12.3.2.1 Partial Contour Matching, Piecewise Segments

Bhanu, B.[Bir], Ming, J.C.[John C.],
Recognition of Occluded Objects: A Cluster-Structure Algorithm,
PR(20), No. 2, 1987, pp. 199-211. BibRef 8700
Earlier:
Clustering Based Recognition of Occluded Objects,
ICPR86(732-734).
Elsevier DOI Improve on the technique of Price above by applying a clustering technique to limit the set of possible matches. BibRef

Liu, H.C., and Srinath, M.D.,
Partial Shape Classification Using Contour Matching in Distance Transformation,
PAMI(12), No. 11, November 1990, pp. 1072-1079.
IEEE DOI Chamfer matching of contours using control points (corners). (
See also Distance Transformations in Digital Images. ) BibRef 9011

Liu, H.C., Srinath, M.D.,
A String Descriptor for Matching Partial Shapes,
CVIP92(575-592). BibRef 9200

Hwang, V.,
Recognizing and Locating Partially Occluded 2-D Objects: Symbolic Clustering Method,
SMC(19), No. 6, Nov/Dec 1989, pp. 1644-1656. BibRef 8911
Earlier:
Recognition of Two-Dimensional Objects Using Hypothesis Integration Techniques,
CVWS87(106-111). Recognize Two-Dimensional Objects. This is similar to other methods. BibRef

Bhanu, B.[Bir], and Faugeras, O.D.[Olivier D.],
Shape Matching of Two-Dimensional Objects,
PAMI(6), No. 2, March 1984, pp. 137-155. BibRef 8403
Earlier: A1 Only:
Recognition of Occluded Objects,
IJCAI83(1136-1138). BibRef
Earlier: A1, A2:
Recognition of Occluded Two Dimensional Objects,
SCIA81(72-77). Recognize Two-Dimensional Objects. Relaxation. Segment matching and relaxation using occluded objects from his thesis, matching the occluded parts using segments as the basis. BibRef

Horaud, R., and Skordas, T.,
Model-Based Strategy Planning for Recognizing Partially Occluded Parts,
Computer(20), No. 8, August 1987, pp. 58-65. BibRef 8708
Earlier: A2, A1:
Planning A Strategy for Recognizing Partially Occluded Parts,
ICPR86(1080-1083). Recognize Two-Dimensional Objects. A search strategy based on what to look for is generated based on the distinguishing features of the given object. The system determines the correct orientation for the single type of part (top and bottom may be different). BibRef

Rutkowski, W.S.[Wallace S.],
Shape Completion,
CGIP(9), No. 1, 1979, pp. 89-101.
Elsevier DOI BibRef 7900

Rutkowski, W.S.[Wallace S.],
Recognition of Occluded Shapes Using Relaxation,
CGIP(19), No. 2, June 1982, pp. 111-128.
Elsevier DOI Recognize Two-Dimensional Objects. Relaxation. The description is based on selecting high curvature points and extending to sample points a fixed distance on either side. If necessary straight (or slightly curved) segments are used to connect these end points. Because feature values are used in the relaxation step no initial assignments are made. BibRef 8206

Koch, M.W., and Kashyap, R.L.,
Using Polygons to Recognize and Locate Partially Occluded Objects,
PAMI(9), No. 4, July 1987, pp. 483-494. BibRef 8707
Earlier: A2, A1:
Computer Vision Algorithms Used in Recognition of Occluded Objects,
CAIA84(150-155). Recognize Two-Dimensional Objects. This one hurts. Polygon means linear segment representation of the boundary. It uses clusters of matches to find the best rotation and translation for the match. It is too much of a copy for me. BibRef

Ansari, N.[Nirwan], Delp, E.J.,
Partial Shape Recognition: A Landmark-Based Approach,
PAMI(12), No. 5, May 1990, pp. 470-483.
IEEE DOI BibRef 9005
Earlier:
A Note on Two-Dimensional Landmark-Based Object Recognition,
CVPR86(622-624). Shape matching based on landmarks (important features) using a dynamic programming approach. BibRef

Ansari, N.[Nirwan],
Shape Recognition: A Landmark-Based Approach,
PurdueTR-EE-88-31, July 1988, BibRef 8807 Ph.D.Thesis (EE). The thesis form of the above paper. BibRef

Ansari, N.[Nirwan], Li, K.W.[Kuo-Wei],
Landmark-Based Shape Recognition by a Modified Hopfield Neural Network,
PR(26), No. 4, April 1993, pp. 531-542.
Elsevier DOI Uses a three point based shape measure and major curvature points on the contour to match rotated and scaled and partially occluded objects to models. Requires 4 landmark points (corners) to appear in the scene. BibRef 9304

