基于相似度模型耦合角度制约规则的图像匹配算法
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摘要
为了克服当前图像匹配方法主要通过测量距离的方法来实现图像匹配,忽略了图像间的相似度,导致算法存在错误匹配较多以及鲁棒性较差的问题。本文提出了基于相似度模型耦合角度制约规则的图像匹配算法。采用FAST检测方法对图像特征进行检测,快速获取鲁棒特征点,以改善算法的匹配正确率。随后,利用SURF特征描述机制,通过计算特征圆域内的Haar小波响应值,对特征点进行描述。引入结构相似度SSIM(structural similarity index measurement)模型,将其与欧氏距离模型相结合,构造相似度模型,从结构相似度与测量距离两方面出发,将特征点进行粗匹配。最后,利用特征点的余弦关系,求取特征点间角度,建立角度制约规则,对粗匹配结果完成优化。实验结果显示:与典型的匹配方法相比,该算法具有更好的匹配性能较好,在多种几何变换下仍具有理想的匹配精度。
The current image matching methods mainly achieve image matching by measuring the distance, which neglect the similarity between images and result in more mismatches and poor robustness. In this paper, an image matching algorithm based on similarity degree model and coupling angle constraint rule is proposed. High-speed and high-accuracy feature detection method is used to detect the image features, and the feature points with high accuracy can be obtained fast, which is helpful to improve the matching accuracy of the algorithm. Using the feature description mechanism, the feature points are described by calculating the wavelet response values in the feature circle domain. The structure similarity model is introduced and combined with Euclidean distance model to construct similarity model. The feature points are roughly matched from the aspects of structure similarity and measurement distance. The cosine relation of feature points is used to calculate the angle between feature points,and the angle restriction rules are established to match the feature points accurately. Experimental results show that this matching algorithm has better matching performance and higher matching accuracy compared with the typical matching method.
The current image matching methods mainly achieve image matching by measuring the distance, which neglect the similarity between images and result in more mismatches and poor robustness. In this paper, an image matching algorithm based on similarity degree model and coupling angle constraint rule is proposed. High-speed and high-accuracy feature detection method is used to detect the image features, and the feature points with high accuracy can be obtained fast, which is helpful to improve the matching accuracy of the algorithm. Using the feature description mechanism, the feature points are described by calculating the wavelet response values in the feature circle domain. The structure similarity model is introduced and combined with Euclidean distance model to construct similarity model. The feature points are roughly matched from the aspects of structure similarity and measurement distance. The cosine relation of feature points is used to calculate the angle between feature points,and the angle restriction rules are established to match the feature points accurately. Experimental results show that this matching algorithm has better matching performance and higher matching accuracy compared with the typical matching method.
引文
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[15]Seonyeong P,Siyong K,Byongyong Y.A Novel Method of Cone Beam CT Projection Binning Based on Image Registration[J].IEEE Trans Med Imaging,2017,36(8):1733-1745.
[16]吴鹏徐,洪玲,宋文龙.结合小波金字塔的快速NCC图像匹配算法[J].哈尔滨工程大学学报,2017,38(5):791-796.
[17]Sebastián C C,JoséG G.SIFT Otimization and Automation for Matching Images from Multiple Temporal Sources[J].International Journal of Applied Earth Observation and Geoinformation,2017,57(1):113-122.
[2]Tony L.Image Matching Using Generalized Scale-Space Interest Points[J].Journal of Mathematical Imaging and Vision,2015,52(1):3-36.
[3]Hossain M T,Shyh W T.Multimodal Image Registration Technique Based on Improved Local Feature Descriptors[J].Journal of Electronic Imaging,2013,1(24):1-17.
[4]Zhao C Y,Zhao H C,Lv J F.Multimodal Image Matching Based on Multimodality Robust Line Segment Descriptor[J].Neurocomputing,2016,177(1):290-303.
[5]张焕龙,张秀娇,贺振东.基于布谷鸟搜索的图像匹配方法研究[J].郑州大学学报:理学版,2017,49(4):51-56.
[6]Tsai C H,Lin Y C.An Accelerated Image Matching Technique for UAV Orthoimage Registration[J].ISPRSJournal of Photogrammetry and Remote Sensing,2017,128(1):130-145.
[7]陈剑虹,韩小珍.结合FAST-SURF和改进k-d树最近邻查找的图像配准[J].西安理工大学学报,2016,32(2):213-217.
[8]Jia D,CAO J,Song W D.Colour FAST(CFAST)Match:Fast Affine Template Matching for Colour Images[J].Electronics Letters,2016,52(14):1220-1221.
[9]彭勃宇,王崴,周诚.面向增强现实的SUSAN-SURF快速匹配算法[J].计算机应用研究,2015,32(8):2538-2542.
[10]Dou J F,Qin Q,Tu Z M.Robust Image Matching with Cascaded Outliers Removal[J].Pattern Recognition and Image Analysis,2017,27(3):480-493.
[11]Beatriz O,Eva R,Jacint V.SURF-Based Mammalian Species Identification System[J].Multimedia Tools and Applications,2017,76(7):10133-10147.
[12]Zhang E L,Ma J,Wang X T.Improved SURF Algorithm for Color Remote Sensing Image Registration[J].Chinese Journal of Liquid Crystals and Displays,2017,32(2):144-152
[13]Sun Y W,Li H,Sun L.Use of Satellite Image for Constructing the Unmanned Aerial Vehicle Image Matching Framework[J].Journal of Applied Remote Sensing,2017,11(1):1-12.
[14]Bo Y,Bahetiyaer B,Ke L.Learning Quality Assessment of Retargeted Images[J].Signal Processing:Image Communication,2017,56(1):12-19.
[15]Seonyeong P,Siyong K,Byongyong Y.A Novel Method of Cone Beam CT Projection Binning Based on Image Registration[J].IEEE Trans Med Imaging,2017,36(8):1733-1745.
[16]吴鹏徐,洪玲,宋文龙.结合小波金字塔的快速NCC图像匹配算法[J].哈尔滨工程大学学报,2017,38(5):791-796.
[17]Sebastián C C,JoséG G.SIFT Otimization and Automation for Matching Images from Multiple Temporal Sources[J].International Journal of Applied Earth Observation and Geoinformation,2017,57(1):113-122.