12.2 Matching Linear Features

Chapter Contents (Back)
Matching, Lines.

12.2.1 2-D Line Segments with 2-D Structure

Chapter Contents (Back)
Matching, Lines. Line Matching. Line implies infinite, everything is really a segment.
See also Line Segment Based Stereo Analysis, Line Matching.

Edmonds, J.,
Maximum Matching and a Polyhedron with (0, 1) Vertices,
NBS(68)(b), 1965, pp.125-130. BibRef 6500

Edmonds, J., and Johnson, E.L.,
Matching, Euler Tours and the Chinese Postman,
Math. Programming(5), 1973, pp. 88-124. BibRef 7300

Gips, J.[James],
A syntax-directed program that performs a three-dimensional perceptual task,
PR(6), No. 3-4, December 1974, pp. 189-199.
Elsevier DOI 0309
Analyze mental rotation problem with line drawings. BibRef

Udupa, K.J., Murthy, I.S.N.,
Some new concepts for encoding line patterns,
PR(7), No. 4, December 1975, pp. 225-233.
Elsevier DOI 0309
A sequence of turning and end points.
See also New Concepts for Three-Dimensional Shape Analysis. BibRef

Keegan, J.F.[James F.], Lesk, A.M.[Arthur M.],
How Can You Tell if Two Line Drawings Are the Same?,
CGIP(6), No. 1, February 1977, pp. 90-92.
Elsevier DOI Reduce line drawings to canonical form. Equivalent drawings will have same form. BibRef 7702

Akl, S.G., Toussaint, G.T.,
An Improved Algorithm to Check for Polygon Similarity,
IPL(7), 1978, pp. 127-128. BibRef 7800
Earlier: PRIP78(39-41). BibRef

Shapiro, L.G.[Linda G.],
Inexact matching of line drawings in a syntactic pattern recognition system,
PR(10), No. 5-6, 1978, pp. 313-321.
Elsevier DOI 0309
components and interrelationships. BibRef

Medioni, G.G., and Nevatia, R.,
Matching Images Using Linear Features,
PAMI(6), No. 6, November 1984, pp. 675-685. BibRef 8411 USC Computer Vision BibRef
Earlier:
Matching Linear Features of Images and Maps,
DARPA82(103-111). BibRef
And: A1 only: first title: Ph.D.August 1983, BibRef USC Matching, Lines. Relaxation. This system matches two views of a scene (or a map and an image) where there are few geometric distortions--a global transformation is sufficient to align the line segments in the two views. Given two line segments in the model and a match of one of these with a segment in the image, the valid area of the second segment can be restricted to a parallelogram. This is caused by the fact that the segments are allowed to partially match anywhere along their lengths. This parallelogram is used to judge whether a match is valid when another is assumed. Several matches (3) are derived with a relaxation based procedure that finds a kernel of matching segments. This kernel is used to find all the other matching line segments since the segments in the kernel constrain the position of all the other segments in the image. This method was used in model to image matching and image to image matching and can be used in a multi-resolution mode if desired.
See also Segment-Based Stereo Matching. BibRef

Kaufman, P., Medioni, G.G., and Nevatia, R.,
Visual Inspection Using Linear Features,
PR(17), No. 5, 1984, pp. 485-491.
Elsevier DOI BibRef 8400 USC Computer Vision
PDF File. BibRef
Earlier: CVPR83(496-497). An application of the segment matching technique. BibRef

Medioni, G.G., Huertas, A., and Wilson, M.R.,
Automatic Registration of Color Separation Films,
MVA(4), No. 1, 1991. BibRef 9100 USC Computer VisionFeature based approach to get very accurate alignment. BibRef

Medioni, G.G.[Gerard G.], Wilson, M.R.[Monti R.], Prohaska, T.F.[Timothy F.], Poretta, L.R.[Lynn R.],
Method and apparatus for registering color separation film,
US_Patent4,849,914, July 18, 1989.
WWW Link. BibRef 8907

Wilson, M.R.[Monti R.], Hutchison, V.E.[Victor E.], Bendure, W.J.[William J.], Anderson, F.W.[Frederick W.],
Method and apparatus for registering color separation film 1,
US_Patent4,641,244, February 3, 1987.
WWW Link. BibRef 8702

