基于CSS角点检测的快速匹配算法
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摘要
为了实现准确、快速的图像匹配,从角点检测与描述子两方面入手,提出了一种基于CSS角点检测的匹配算法。首先,在曲率尺度空间下,检测图像在不同尺度下的角点并剔除不稳定角点;其次,基于曲率对图像轮廓描述的精确性,以特征点为中心划分3×4的子邻域,计算子邻域内轮廓曲线点的高斯加权曲率等4维向量特征,建立48维描述子,由于CSS角点检测包含曲率计算,因此生成描述子时避免了曲率的二次计算,提高了匹配速度;最后,提出一种"二进制距离"方法对描述子进行匹配,进一步优化匹配速度。通过实验证明,在保证精度的情况下,CSS快速匹配算法大幅度缩短了匹配时间,对旋转、亮度变化具有较好的匹配效果。
In order to realize accurate and rapid image matching, this paper proposes a corner matching algorithm based on corner point detection and the descriptor. Firstly, in the curvature scale space, the corner points of the image at different scales are detected, and the unstable corner points are removed. Secondly,based on the accuracy of the curvature describing the contour of the image, the neighborhood is divided into3 × 4 sub-neighborhoods taking the character points as the center, the 4-dimensional vector features in the sub-neighborhood are calculated, such as the Gaussian weighted curvature of the contour curve points, and a48-dimensional descriptor is built. Since the CSS corner point detection includes curvature calculation, the quadratic calculation of the curvature is avoided when generating the descriptor, and thus the matching speed is improved. Finally, a binary distance method is proposed to match the descriptors to further optimize the matching speed. Experiments have proved that, with the assurance of accuracy, the CSS fast matching algorithm can shorten the matching time to a great extent and achieve satisfying matching results under rotation and illumination changes.
In order to realize accurate and rapid image matching, this paper proposes a corner matching algorithm based on corner point detection and the descriptor. Firstly, in the curvature scale space, the corner points of the image at different scales are detected, and the unstable corner points are removed. Secondly,based on the accuracy of the curvature describing the contour of the image, the neighborhood is divided into3 × 4 sub-neighborhoods taking the character points as the center, the 4-dimensional vector features in the sub-neighborhood are calculated, such as the Gaussian weighted curvature of the contour curve points, and a48-dimensional descriptor is built. Since the CSS corner point detection includes curvature calculation, the quadratic calculation of the curvature is avoided when generating the descriptor, and thus the matching speed is improved. Finally, a binary distance method is proposed to match the descriptors to further optimize the matching speed. Experiments have proved that, with the assurance of accuracy, the CSS fast matching algorithm can shorten the matching time to a great extent and achieve satisfying matching results under rotation and illumination changes.
引文
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[2]柯杉,王博亮,黄晓阳.一种改进的SIFT算法及其在医学图像配准中的应用[J].厦门大学学报:自然科学版,2010,49(3):354-358.
[3]胡文超,周伟,关键.基于改进SIFT的遥感图像匹配方法[J].电光与控制,2017,24(5):36-39.
[4] SUZUKI T. Vision based localization of a small UAV for generating a large mosaic images[C]//IEEE SICE Annual Conference,Taiwan,2010:2960-2964.
[5]熊英,马惠敏. 3维物体SIFT特征的提取与应用[J].中国图象图形学报,2010,15(5):814-819.
[6]刘洋,韩广良,史春蕾.基于SIFT算法的多表情人脸识别[J].液晶与显示,2016,31(12):1156-1160.
[7]任立胜,王立中.基于曲率尺度空间的角点检测图像匹配算法分析[J].电子技术应用,2016,42(12):112-114,118.
[8]高晶,吴育峰,吴昆,等.基于角点检测的图像匹配算法[J].仪器仪表学报,2013,34(8):1717-1725.
[9] LOWE D G. Distinctive image features from scale invariant keypoints[J]. International Journal of Computer Vision,2004,60(2):91-110.
[10] MOKHTARIAN F,SUOMELA R. Robust image corner detection through curvature scale space[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,1998,20(12):1376-1381.