12.2 Matching Linear Features

Chapter Contents (Back)
Matching, Lines.

12.2.1 2-D Lines with 2-D Structure

Chapter Contents (Back)
Matching, Lines. Line Matching. 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.
WWW Link. 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.
WWW Link. 0309
A sequence of turning and end points. See also New Concepts for Three-Dimensional Shape Analysis. BibRef

Keegan, J.F., Lesk, A.M.,
How Can You Tell if Two Line Drawings Are the Same?,
CGIP(6), No. 1, February 1977, pp. 90-92.
WWW Link. 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.
WWW Link. 0309

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
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.
WWW Link. 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,
WWW Link. BibRef 9200 USC Computer Vision BibRef

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

Matsuyama, T., Arita, H., and Nagao, M.,
Structural Matching of Line Drawings Using the Geometric Relationship between Line Segments,
CVGIP(27), No. 2, August 1984, pp. 177-194. (Kyoto)
WWW Link. 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., and Mutch, K.M.,
Matching Straight Lines,
CVGIP(43), No. 3, September 1988, pp. 386-408.
WWW Link. 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.
WWW Link. 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
An Improved Version of the Chamfer Matching Algorithm,
ICPR84(1175-1177). BibRef
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,
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.,
Cooperative Strategy for Matching Multilevel Edge Primitives,
IVC(13), No. 2, March 1995, pp. 89-99.
WWW Link. 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
Hybrid Weak-Perspective and Full-Perspective Matching,
PS File. BibRef
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.
PS File. 9708

Beveridge, J.R.[J. Ross], Graves, C.R.[Christopher R.], Steinborn, J.[Jim],
Comparing Random-Starts Local Search with Key-Feature Matching,
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,
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,
IEEE Top Reference. 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
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.
PS File. 9703

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

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).
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,
PS File. BibRef 9600

Lee, C.H., Joshi, A.,
On Correspondence, Line Tokens And Missing Tokens,
PR(28), No. 11, November 1995, pp. 1751-1764.
WWW Link. 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.
WWW Link. 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

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

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.
WWW Link. 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.
WWW Link. 0506

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.
WWW Link. 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.
Match edges in imprecise graph to precise edges in the image. 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).

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

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

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
Line matching leveraged by point correspondences,
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

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
A Generic Method of Line Matching for Central Imaging Systems under Short-Baseline Motion,
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

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
Line Matching Using Appearance Similarities and Geometric Constraints,
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.
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

Jia, Q.[Qi], Gao, X.[Xinkai], Fan, X.[Xin], Luo, Z.X.[Zhong-Xuan], Li, H.[Haojie], Chen, Z.[Ziyao],
Novel Coplanar Line-Points Invariants for Robust Line Matching Across Views,
ECCV16(VIII: 599-611).
Springer DOI 1611

Wang, J.X.[Jing-Xue], Wang, W.[Weixi], Li, X.M.[Xiao-Ming], Cao, Z.Y.[Zhen-Yu], Zhu, H.[Hong], Li, M.[Miao], He, B.[Biao], Zhao, Z.G.[Zhi-Gang],
Line Matching Algorithm for Aerial Image Combining image and object space similarity constraints,
ISPRS16(B3: 783-788).
DOI Link 1610

Li, K.[Kai], Yao, J.[Jian], Lu, M.S.[Meng-Sheng], Heng, Y.[Yuan], Wu, T.[Teng], Li, Y.X.[Yin-Xuan],
Line segment matching: A benchmark,
Benchmark testing BibRef

Imperoli, M.[Marco], Pretto, A.[Alberto],
D2CO: Fast and Robust Registration of 3D Textureless Objects Using the Directional Chamfer Distance,
Springer DOI 1507
Direct Directional Chamfer Optimization: D2CO BibRef

Li, K.[Kai], Yao, J.[Jian], Lu, X.[Xiaohu],
Robust Line Matching Based on Ray-Point-Ray Structure Descriptor,
Springer DOI 1504

Ly, D.S.[Dieu Sang], Demonceaux, C.[Cedric], Seulin, R.[Ralph], Fougerolle, Y.[Yohan],
Scale invariant line matching on the sphere,
Line matching;hybrid cameras;sphere BibRef

Liu, Z.[Zhe], Marlet, R.[Renaud],
Virtual Line Descriptor and Semi-Local Graph Matching Method for Reliable Feature Correspondence,
DOI Link 1301
See also Indoor Calibration Using Segment Chains. BibRef

Wang, W., Lou, A., Wang, J.,
The Research of Line Matching Algorithm Under The Improved Homograph Matrix Constraint Condition,
DOI Link 1209

Dalyot, S., Dahinden, T., Dahinden, M.J., Boljen, J., Sester, M.,
Geometrical Adjustment Towards The Alignment of Vector Databases,
AnnalsPRS(I-4), No. 2012, pp. 13-18.
HTML Version. 1209

Gherabi, N.[Noreddine], Bahaj, M.[Mohamed],
Outline Matching of the 2D Shapes Using Extracting XML Data,
Springer DOI 1208