Medioni, G., Huertas, A., Wilson, M.R.,
The Registar Machine: From Conception to Installation,
WACV92(224-231).
IEEE DOI BibRef 9200 USC Computer Vision BibRef

Medioni, G.,
Matching Regions in Aerial Images,
CVPR83(364-365). BibRef 8300 USC Computer Vision BibRef
Earlier:
Matching High Level Features of an Aerial Image with a Map or Another Image,
CVWS82(113-115). BibRef

Matsuyama, T.[Takashi], Arita, H.[Hidekazu], Nagao, M.[Makoto],
Structural Matching of Line Drawings Using the Geometric Relationship between Line Segments,
CVGIP(27), No. 2, August 1984, pp. 177-194. (Kyoto)
Elsevier DOI Relationships between pairs of lines are used in the matching process. The relations seem to be sets of three lines which intersect one line. The properties would then be related to this pattern. This may be useful. BibRef 8408

McIntosh, J.H.[James H.], Mutch, K.M.[Kathleen M.],
Matching Straight Lines,
CVGIP(43), No. 3, September 1988, pp. 386-408.
Elsevier DOI The matching is based on parameters extracted from the regions where the straight line is located (i.e. features of the segments), not on adjacencies, contours, etc. BibRef 8809

Liu, Y.C.[Yun-Cai], and Huang, T.S.,
Determining Straight Line Correspondences from Intensity Images,
PR(24), No. 6, 1991, pp. 489-504.
Elsevier DOI For use of this algorithm:
See also Estimation of Rigid Body Motion Using Straight Line Correspondences.
See also Rigid Object Motion Estimation from Intensity Images Using Straight Line Correspondences. BibRef 9100

Borgefors, G.[Gunilla],
Hierarchical Chamfer Matching: A Parametric Edge Matching Algorithm,
PAMI(10), No. 6, November 1988, pp. 849-865.
IEEE DOI BibRef 8811
Earlier:
An Improved Version of the Chamfer Matching Algorithm,
ICPR84(1175-1177). BibRef
Earlier:
Chamfering: A fast method for obtaining approximations of the Euclidean distance in N dimensions,
SCIA83(250-255). Pyramid Structure. Chamfer Matching. Matching, Chamfer. Applies chamfer techniques to a pyramid representation for efficiency. BibRef

Burr, D.J.,
Elastic Matching of Line Drawings,
PAMI(3), No. 6, November 1981, pp. 708-713. BibRef 8111
Earlier: ICPR80(223-228). (Bell Labs). Interesting for a low level Comparison of lines using a local stretching of the segment data. E.g. use for determining edge-edge match in a global system. BibRef

Burr, D.J.,
A Technique for Comparing Curves,
PRIP79(271-277). BibRef 7900

Fritsch, D.S., Pizer, S.M., Morse, B.S., Eberly, D.H., Liu, A.,
The Multiscale Medial Axis and Its Applications in Image Registration,
PRL(15), No. 5, May 1994, pp. 445-452. Medial Axis Transform. BibRef 9405

Cox, I.J., Kruskal, J.B., and Wallach, D.A.,
Predicting and Estimating the Accuracy of a Subpixel Registration Algorithm,
PAMI(12), No. 8, August 1990, pp. 721-734.
IEEE DOI Discusses different causes of errors and enables the prediction of the match error. BibRef 9008

Cox, I.J., and Kruskal, J.B.,
On the Congruence of Noisy Images to Line Segment Models,
ICCV88(252-258).
IEEE DOI Find the closest line. See the above for an analysis. BibRef 8800

Barrow, H.G., Tenenbaum, J.M., Bolles, R.C., and Wolf, H.C.,
Parametric Correspondence and Chamfer Matching: Two New Techniques for Image Matching,
IJCAI77(659-663). BibRef 7700
And: DARPA77(21-27). Matching, Chamfer. Chamfer matching. The early incomplete chamfer matching paper. Weight the distance from the ideal match, store the values in an image. This is used to guide the search for the best fit. k For the model, a chamfer image is created where the image value is based on the distance to a line in the model (this is the derivation of the name). There is no better reference that I know, this one is poor, but at the time there were fewer journal publications. BibRef

Lai, J.Z.C.,
Sensitivity Analysis Of Line Correspondence,
SMC(25), No. 6, June 1995, pp. 1016-1023. BibRef 9506

