然图像上的实验结果表明，算法能够匹配任意角度旋转的图像，对局部遮挡、光照变化、随机噪声等具有较强的健壮性，并具有较快的计算速度和较高的匹配精度.此外，该算法易于和其他匹配技术进行融合并获得性能提升，其与SIFT描述子结合的匹配实验结果表明，该算法具有良好的扩展性和实用性.

Abstract: Image matching plays an important role in many fields, such as computer vision, remote sensing and medical image analysis. Traditional-correlation-based matching methods require heavy computation time and they are sensitive to image rotation. In this paper, a new image matching algorithm based on singular value decomposition is proposed to efficiently match image pairs with arbitrary rotation angle. Corner points with dominant orientation are firstly extracted as feature points in the two images. The initial set of feature point matches is then obtained by singular value decomposition of a correspondence strength matrix. A new expression of the correspondence strength matrix is introduced to handle more complicated imaging conditions. Each element of this matrix is computed from the similarity measure between two feature points based on normalized cross-correlation. The similarity measure is invariant to image rotation by taking into account the dominant orientation of the two feature points. Finally the epipolar geometry constraint is imposed on the initial matches in order to reject the false matches. Experimental results on complicated real images show the effectiveness and robustness of the algorithm. Moreover, it is easy to combine the proposed algorithm with other matching techniques. Experimental results with SVD-SIFT prove the expansibility and practicability of the algorithm.

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