Ullmann, J.R.[Julian R.],
Analysis of 2-D Occlusion by Subtracting Out,
PAMI(14), No. 4, April 1992, pp. 485-489.
IEEE DOI Matching and analysis of occlusions. One-D occlusion:
See also Investigation of Occlusion in One Dimension, An. BibRef 9204

Ullmann, J.R.[Julian R.],
Edge Replacement in the Recognition of Occluded Objects,
PR(26), No. 12, December 1993, pp. 1771-1784.
Elsevier DOI Hough related. BibRef 9312

Tsai, D.M., Tsai, R.Y.,
Use Neural Networks to Determine Matching Order for Recognizing Overlapping Objects,
PRL(17), No. 10, September 2 1996, pp. 1077-1088. Neural Networks. Hough Transform. BibRef 9609

Han, M.H.[Min-Hong], Jang, D.S.[Dong-Sig],
The Use of Maximum Curvature Points for the Recognition of Partially Occluded Objects,
PR(23), No. 1-2, 1990, pp. 21-33.
Elsevier DOI partially occluded, maximal cliques in graph. BibRef 9000

Salari, E., Balaji, S.,
Recognition of Partially Occluded Objects Using B-Spline Representation,
PR(24), No. 7, 1991, pp. 653-660.
Elsevier DOI B-spline of boundary. BibRef 9100

Pikaz, A.[Arie], Dinstein, I.[Its'hak], Pikaz, A., Dinstein, I.H.,
Matching of Partially Occluded Planar Curves,
PR(28), No. 2, February 1995, pp. 199-209.
Elsevier DOI total curvature of neighborhoods, lengts of matched curve segments. BibRef 9502

Dinstein, I.[Its'hak], Silberberg, T.,
Shape Discrimination with Walsh Descriptors,
ICPR80(1055-1061). BibRef 8000

Kim, J.H.[Jung H.], Yoon, S.H.[Sung H.], Sohn, K.H.[Kwang H.],
A Robust Boundary-Based Object Recognition in Occlusion Environment by Hybrid Hopfield Neural Networks,
PR(29), No. 12, December 1996, pp. 2047-2060.
Elsevier DOI 9701
Hopfield Networks. BibRef

Illing, D.P., Fairney, P.T.,
Reconstructing Partially Occluded Object Boundaries,
PRL(12), 1991, pp. 31-38. BibRef 9100

Illing, D.P., Fairney, P.T., Wiltshire, R.J.,
3-D object recognition and orientation from both noisy and occluded 2-D data,
BMVC90(xx-yy).
PDF File. 9009
BibRef

Koch, M.W., Kashyap, R.L.,
Matching Polygon Fragments,
PRL(10), 1989, pp. 297-308. BibRef 8900

Ray, K.S., Dutta Majumder, D.,
Application of Differential Geometry to Recognize and Locate Partially Occluded Objects,
PRL(9), 1989, pp. 351-360.
See also Recognition and Positioning of Partially Occluded 3-D Objects. BibRef 8900

Ray, K.S., Majumder, D.D.,
Application of Hopfield Neural Networks and Canonical Perspectives to Recognize and Locate Partially Occluded 3-D Objects,
PRL(15), No. 8, August 1994, pp. 815-824. BibRef 9408

Chen, J.M.[Jen-Ming], Ventura, J.A.[Jose A.],
Optimization Models for Shape-Matching of Nonconvex Polygons,
PR(28), No. 6, June 1995, pp. 863-877.
Elsevier DOI Model contour with set of primitives.
See also Optimization Algorithm for Shape Analysis of Regular Polygons, An. BibRef 9506

Ventura, J.A.[Jose A.], Chen, J.M.[Jen-Ming], Ventura, J.A., Chen, J.M.,
A Structural Model For Shape-Recognition Using Neural Nets,
JIM(7), No. 1, February 1996, pp. 1-11. BibRef 9602

Gewali, L.P.[Laxmi P.],
Recognizing S-Star Polygons,
PR(28), No. 7, July 1995, pp. 1019-1032.
Elsevier DOI staircase visibility. BibRef 9507

Tsang, P.W.M.,
A Genetic Algorithm for Aligning Object Shapes,
IVC(15), No. 11, November 1997, pp. 819-831.
Elsevier DOI 9712
Genetic. Contour alignments. BibRef