Clark, C.S., Conti, D.K., Eckhardt, W.O., McCulloh, T.A., Nevatia, R., and Tseng, D.Y.,
Matching of Natural Terrain Scenes,
ICPR80(217-222). (Hughes). BibRef 8000 USC Computer Vision Matching, Lines. The program matches two views of a scene with natural terrain using the boundaries of dominant objects. The input line segments can be generated by any means, but this paper used objects extracted by a histogram based segmentation procedure that gets some of the more obvious regions. Straight lines segment representations of the regions provide the input to the matching procedure. From three lines in the image, form an initial guess for the match. Evaluate the match by applying the computed transform to all other lines and finding corresponding line segments in the second view. The best match, in terms of the number and quality of the matching segments, provides the global transformation between the two views. BibRef

Clark, C.S., Luk, A., McNary, C.,
Feature Based Scene Analysis and Model Matching,
NATO ASI(), Pattern Recognition and Signal Processing Paris, 1978. Early version of the other Clark et al. papers. BibRef 7800

Clark, C.S., Eckhardt, W.O., McNary, C., Nevatia, R., Olin, K., and van Orden, E.,
High Accuracy Model Matching for Scenes Containing Man-Made Structures,
SPIE(186), Symposium on Digital Processing of Aerial Images, 1979, pp. 54-62. BibRef 7900 USC Computer VisionRelated to the first Clark et al. paper. BibRef

Xie, M.[Ming],
Cooperative Strategy for Matching Multilevel Edge Primitives,
IVC(13), No. 2, March 1995, pp. 89-99.
Elsevier DOI BibRef 9503

Beveridge, J.R., and Riseman, E.M.,
Optimal Geometric Model-Matching under Full 3D Perspective,
CVIU(61), No. 3, May 1995, pp. 351-364.
DOI Link
PS File. BibRef 9505
Earlier:
Hybrid Weak-Perspective and Full-Perspective Matching,
CVPR92(432-438).
IEEE DOI
PS File. BibRef
Earlier:
Can Too Much Perspective Spoil the View? A Case Study in 2D Affine Versus 3D Perspective Model Matching,
DARPA92(655-663). BibRef
And: COINSTR-91-86, November 1991. BibRef

Beveridge, J.R., Riseman, E.M.,
How Easy Is Matching 2D Line Models Using Local Search?,
PAMI(19), No. 6, June 1997, pp. 564-579.
IEEE DOI
PS File. 9708
BibRef

Beveridge, J.R.[J. Ross], Graves, C.R.[Christopher R.], Steinborn, J.[Jim],
Comparing Random-Starts Local Search with Key-Feature Matching,
IJCAI97(1476-1481).
PS File. BibRef 9700

Beveridge, J.R.,
Local Search Algorithms for Geometric Object Recognition: Finding the Optimal Correspondence and Pose,
UMassTR 93-71, September 1993. BibRef 9309 Ph.D.Thesis.
PS File. BibRef

Whitley, D., Beveridge, J.R., Graves, C., Mathias, K.,
Test Driving Three 1995 Genetic Algorithms: New Test Functions and Geometric Matching,
Heuristics(1), No. 1, 1995, pp. 77-104. BibRef 9500

Collins, R.T., and Beveridge, J.R.,
Matching Perspective Views of Coplanar Structures Using Projective Unwarping and Similarity Matching,
CVPR93(240-245).
IEEE DOI BibRef 9300
And: DARPA93(459-463). BibRef
And: COINS94-06, February 1994. Line segment matching for rectification. BibRef

Beveridge, J.R., Riseman, E.R., Graves, C.R.,
Demonstrating Polynomial Run-Time Growth for Local Search Matching,
SCV95(533-538).
IEEE DOI Colorado State Univ.. U. of Massachusetts. Colorado State Univ.. BibRef 9500

Beveridge, J.R., Weiss, R., and Riseman, E.M.,
Optimization of 2-Dimensional Model Matching,
ICPR90(I: 18-23).
IEEE DOI BibRef 9000
And: DARPA89(815-830). BibRef
And:
Optimization of 2-Dimensional Model Matching Under Rotation, Translation and Scale,
COINSTR-89-57, June 1989. Given a line model and edges in the image, match by a Hough approach over global translation and rotation. This is followed by a local search to find the exact match. The results are good, but the idea is basic. BibRef

