基于差分制约耦合三角网约束的图像匹配算法
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摘要
为解决当前图像匹配算法忽略了相邻图像层次间灰度量级的差异性导致的较多的误检与漏检现象,使其匹配精度不高的问题,本文设计了基于差分制约模型与三角网优化的图像匹配技术.首先,利用差分高斯函数来构造差分制约方法,对相邻图像层次间的灰度量级进行一致性约束,准确提取图像特征点;然后,通过计算圆形邻域内的Haar小波响应值,确定特征点的主方向;再计算圆形邻域内的梯度与灰度特征,得到相应的特征向量;利用主方向与特征向量来生成实现特征点描述符.利用特征点描述符求取特征点之间欧氏距离的最近邻与次近邻比值,对图像特征完成初步匹配;最后,通过初匹配之间的空间关系构建三角网约束规则,对错误匹配特征点进行剔除,对匹配结果进行优化.实验结果表明:与当前图像匹配算法相比,所提算法具有更高的匹配正确度与鲁棒性.
At present many image matching algorithm mainly through to image feature detection and image matching in each image layer by using Hessian matrix, because the method ignores the differences between adjacent levels of image gray level of the image feature detection, missing a lot of feature points and there are more error detection. An image matching algorithm based on differential constraint model coupling triangulation constraint was proposed in this paper. First, the differential Gauss function is used to construct the differential control model, which is used to congrate the gray level between adjacent images, so as to achieve fast and accurate extraction of image feature points. Then, we calculate the main direction of the feature points by calculating the Haar wavelet response value in the circular neighborhood, then we get the gradient and grayscale feature in the circular neighborhood to get the feature vectors, so as to achieve the generation of the feature point descriptors. Finally, using the feature point descriptor for the minimum Euclidean distance between feature points and small Euclidean distance to match feature points, then using the matching space relationship between the feature points of constructing triangulation constraint rules, to remove the false feature points matching, thus completing the image matching. The simulation results and analysis show that: compared with the current image matching algorithm, the algorithm designed in this paper has higher matching accuracy and matching efficiency.
At present many image matching algorithm mainly through to image feature detection and image matching in each image layer by using Hessian matrix, because the method ignores the differences between adjacent levels of image gray level of the image feature detection, missing a lot of feature points and there are more error detection. An image matching algorithm based on differential constraint model coupling triangulation constraint was proposed in this paper. First, the differential Gauss function is used to construct the differential control model, which is used to congrate the gray level between adjacent images, so as to achieve fast and accurate extraction of image feature points. Then, we calculate the main direction of the feature points by calculating the Haar wavelet response value in the circular neighborhood, then we get the gradient and grayscale feature in the circular neighborhood to get the feature vectors, so as to achieve the generation of the feature point descriptors. Finally, using the feature point descriptor for the minimum Euclidean distance between feature points and small Euclidean distance to match feature points, then using the matching space relationship between the feature points of constructing triangulation constraint rules, to remove the false feature points matching, thus completing the image matching. The simulation results and analysis show that: compared with the current image matching algorithm, the algorithm designed in this paper has higher matching accuracy and matching efficiency.
引文
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[14]Beatriz Otero,Eva Rodriguez,Jacient Ventura.SURF-based Mammalian Species Identification System[J].Multimedia Tools and Applications,2017,76(7):10133-10147.
[15]王向军,邢峰,刘峰.Delaunay三角剖分和仿射约束的特征相同多物体同名点立体[J].光学学报,2016,36(11):197-204.
[16]Javad Khodadoust,Ali Mohammad Khodadoust.Fingerprint indexing based on expanded Delaunay triangulation[J].Expert Systems with Applications,2017,81(3):251-267.
[17]Dou Jianfang,Qin Qin,Tu Zimei.Robust Image Matching with Cascaded Outlier’s Removal[J].Pattern Recognition and Image Analysis,2017,27(3):480-493.
[18]胡旻涛,彭勇,徐赟.基于改进SURF的快速图像配准算法[J].传感器与微系统,2017,36(11):151-154.
[19]Li Jiayuan,Hu Qingwu,Ai Mingyao.Robust Feature Matching for Geospatial Images Via An Affine-Invariant Coordinate System[J].The Photogram metric Record,2017,159(32):317-331.
[2]付利军,张福泉,杨金劳.基于信息特征耦合夹角一致性规则的图像匹配算法[J].包装工程,2018,39(9):190-199.
[3]Chen Yong,Shang Lei.Improved SIFT Image Registration Algorithm on Characteristic Statistical Distributions and Consistency Constraint[J].Optik-International Journal for Light and Electron Optics,2016,127(2):900-911.
[4]Zhao Chunyang,Zhao Huaici,Lv Jinfeng.Multimodal Image Matching Based on Multimodality Robust Line Segment Descriptor[J].Neurocomputing,2016,177(3):290-303.
[5]Kahaki S M,Jan Nordin.Deformation Invariant Image Matching Based on Dissimilarity of Spatial Features[J].Neurocomputing,2016,175(B):1009-1018.
[6]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.
[7]Lukas Roth,Andresas Kuhn,Helmut Mayer.Wide-Baseline Image Matching with Projective View Synthesis and Calibrated Geometric Verification[J].PFG-Journal of Photogrammetry,Remote Sensing and Geoinformation Science,2017,85(2):85-95.
[8]Sebasti Castillo Carrión.SIFT Optimization and Automation for Matching Images From Multiple Temporal Sources[J].International Journal of Applied Earth Observation and Geoinformation,2017,57(3):113-122.
[9]Ashok Aravindan,Anzar S-M.Robust Partial Fingerprint Recognition Using Wavelet SIFT Descriptors[J].Pattern Analysis and Applications,2017,20(4):963-979.
[10]Huang Minming,Mu Zhichun,Zeng Hui.Efficient Image Classification Via Sparse Coding Spatial Pyramid Matching Representation of SIFT-WCS-LTP Feature[J].Image Processing,IET,2016,10(1):61-67.
[11]Zhang Chenfe,Wu Yaozu,Liu Ning.Enhanced SURF-Based Image Matching Using Pre-and Post-processing[J].Digital TV and Wireless Multimedia Communication,2017,685(12):83-92.
[12]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.
[13]王招娣,王贤立,杨数强.基于颜色和SURF特征的图像匹配算法[J].电子科技,2017,30(12):67-71.
[14]Beatriz Otero,Eva Rodriguez,Jacient Ventura.SURF-based Mammalian Species Identification System[J].Multimedia Tools and Applications,2017,76(7):10133-10147.
[15]王向军,邢峰,刘峰.Delaunay三角剖分和仿射约束的特征相同多物体同名点立体[J].光学学报,2016,36(11):197-204.
[16]Javad Khodadoust,Ali Mohammad Khodadoust.Fingerprint indexing based on expanded Delaunay triangulation[J].Expert Systems with Applications,2017,81(3):251-267.
[17]Dou Jianfang,Qin Qin,Tu Zimei.Robust Image Matching with Cascaded Outlier’s Removal[J].Pattern Recognition and Image Analysis,2017,27(3):480-493.
[18]胡旻涛,彭勇,徐赟.基于改进SURF的快速图像配准算法[J].传感器与微系统,2017,36(11):151-154.
[19]Li Jiayuan,Hu Qingwu,Ai Mingyao.Robust Feature Matching for Geospatial Images Via An Affine-Invariant Coordinate System[J].The Photogram metric Record,2017,159(32):317-331.