Tsang, P.W.M.,
A Genetic Algorithm for Affine Invariant Recognition of Object Shapes from Broken Boundaries,
PRL(18), No. 7, July 1997, pp. 631-639. 9711
BibRef

Tsang, P.W.M., Yu, Z.,
Genetic algorithm for model-based matching of projected images of three-dimensional objects,
VISP(150), No. 6, December 2003, pp. 351-359.
IEEE Abstract. 0402
BibRef

Park, J.S.[Jong Seung], Han, J.H.[Joon Hee],
Contour Matching: A Curvature-Based Approach,
IVC(16), No. 3, March 16 1998, pp. 181-189.
Elsevier DOI 9804
Tangential velocity from curvature and normal.
See also Estimating Optical Flow by Tracking Contours. BibRef

Han, J.H.[Joon Hee], Park, J.S.[Jong Seung],
Contour Matching Using Epipolar Geometry,
PAMI(22), No. 4, April 2000, pp. 358-370.
IEEE DOI 0006
Using the epipolar constraint in matching through a sequence helps eliminate mis-matches. BibRef

Kupeev, K.Y., Brailovsky, V.L.,
A Reinforced Random Algorithm for a Partial Contour Perceptual Similarity Problem,
PRL(19), No. 3-4, March 1998, pp. 287-297.
PS File. 9807
BibRef

Saber, E.[Eli], Xu, Y.W.[Yao-Wu], Tekalp, A.M.[A. Murat],
Partial shape recognition by sub-matrix matching for partial matching guided image labeling,
PR(38), No. 10, October 2005, pp. 1560-1573.
Elsevier DOI 0508
BibRef

Zhu, L.J.[Liang-Jia], Zhou, Z.T.[Zong-Tan], Hu, D.[Dewen],
Globally Consistent Reconstruction of Ripped-Up Documents,
PAMI(30), No. 1, January 2008, pp. 1-13.
IEEE DOI 0711
First curve matching, then disambiguate using relaxation process. BibRef

Tsang, P.W.M.[Peter W.M.], Yuen, T.Y.F.[Terry Y.F.],
Affine invariant matching of broken boundaries based on an enhanced genetic algorithm and distance transform,
IET-CV(2), No. 3, September 2008, pp. 142-149.
DOI Link 0905
BibRef

Tsang, P.W.M., Situ, W.C.,
Affine invariant matching of broken boundaries based on simple genetic algorithm and contour reconstruction,
PRL(31), No. 9, 1 July 2010, pp. 771-780.
Elsevier DOI 1004
Affine invariant matching; Fragmented contours; Contour reconstruction; Simple genetic algorithm; Migrant principle; Quality migrants BibRef

Yuen, T.Y.F.[Terry Y.F.], Tsang, P.W.M.[Peter W.M.],
Affine invariant matching of broken boundaries based on particle swarm optimization,
IVC(26), No. 9, 1 September 2008, pp. 1230-1239.
Elsevier DOI 0806
Affine invariant matching; Broken boundary; Simple genetic algorithm; Real coded genetic algorithm; Particle swarm optimization; Repeated trial BibRef

Lin, L.[Liang], Wang, X.L.[Xiao-Long], Yang, W.[Wei], Lai, J.H.[Jian-Huang],
Discriminatively Trained And-Or Graph Models for Object Shape Detection,
PAMI(37), No. 5, May 2015, pp. 959-972.
IEEE DOI 1504
BibRef
Earlier:
Learning contour-fragment-based shape model with And-Or tree representation,
CVPR12(135-142).
IEEE DOI 1208
Collaboration BibRef

Guerrero-Peña, F.A., Vasconcelos, G.C.,
Object recognition under severe occlusions with a hidden Markov model approach,
PRL(86), No. 1, 2017, pp. 68-75.
Elsevier DOI 1702
Severe occluded. Parts at high curvature points. BibRef

Yang, C.[Cong], Tiebe, O.[Oliver], Shirahama, K.[Kimiaki], Lukasik, E.[Ewa], Grzegorzek, M.[Marcin],
Evaluating contour segment descriptors,
MVA(28), No. 3-4, May 2017, pp. 373-391.
Springer DOI 1704
For partial boundary matching BibRef

Zafari, S.[Sahar], Murashkina, M.[Mariia], Eerola, T.[Tuomas], Sampo, J.[Jouni], Kälviäinen, H.[Heikki], Haario, H.[Heikki],
Resolving overlapping convex objects in silhouette images by concavity analysis and Gaussian process,
JVCIR(73), 2020, pp. 102962.
Elsevier DOI 2012
Segmentation, Overlapping objects, Convex objects, Image processing, Gaussian process, Kriging, Branch and bound BibRef

