点线特征融合的误匹配剔除算法

doi:10.11896/j.issn.1002-137X.2019.02.044

Abstract

Abstract: Image feature matching plays an important role in computer vision,the feature point matching technology based on descriptors have made a series of achievements,since curves have different lengths,incorrect position of endpoints and contain lots of relative texture around neighbor,the research of feature curve matching is still a challenging topic,and many curve matching methods have the problem of fewer matches and low accuracy of feature matching.To improve the total number and accuracy of feature matching,this paper proposed a novel Point Line feature Fusion (PLF) algorithm based on the location relationship between feature points and feature curves.Firstly,it defines the distance from a point to a curve,and obtains the matched points and curves using point and curve descriptors respectively from the images.Secondly,it determines the matched point pairs in the support areas of one pair of matched curves,and eliminate the mismatch of curves according to the distance constraints between the obtained matched points and the curve.Then,it removes the mismatch of points according to the distance constraint between the point and the curve.Three combinations of points and curves have been used in the experiment,which are the points extracted by SIFT and the curves extracted by IOCD curve descriptor,the points extracted by SIFT and the curves extracted by IOMSD curve descriptor,the points extracted by SIFT and the curves extracted by GOCD curve descriptor.The method hasapplicabi-lity to many kinds of point and curve descriptors,it is not only suitable for the points and curves,but also for points and lines,it has applicability to many kinds of features.Experimental results show that the proposed algorithm can effectively improve the total number and accuracy of feature matching,and also increase the accuracy of point matching under image rotation,viewpoint change,illumination change,JPEG compression,noise and image blur.

Key words: Curve matching, Distance constraints, PLF algorithm, Point line feature fusion, Point matching

CLC Number:

TP391

WEI Yu-hui, WANG Yong-jun, WANG Guo-dong, LIU Hong-min, WANG Jing. Mismatch Elimination Algorithm Based on Point Line Feature Fusion[J].Computer Science, 2019, 46(2): 286-293.

References

[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.

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Mismatch Elimination Algorithm Based on Point Line Feature Fusion

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