Stevens, M.R., Beveridge, J.R.[J. Ross],
Precise Matching of 3-D Target Models to Multisensor Data,
IP(6), No. 1, January 1997, pp. 126-142.
IEEE DOI
PS File. 9703
BibRef

Stevens, M.R.[Mark R.], Beveridge, J.R.[J. Ross],
Localized Scene Interpretation from 3D Models, Range, and Optical Data,
CVIU(80), No. 2, November 2000, pp. 111-129.
DOI Link 0012
BibRef

Stevens, M.R.[Mark R.], Beveridge, J.R.[J. Ross],
Integrating Graphics and Vision for Object Recognition,
KluwerOctober 2000, ISBN 0-7923-7207-7.
WWW Link. BibRef 0010

Stevens, M.R., Beveridge, J.R.,
Interleaving 3D Model Feature Prediction and Matching to Suport Multi-Sensor Object Recognition,
ICPR96(I: 607-611).
IEEE DOI 9608
BibRef ARPA96(699-706). (Colorado State Univ., USA)
PS File. And the IUW Version:
PS File. BibRef

Stevens, M.R.,
Reasoning About Object Appearance in the Context of a Scene,
Ph.D.Thesis, Colorado State University, 1999. BibRef 9900

Beveridge, J.R., Graves, C., Lesher, C.E.,
Local Search as a Tool for Horizon Line Matching,
ARPA96(683-686).
PS File. BibRef 9600

Lee, C.H.[Chia-Hoang], Joshi, A.[Anupam],
On Correspondence, Line Tokens And Missing Tokens,
PR(28), No. 11, November 1995, pp. 1751-1764.
Elsevier DOI BibRef 9511

Bhandarkar, S.M., Suk, M.,
Sensitivity Analysis for Matching and Pose Computation Using Dihedral Junctions,
PR(24), No. 6, 1991, pp. 505-513.
Elsevier DOI BibRef 9100

Nagao, M.,
Shape Recognition by Human-Like Trial and Error Random Processes,
PRAI(10), 1996, pp. 473-490. BibRef 9600

Lee, H.J., Yu, D.J.,
Line-Based Structural Matching Via Segment Splitting,
PRL(11), 1990, pp. 181-189. BibRef 9000

Christie, S., Kvasnik, F.,
Correlation and Image Recognition with Surface-Scattered Light,
AppOpt(36), No. 14, May 10 1997, pp. 3013-3021. 9706
BibRef

Atalay, V.[Volkan], Yilmaz, M.U.[M. Ugur],
A matching algorithm based on linear features,
PRL(19), No. 9, 31 July 1998, pp. 857-867. BibRef 9807

Pearce, A.R.[Adrian R.], Caelli, T.M.[Terry M.],
Interactively Matching Hand-Drawings Using Induction,
CVIU(73), No. 3, March 1999, pp. 391-403.
DOI Link BibRef 9903

Park, S.H.[Sang Ho], Lee, K.M.[Kyoung Mu], Lee, S.U.[Sang Uk],
A Line Feature Matching Technique Based on an Eigenvector Approach,
CVIU(77), No. 3, March 2000, pp. 263-283.
DOI Link 0004
BibRef

Zhu, Z.F.[Zhen-Feng], Tang, M.[Ming], Lu, H.Q.[Han-Qing],
A new robust circular Gabor based object matching by using weighted Hausdorff distance,
PRL(25), No. 4, March 2004, pp. 515-523.
Elsevier DOI 0402
Matching edge maps from Gabor edge filter. BibRef

Meikle, S.[Stuart], Amavasai, B.P., Caparrelli, F.,
Towards real-time object recognition using pairs of lines,
RealTimeImg(11), No. 1, February 2005, pp. 31-43.
Elsevier DOI 0506
BibRef

Du, H.[Hao], Chen, Y.Q.[Yan Qiu],
Rectified nearest feature line segment for pattern classification,
PR(40), No. 5, May 2007, pp. 1486-1497.
Elsevier DOI 0702
Pattern classification; Nearest feature line; Rectified nearest feature line segment; Distribution concentration; Interpolation and extrapolation accuracy BibRef

Trias-Sanz, R.[Roger], Pierrot-Deseilligny, M.[Marc], Louchet, J.[Jean], Stamon, G.[Georges],
Methods for Fine Registration of Cadastre Graphs to Images,
PAMI(29), No. 11, November 2007, pp. 1990-2000.
IEEE DOI 0711
Match edges in imprecise graph to precise edges in the image. BibRef