Azzopardi, G.[George], Fernández-Robles, L.[Laura], Alegre, E.[Enrique], Petkov, N.[Nicolai],
Increased generalization capability of trainable COSFIRE filters with application to machine vision,
ICPR16(3356-3361)
IEEE DOI 1705
Filters for selective for specific contour parts. Biological cells, Genetic algorithms, Magnetic heads, Milling, Optimization, Prototypes, Training BibRef

Leonard, K.[Kathryn], Morin, G.[Geraldine], Hahmann, S.[Stefanie], Carlier, A.[Axel],
A 2D shape structure for decomposition and part similarity,
ICPR16(3216-3221)
IEEE DOI 1705
Similarities between parts of same object. Databases, Geometry, Robustness, Shape, Shape measurement, Weight, measurement BibRef

Terzic, K.[Kasim], Mohammed, H.A.[Hussein Adnan], du Buf, J.M.H.,
Shape Detection with Nearest Neighbour Contour Fragments,
BMVC15(xx-yy).
DOI Link 1601
BibRef

Chang, L.[Leonardo], Arias-Estrada, M.[Miguel], Hernández-Palancar, J.[José], Sucar, L.E.[L. Enrique],
Partial Shape Matching and Retrieval under Occlusion and Noise,
CIARP14(151-158).
Springer DOI 1411
BibRef

Pepikj, B.[Bojan], Stark, M.[Michael], Gehler, P.[Peter], Schiele, B.[Bernt],
Occlusion Patterns for Object Class Detection,
CVPR13(3286-3293)
IEEE DOI 1309
DPM; object detection; occlusion. Occlusion is not noise, it is a feature. BibRef

Zhong, M.[Ming], Hou, T.B.[Ting-Bo], Hong, Q.[Qin],
A hierarchical approach to high-quality partial shape registration,
ICPR12(113-116).
WWW Link. 1302
BibRef

Hou, T.B.[Ting-Bo], Zhong, M.[Ming], Qin, H.[Hong],
Diffusion-driven high-order matching of partial deformable shapes,
ICPR12(137-140).
WWW Link. 1302
BibRef

Cao, Y.[Yu], Zhang, Z.Q.[Zhi-Qi], Czogiel, I.[Irina], Dryden, I.L.[Ian L.], Wang, S.[Song],
2D nonrigid partial shape matching using MCMC and contour subdivision,
CVPR11(2345-2352).
IEEE DOI 1106
Contours BibRef

Chen, L.B.[Long-Bin], Feris, R.S.[Rogerio S.], Turk, M.A.[Matthew A.],
Efficient partial shape matching using Smith-Waterman algorithm,
NORDIA08(1-6).
IEEE DOI 0806
Contour matching. BibRef

Salberg, A., Harbitz, A., Hanssen, A.,
Shape classification of partially occluded objects using subspace detectors,
ICIP04(III: 2095-2098).
IEEE DOI 0505
BibRef

Marcenaro, L., Gandetto, M., Regazzoni, C.S.,
Localization and classification of partially overlapped objects using self-organizing trees,
ICIP03(III: 137-140).
IEEE DOI 0312
BibRef

Kawaguchi, T.[Tsuyoshi], Nagao, M.[Makoto],
Recognition of Occluded Objects by a Genetic Algorithm,
ICPR98(Vol I: 233-237).
IEEE DOI 9808
BibRef

Vergnet, R.L., Saint-Marc, P., and Ayache, N.J.,
Robustness of Model-Based Recognition in Cluttered Images,
CVPR93(713-714).
IEEE DOI BibRef 9300

Lee, C.H., Quek, G.P.,
Partial Matching of two Dimensional Shapes Using Random Coding,
ICPR88(I: 64-68).
IEEE DOI BibRef 8800

Li, J.C., Yuan, B.Z.,
Using Stereo Vision Analysis to Recognize Partially Obscured Objects,
ICPR88(II: 758-760).
IEEE DOI BibRef 8800

Singer, P.F.[Paul Frank], and Chellappa, R.,
Machine Perception of Partially Obscured Planar Shapes,
CVPR85(497-502). BibRef 8500
Earlier:
Classification of Boundaries on the Plane Using Stochastic Models,
CVPR83(146-147). Uses a circular auto-regressive model. Not the real model for the noise that is expected. BibRef

Chapter on Registration, Matching and Recognition Using Points, Lines, Regions, Areas, Surfaces continues in
Contours Through a Sequence .