计算机科学 ›› 2019, Vol. 46 ›› Issue (2): 286-293.doi: 10.11896/j.issn.1002-137X.2019.02.044
魏玉慧1, 王永军2, 王国东1, 刘红敏2, 王静2
WEI Yu-hui1, WANG Yong-jun2, WANG Guo-dong1, LIU Hong-min2, WANG Jing2
摘要： 特征匹配作为计算机视觉的一项关键技术而备受关注。近年来,基于描述子的特征点匹配技术取得了一系列突破性进展,但曲线长度不一、端点定位不准确以及周围包含的重复性纹理较多等因素,导致了曲线匹配研究依旧是一个极具挑战性的热点研究课题,且现有曲线匹配方法大多出现匹配总数少、匹配正确率低的问题。为增加特征匹配的总数和正确率,利用特征点和特征曲线的位置关系提出一种点线特征融合的误匹配剔除算法(Point Line feature Fusion,PLF)。首先定义点到曲线的距离,利用点、曲线描述子提取图像的点、线特征;其次确定落入匹配曲线对应支撑区域内的匹配点对,并根据匹配点组和曲线间的距离约束剔除错误曲线匹配;最后利用点线距离约束剔除匹配曲线支撑区域内的错误点匹配。实验选取了3种不同的点线组合,即SIFT技术提取的点特征分别与IOCD曲线描述子、IOMSD曲线描述子、GOCD曲线描述子提取的曲线特征相融合,验证算法对多种点、线描述子具有适用性,且该算法不仅适用于特征点与特征曲线的融合,亦适用于特征点与特征直线的融合,从而验证了其对多种图像特征具有适用性。实验结果表明,在旋转、视角变化、光照变化、压缩、噪音、模糊等变换条件下,该算法均能有效提高曲线特征匹配的匹配总数和匹配正确率,同时提高点匹配的正确率。
中图分类号:
| [1]PAN Z,LI Z,FAN H,et al.Feature Based Local Binary Pattern for Rotation Invariant Texture Classification[J].Expert Systems with Applications,2017,88:238-248. [2]ZHANG S,TIAN Q,LU K,et al.Edge-SIFT:Discriminative Binary Descriptor for Scalable Partial-Duplicate Mobile Search[J].IEEE Transactions on Image Processing A Publication of the IEEE Signal Processing Society,2013,22(7):2889-2902. [3]ZENG L,ZHANG S,ZHANG J,et al.Dynamic image mosaic via SIFT and dynamic programming[J].Machine Vision & Applications,2014,25(5):1271-1282. [4]LIU Y,HE F,ZHU X,et al.The Improved Characteristics of Bionic Gabor Representations by Combining with SIFT Key-points for Iris Recognition[J].Journal of Bionic Engineering,2015,12(3):504-517. [5]LI J,ALLINSON N.Building Recognition Using Local Oriented Features[J].IEEE Transactions on Industrial Informatics,2013,9(3):1697-1704. [6]LI Z,GAO S,KE N.Robust Object Tracking Based on Adaptive Templates Matching via the Fusion of Multiple Features[J].Journal of Visual Communication & Image Representation,2017,44:1-20. [7]LOWE D G.Distinctive Image Features from Scale-Invariant Keypoints[J].International Journal of Computer Vision,2004,60(2):91-110. [8]KE Y,SUKTHANKAR R.PCA-SIFT:a more distinctive representation for local image descriptors[C]∥ IEEE Computer So-ciety Conference on Computer Vision and Pattern Recognition.IEEE Computer Society,2004:506-513. [9]BAY H,TUYTELAARS T,GOOL L V.SURF:Speeded Up Robust Features[C]∥ European Conference on Computer Vision.Springer,Berlin,Heidelberg,2006:404-417. [10]CALONDER M,LEPETIT V,OZUYSAL M,et al.BRIEF: Computing a Local Binary Descriptor very Fast[J].IEEE Transactions on Pattern Analysis & Machine Intelligence,2012,34(7):1281. [11]RUBLEE E,RABAUD V,KONOLIGE K,et al.ORB:An efficient alternative to SIFT or SURF[C]∥ IEEE International Conference on Computer Vision.IEEE,2012:2564-2571. [12]LEUTENEGGER S,CHLI M,SIEGWART R Y.BRISK:Binary Robust invariant scalable keypoints[C]∥ IEEE International Conference on Computer Vision.IEEE,2012:2548-2555. [13]WANG Z,WU F,HU Z.MSLD:A robust descriptor for line matching[J].Pattern Recognition,2009,42(5):941-953. [14]FAN B,WU F,HU Z.Aggregating gradient distributions into intensity orders:A novel local image descriptor[C]∥IEEE Conference on Computer Vision and Pattern Recognition.IEEE Computer Society,2011:2377-2384. [15]WANG Z H,ZHI S S,LIU H M.IOMSD:Intensity order based mean-standed deviation descriptor[J].Pattern Recognition and Artificial Intelligence,2012,26(4):409-416.