Zhang, L.[Lulin], Rupnik, E.[Ewelina], Pierrot-Deseilligny, M.[Marc],
Feature matching for multi-epoch historical aerial images,
PandRS(182), 2021, pp. 176-189.
Elsevier DOI 2112
Feature matching, Historical images, Multi-epoch, Pose estimation, Self-calibration BibRef

Trias-Sanz, R., Pierrot-Deseilligny, M.,
A region-based method for graph to image registration with an application to cadastre data,
ICIP04(III: 1703-1706).
IEEE DOI 0505
BibRef

Ko, S.[San], Lee, K.M.[Kyoung Mu],
Structural Object Recognition Using Entropy Correspondence Measure of Line Features,
IEICE(E91-D), No. 1, January 2008, pp. 78-85.
DOI Link 0801
BibRef

Wang, Z.H.[Zhi-Heng], Wu, F.C.[Fu-Chao], Hu, Z.Y.[Zhan-Yi],
MSLD: A robust descriptor for line matching,
PR(42), No. 5, May 2009, pp. 941-953.
Elsevier DOI 0902
Line matching; Line descriptor; MSLD descriptor
See also Towards reliable matching of images containing repetitive patterns. BibRef

Wang, Z.H.[Zhi-Heng], Liu, H.M.[Hong-Min], Wu, F.C.[Fu-Chao],
Image Content Based Curve Matching Using HMCD Descriptor,
ACCV09(III: 448-455).
Springer DOI 0909
BibRef

Fan, B.[Bin], Wu, F.C.[Fu-Chao], Hu, Z.Y.[Zhan-Yi],
Robust line matching through line-point invariants,
PR(45), No. 2, February 2012, pp. 794-805.
Elsevier DOI 1110
BibRef
Earlier:
Line matching leveraged by point correspondences,
CVPR10(390-397).
IEEE DOI 1006
Line matching; Point matching; Line-point invariants BibRef

Ueaoki, K.[Katsutoshi], Iwata, K.[Kazunori], Suematsu, N.[Nobuo], Hayashi, A.[Akira],
Matching Handwritten Line Drawings with Von Mises Distributions,
IEICE(E94-D), No. 12, December 2011, pp. 2487-2494.
WWW Link. 1112
BibRef

Iwata, K.[Kazunori], Yamamoto, H.[Hiroki], Mimura, K.[Kazushi],
An Extended Scheme for Shape Matching with Local Descriptors,
IEICE(E104-D), No. 2, February 2021, pp. 285-293.
WWW Link. 2102
BibRef

Shang, S.[Shuo], Yuan, B.[Bo], Deng, K.[Ke], Xie, K.[Kexin], Zheng, K.[Kai], Zhou, X.F.[Xiao-Fang],
PNN query processing on compressed trajectories,
GeoInfo(16), No. 3, July 2012, pp. 467-496.
WWW Link. 1202
Trajectory compression for spatial-temporal database. Path nearest neighbor: PNN. Find most likely path in a given road network. BibRef

Mosaddegh, S.[Saleh], Fofi, D.[David], Vasseur, P.[Pascal],
Short baseline line matching for central imaging systems,
PRL(33), No. 16, 1 December 2012, pp. 2292-2301.
Elsevier DOI 1210
BibRef
Earlier:
A Generic Method of Line Matching for Central Imaging Systems under Short-Baseline Motion,
CIAP09(939-948).
Springer DOI 0909
Central omnidirectional image; Catadioptric; Fish-eye; Line segments matching; Short baseline; Constructed scene BibRef

Mosaddegh, S.[Saleh], Fofi, D.[David], Vasseur, P.[Pascal], Ainouz, S.[Samia],
Line Matching across Catadioptric Images,
OMNIVIS08(xx-yy). 0810
BibRef

Altnçay, H.[Hakan], Erenel, Z.[Zafer],
Avoiding the interpolation inaccuracy in nearest feature line classifier by spectral feature analysis,
PRL(34), No. 12, 1 September 2013, pp. 1372-1380.
Elsevier DOI 1306
Nearest feature line; Interpolation inaccuracy; Spectral clustering; Spectral feature analysis; Shortest feature line segment BibRef