Ma, T.Y.[Tian-Yang], Yang, X.W.[Xing-Wei], Latecki, L.J.[Longin Jan],
Boosting Chamfer Matching by Learning Chamfer Distance Normalization,
ECCV10(V: 450-463).
Springer DOI 1009

Liu, M.Y.[Ming-Yu], Tuzel, O.[Oncel], Veeraraghavan, A.[Ashok], Chellappa, R.[Rama],
Fast directional chamfer matching,
Chamfer Matching. BibRef

Kim, T.M.[Tae-Min], Woo, J.W.[Jih-Wan], Kweon, I.S.[In So],
Probabilistic matching of lines for their homography,
Earlier: A2, A3, A1:
Probabilistic matching of line segments for their homography,

Cho, T.H.[Tai-Hoon],
Improved Chamfer Matching Using Interpolated Chamfer Distance and Subpixel Search,
Springer DOI 0706

Yu, Z.W.[Zhan-Wu], Prinet, V., Pan, C.H.[Chun-Hong],
A novel two-steps strategy for automatic GIS-image registration,
ICIP04(III: 1711-1714).

Chan, H.B., Hung, Y.S.,
Matching patterns of line segments by eigenvector decomposition,
IEEE Top Reference. 0208

Kawaguchi, T.[Tsuyoshi], Sinozaki, T.[Takayuki], Nagata, R.I.[Ryo-Ichi],
Detection of Target Models in 2D Images by Line-Based Matching and a Genetic Algorithm,
IEEE DOI BibRef 9900

Laumy, M., Dhome, M., La Preste, J.T.,
Segments Matching: Comparison Between a Neural Approach and a Classical Optimization Way,
ICPR96(IV: 261-265).
(Univ. Blaise Pascal, F) BibRef

Sugiyama, T., Abe, K.,
Edge Feature Analysis by a Vectorized Feature Extractor and in Multiple Edges,
ICPR96(II: 280-284).
(Shizuoka Univ., J) BibRef

Marchand-Maillet, S., Sharaiha, Y.M.,
A Minimum Spanning Tree Approach to Line Image Analysis,
ICPR96(II: 225-230).
(Imperial College of Science, Technology and Medicine, UK) BibRef

de Knecht, J., Schutte, K.,
Finding map correspondence using geometric models,
ICPR96(II: 755-759).
(Delft Univ. of Technology, NL) BibRef

Cross, A.D.J., Hancock, E.R.,
Relational Matching with Stochastic Optimisation,
IEEE Top Reference. Relaxation. University of York. Graph based matching for line drawings (map) with an image. BibRef 9500

Cross, A.D.J., Hancock, E.R.,
Holistic matching,
ECCV98(II: 140).
Springer DOI BibRef 9800

Finch, A.M., Hancock, E.R., Wilson, R.C.,
Relational Matching With Mean Field Annealing,
ICPR96(II: 359-363).
(Univ. of York, UK) BibRef

Heller, A.J., and Stenstrom, J.R.,
Verification of Recognition and Alignment Hypotheses by Means of Edge Verification Statistics,
DARPA89(957-966). Line Segment based matching to verify recognition results. The line must be in the right area. BibRef 8900

Poggio, T.[Tomaso], Librande, S.E.[Stephen E.],
Computer method and apparatus for matching between line drawings,
US_Patent5,325,475, Jun 28, 1994
WWW Link. BibRef 9406

Jones, M.J.[Michael J.], Poggio, T.[Tomaso],
Model-Based Matching by Linear Combinations of Prototypes,
DARPA97(1357-1366). BibRef 9700

Jones, M.J.[Michael J.], Poggio, T.[Tomaso],
Model-Based Matching of Line Drawings by Linear Combinations of Prototypes,
IEEE DOI BibRef 9500
And: MIT AI Memo-1559, December 1995. BibRef
And: Update: MIT AI Memo-1583.
WWW Link. And the updated version:
WWW Link. Basic line/contour match with prototypes. BibRef

Jiang, X.Y., Meier, U., Bunke, H.,
Scale-Invariant Polyhedral Object Recognition Using Fragmentary Edge Segments,
IEEE DOI BibRef 9400

Meer, P., Weiss, I.,
Point/Line Correspondence under 2D Projective Transformation,
IEEE DOI BibRef 9200

Laganière, R.[Robert], Mitiche, A.[Amar],
A 3D interpretation system based on consistent labeling of a set of propositions. Application to the interpretation of straight line correspondences,
Springer DOI 9004

Nakamura, Y., Nagao, M.,
Recognition of Overlapping 2-D Objects by Local Feature Construction Method,
ICPR88(II: 1046-1048).
IEEE DOI BibRef 8800

Pan, F., Gu, W.K., Jing, R.J.,
A Back Tracking Algorithm for Matching Two Line Drawings of a 3-D Moving Object,
ICPR86(1096-1098). BibRef 8600

Chapter on Registration, Matching and Recognition Using Points, Lines, Regions, Areas, Surfaces continues in
2-D/2-D Lines Accumuation Techniques .

Last update:Mar 13, 2017 at 16:25:24