(in Chinese) 王志衡,智珊珊,刘红敏.基于亮度序的均值标准差描述子[J].模式识别与人工智能,2012,26(4):409-416. [16]LIU H M,ZHI S S,WANG Z H.IOCD:Intensity Order Curve Descriptor [J].International Journal of Pattern Recognition and Artificial Intellgence,2013,27(7):1355011. [17]LOURAKIS M I A,HALKIDIS S T,ORPHANOUDAKIS S C.Matching disparate views of planar surfaces using projective invariants[J].Image & Vision Computing,2000,18(9):673-683. [18]FAN B,WU F,HU Z.Line matching leveraged by point correspondences[C]∥ Computer Vision and Pattern Recognition.IEEE,2010:390-397. [19]ZHANG L,KOCH R.An efficient and robust line segment matching approach based on LBD descriptor and pairwise geometric consistency[J].Journal of Visual Communication & Ima-ge Representation,2013,24(7):794-805. [20]LIU H,CHEN L,WANG Z,et al.GOCD:Gradient Order Curve Descriptor[J].Ieice Transactions on Information & Systems,2017,E100.D(12):2973-2983. |
| [1] | 杨思燕,贺国旗,刘如意. 基于SIFT算法的大场景视频拼接算法及优化 Video Stitching Algorithm Based on SIFT and Its Optimization 计算机科学, 2019, 46(7): 286-291. https://doi.org/10.11896/j.issn.1002-137X.2019.07.044 |
| [2] | 朱永丰,朱述龙,张静静,朱永康. 基于ORB特征的单目视觉定位算法研究 Monocular Vision Alignment Algorithm Based on ORB 计算机科学, 2016, 43(Z6): 198-202. https://doi.org/10.11896/j.issn.1002-137X.2016.6A.047 |
| [3] | 柴先涛,梁久祯,稂龙亚. 基于曲率的全仿射曲线图像配准 Full Affine Curve in Curvature Scale Space Image Registration 计算机科学, 2016, 43(1): 53-56. https://doi.org/10.11896/j.issn.1002-137X.2016.01.012 |
| [4] | 王彩霞,吕 强,李海鸥,罗 升. 基于蛋白质进化配对的残基间距离约束挖掘方法 Mining Residues Distance Constraints from Protein Evolution Couplings by Classification 计算机科学, 2015, 42(7): 262-264. https://doi.org/10.11896/j.issn.1002-137X.2015.07.056 |
| [5] | 郭庆慧,梁秀霞,张锐. 基于稀疏特征点的单视点深度图像校准 Calibration for Single Viewpoint Depth Image Based on Sparse Feature Points 计算机科学, 2014, 41(Z6): 191-195. |
| [6] | 盖赟. 基于曲面形变的三维人脸样本配准 3D Face Registration Based on Surface Deformation 计算机科学, 2014, 41(Z11): 116-118. |
| [7] | 马正华,顾苏杭,戎海龙. 基于SIFT特征匹配的CamShift运动目标跟踪算法 CamShift Moving Object Tracking Algorithm Based on SIFT Feature Points Matching 计算机科学, 2014, 41(6): 291-294. https://doi.org/10.11896/j.issn.1002-137X.2014.06.058 |
| [8] | 路晓静,黄向生. 一种快速的空间变换模型计算方法 Fast Calculation Method of Space Transform Model 计算机科学, 2014, 41(3): 279-281. |
| [9] | 翟东海,李同亮,段维夏,鱼江,肖杰. 基于矩阵相似度的最佳样本块匹配算法及其在图像修复中的应用 Optimal Exemplar Matching Algorithm Based on Matrix Similarity and its Application in Image Inpainting 计算机科学, 2014, 41(1): 307-310. |
| [10] | 王万良,金亦挺,赵燕伟,郑建炜. 基于边缘相关性距离约束的角点匹配 Corner Matching Based on Edge Correlation Distance Constraints 计算机科学, 2013, 40(5): 283-286. |
| [11] | 徐正光，陈宸. 鲁棒且快速的特征点匹配算法 Robust and Fast Feature Points Matching 计算机科学, 2013, 40(2): 294-296. |
| [12] | . 视觉图像三维重构计算一般性框架研究 计算机科学, 2008, 35(8): 208-212. |
| [13] | 谭志国 孙即祥 滕书华. 基于仿射参数估计的迭代点匹配算法 计算机科学, 2007, 34(10): 221-225. |
|
||
首页