Zhang, L.[Lilian], Koch, R.[Reinhard],
An efficient and robust line segment matching approach based on LBD descriptor and pairwise geometric consistency,
JVCIR(24), No. 7, 2013, pp. 794-805.
Elsevier DOI 1309
BibRef
Earlier:
Line Matching Using Appearance Similarities and Geometric Constraints,
DAGM12(236-245).
Springer DOI 1209
Line segment matching BibRef

Kaliamoorthi, P., Kakarala, R.,
Directional Chamfer Matching in 2.5 Dimensions,
SPLetters(20), No. 12, 2013, pp. 1151-1154.
IEEE DOI 1311
image matching BibRef

Jegelka, S.[Stefanie], Kapoor, A.[Ashish], Horvitz, E.J.[Eric J.],
An Interactive Approach to Solving Correspondence Problems,
IJCV(108), No. 1-2, May 2014, pp. 49-58.
Springer DOI 1405
interactive, occlusions, deformations. BibRef

Kondo, T.[Toshiaki],
Gradient orientation pattern matching with the Hamming distance,
PR(47), No. 10, 2014, pp. 3387-3404.
Elsevier DOI 1406
Pattern matching. Gradient orientations, not image values. BibRef

López, J.[Juan], Santos, R.[Roi], Fdez-Vidal, X.R.[Xosé R.], Pardo, X.M.[Xosé M.],
Two-view line matching algorithm based on context and appearance in low-textured images,
PR(48), No. 7, 2015, pp. 2164-2184.
Elsevier DOI 1504
Line detection and matching BibRef

Yu, Q.[Qian], Wei, H.[Hui], Yang, C.Z.[Cheng-Zhuan],
Local part chamfer matching for shape-based object detection,
PR(65), No. 1, 2017, pp. 82-96.
Elsevier DOI 1702
Chamfer matching BibRef

Wei, H.[Hui], Yang, C.Z.[Cheng-Zhuan], Yu, Q.[Qian],
Contour segment grouping for object detection,
JVCIR(48), No. 1, 2017, pp. 292-309.
Elsevier DOI 1708
Shape-based, object, detection BibRef

Shi, J.C.[Jia-Cha], Wang, X.Y.[Xuan-Yin],
A local feature with multiple line descriptors and its speeded-up matching algorithm,
CVIU(162), No. 1, 2017, pp. 57-70.
Elsevier DOI 1710
Local feature BibRef

El Mokhtari, K.[Karim], Reboul, S.[Serge], Choquel, J.B.[Jean-Bernard], Stienne, G.[Georges], Amami, B.[Benaissa], Benjelloun, M.[Mohammed],
Circular particle fusion filter applied to map matching,
IET-ITS(11), No. 8, October 2017, pp. 491-500.
DOI Link 1710
BibRef

Lin, J.Y.[Jing-Yi], Ban, Y.F.[Yi-Fang],
Comparative Analysis on Topological Structures of Urban Street Networks,
IJGI(6), No. 10, 2017, pp. xx-yy.
DOI Link 1710
Much more than just matching. Compare topology of streets. BibRef

Bai, L.F.[Li-Fei], Yang, X.Q.[Xian-Qiang], Gao, H.J.[Hui-Jun],
Improved chamfer matching method for surface mount component positioning,
IET-IPR(11), No. 12, Decmeber 2017, pp. 1265-1272.
DOI Link 1712
BibRef

Jia, Q.[Qi], Fan, X.[Xin], Gao, X.K.[Xin-Kai], Yu, M.Y.[Mei-Yu], Li, H.J.[Hao-Jie], Luo, Z.X.[Zhong-Xuan],
Line matching based on line-points invariant and local homography,
PR(81), 2018, pp. 471-483.
Elsevier DOI 1806
Line matching, Characteristic number, Line-points projective invariant BibRef

Li, Y., Wang, F., Stevenson, R., Fan, R., Tan, H.,
Reliable Line Segment Matching for Multispectral Images Guided by Intersection Matches,
CirSysVideo(29), No. 10, October 2019, pp. 2899-2912.
IEEE DOI 1910
feature extraction, image matching, image segmentation, improved matching accuracy, detection inaccuracy, line matching, intersection BibRef

Wei, D.[Dong], Zhang, Y.J.[Yong-Jun], Li, C.[Chang],
Robust line segment matching via reweighted random walks on the homography graph,
PR(111), 2021, pp. 107693.
Elsevier DOI 2012
Line segment matching, Epipolar geometry, Reweighted random walks, Graph matching BibRef

Wei, D.[Dong], Zhang, Y.J.[Yong-Jun], Liu, X.[Xinyi], Li, C.[Chang], Li, Z.[Zhuofan],
Robust line segment matching across views via ranking the line-point graph,
PandRS(171), 2021, pp. 49-62.
Elsevier DOI 2012
Line segment matching, Graph rank, Scene plane theory, 3D reconstruction BibRef

Wang, J.X.[Jing-Xue], Zhu, Q.[Qing], Liu, S.[Suyan], Wang, W.X.[Wei-Xi],
Robust line feature matching based on pair-wise geometric constraints and matching redundancy,
PandRS(172), 2021, pp. 41-58.
Elsevier DOI 2101
Line segment matching, Line pair matching, Pair-wise geometric constraint, Matching result checking BibRef

Maiwald, F.[Ferdinand], Lehmann, C.[Christoph], Lazariv, T.[Taras],
Fully Automated Pose Estimation of Historical Images in the Context of 4D Geographic Information Systems Utilizing Machine Learning Methods,
IJGI(10), No. 11, 2021, pp. xx-yy.
DOI Link 2112
BibRef

Zheng, X.W.[Xian-Wei], Yuan, Z.[Zhuang], Dong, Z.[Zhen], Dong, M.Y.[Ming-Yue], Gong, J.Y.[Jian-Ya], Xiong, H.J.[Han-Jiang],
Smoothly varying projective transformation for line segment matching,
PandRS(183), 2022, pp. 129-146.
Elsevier DOI 2201
Line segment matching, Street-level images, Motion modeling, Projective transformation BibRef

Wang, J.X.[Jing-Xue], Liu, S.[Suyan], Zhang, P.[Ping],
A New Line Matching Approach for High-Resolution Line Array Remote Sensing Images,
RS(14), No. 14, 2022, pp. xx-yy.
DOI Link 2208
BibRef

Hu, H.C.[Hao-Chen], Li, B.Y.[Bo-Yang], Yang, W.Y.[Wen-Yu], Wen, C.Y.[Chih-Yung],
A Novel Multispectral Line Segment Matching Method Based on Phase Congruency and Multiple Local Homographies,
RS(14), No. 16, 2022, pp. xx-yy.
DOI Link 2208
BibRef

Lapaine, M.[Miljenko],
Matching Standard and Secant Parallels in Cylindrical Projections,
IJGI(12), No. 2, 2023, pp. xx-yy.
DOI Link 2303
BibRef

Shen, L.[Liang], Zhu, J.H.[Jia-Hua], Xin, Q.[Qin], Huang, X.T.[Xiao-Tao], Jin, T.[Tian],
Robust line segment mismatch removal using point-pair representation and Gaussian-uniform mixture formulation,
PandRS(203), 2023, pp. 314-327.
Elsevier DOI 2310
Image matching, Line segment matching, Mismatch removal, Outlier rejection, Gaussian-uniform formulation, Feature matching BibRef

Guo, H.Y.[Hao-Yu], Wei, D.[Dong], Zhang, Y.J.[Yong-Jun], Wan, Y.[Yi], Zheng, Z.[Zhi], Yao, Y.X.[Yong-Xiang], Liu, X.[Xinyi], Li, Z.[Zhuofan],
The One-Point-One-Line geometry for robust and efficient line segment correspondence,
PandRS(210), 2024, pp. 80-96.
Elsevier DOI Code:
WWW Link. 2404
Line matching, Epipolar geometry, Point orientation, One-point-one-line geometry, 3D reconstruction BibRef

Liu, Z.Y.[Zhi-Yu], Zhong, B.J.[Bao-Jiang],
DALSM: A Direction-Aware Line Segment Matching Method,
ICIP24(3484-3490)
IEEE DOI 2411
Image segmentation, Matched filters, Accuracy, Benchmark testing, Line segment matching, line similarity, mismatch removal, gradient alignment BibRef

Pautrat, R.[Rémi], Suárez, I.[Iago], Yu, Y.F.[Yi-Fan], Pollefeys, M.[Marc], Larsson, V.[Viktor],
GlueStick: Robust Image Matching by Sticking Points and Lines Together,
ICCV23(9672-9682)
IEEE DOI Code:
WWW Link. 2401
BibRef