Rodriguez, J.J., and
Aggarwal, J.K.,
Matching Aerial Images to 3-D Terrain Maps,
PAMI(12), No. 12, December 1990, pp. 1138-1149.
IEEE Abstract.
IEEE DOI
BibRef
9012
And:
Terrain Matching by Analysis of Aerial Images,
ICCV90(677-681).
IEEE DOI
BibRef
Earlier:
Navigation Using Image Sequence Analysis and 3-D Terrain Matching,
3DWS89(200-207).
Matching, Surfaces. First compute the 3-D representation of the terrain using stereo.
Then convert both the map and the scene into "Cliff" maps using edge
detection. Finally, match the contours in the edge maps to find the
location.
BibRef
Ben-Arie, J.,
The Probabilistic Peaking Effect of Viewed Angles
and Distances with Application to 3-D Object Recognition,
PAMI(12), No. 8, August 1990, pp. 760-774.
IEEE DOI
BibRef
9008
Ben-Arie, J.,
The Properties of Viewed Angles and Distances with Application to
3-D Object Recognition,
ICPR88(I: 309-312).
IEEE DOI
BibRef
8800
Bergevin, R.[Robert], and
Levine, M.D.[Martin D.],
Part Decomposition of Objects from Single View Line Drawings,
CVGIP(55), No. 1, January 1992, pp. 73-83.
Elsevier DOI Decompose line drawings into separate components. Not using line
labeling kinds of techniques.
BibRef
9201
Bergevin, R.[Robert], and
Levine, M.D.[Martin D.],
Extraction of Line Drawing Features for Object Recognition,
PR(25), No. 3, March 1992, pp. 319-334.
Elsevier DOI
BibRef
9203
Earlier:
ICPR90(I: 496-501).
IEEE DOI
9006
BibRef
Bergevin, R.[Robert], and
Levine, M.D.[Martin D.],
Generic Object Recognition:
Building and Matching Coarse Descriptions from Line Drawing,
PAMI(15), No. 1, January 1993, pp. 19-36.
IEEE DOI
BibRef
9301
Earlier:
Generic Object Recognition: Building Coarse 3D Descriptions from
Line Drawings,
3DWS89(68-74).
The paper makes strong claims regarding the type of matching
techniques and the quality. It matches line drawings to one model.
It uses recognition by components as proposed by Biederman.
BibRef
Ellis, R.E.,
Geometric Uncertainties in Polyhedral Object Recognition,
RA(7), 1991, pp. 361-371.
BibRef
9100
Ellis, R.E.,
Uncertainty Estimates for Polyhedral Object Recognition,
CRA89(348-353).
BibRef
8900
Bodington, R.M.,
Sullivan, G.D., and
Baker, K.D.,
Consistent Labelling of Image Features Using an Assumption-Based
Truth Maintenance System,
IVC(7), No. 1, February 1989, pp. 43-49.
Elsevier DOI
BibRef
8902
Grosky, W.I.[William I.],
Mehrotra, R.[Rajiv],
Index-Based Object Recognition in Pictorial Data Management,
CVGIP(52), No. 3, December 1990, pp. 416-436.
Elsevier DOI Model driven recognition.
BibRef
9012
Chou, S.L.,
Tsai, W.H.,
Line Segment Matching for 3D Computer Vision Using a
New Iteration Scheme,
MVA(6), 1993, pp. 191-205.
BibRef
9300
Mehrotra, R.,
Gary, J.E.,
Similar-Shape Retrieval in Shape Data Management,
Computer(28), No. 9, September 1995, pp. 57-62.
Database. Matching of contours to find similar shapes. Generate a feature index from
the database item and the query and match them.
BibRef
9509
Reid, I.D.[Ian D.],
Brady, J.M.[J. Michael],
Recognition of Object Classes from Range Data,
AI(78), No. 1-2, October 1995, pp. 289-326.
Elsevier DOI
BibRef
9510
Earlier:
ICCV93(302-307).
IEEE DOI Match line based models to images.
See also Recognition of Parameterized Objects from 3D Data: A Parallel Implementation.
BibRef
Reid, I.D.[Ian D.],
Brady, J.M.[J. Michael],
Model-Based Recognition and Range Imaging for a Guided Vehicle,
IVC(10), No. 3, April 1992, pp. 197-207.
Elsevier DOI
BibRef
9204
Chang, Y.L.,
Leou, J.J.,
Representation and Matching of Feature Patterns for
Robot Operation Monitoring,
RA(10), No. 4, 1995, pp. 143-151.
BibRef
9500
Olson, C.F.,
Huttenlocher, D.P.,
Automatic Target Recognition by Matching Oriented Edge Pixels,
IP(6), No. 1, January 1997, pp. 103-113.
IEEE DOI
HTML Version.
PDF File. Or:
PDF File.
9703
BibRef
Earlier:
Determining the Probability of a False Positive When Matching Chains
of Oriented Pixels,
ARPA96(1175-1180).
BibRef
Earlier:
Recognition by Matching Dense, Oriented Pixels,
SCV95(91-96).
IEEE DOI Cornell University. Used for tracking an object in the sequence.
BibRef
Olson, C.F.,
Huttenlocher, D.P.,
Doria, D.M.,
Recognition by Matching with Edge Location and Orientation,
ARPA96(1167-1174).
BibRef
9600
Cass, T.A.[Todd A.],
Polynomial-Time Geometric Matching for Object Recognition,
IJCV(21), No. 1-2, January 1997, pp. 37-61.
DOI Link
9704
BibRef
Earlier:
MIT AI-TR-1470, September 1992.
BibRef
And:
Polynomial-Time Object Recognition in the Presence of
Clutter, Occlusions, and Uncertainty,
ECCV92(834-842).
Springer DOI
BibRef
And:
DARPA92(693-704).
BibRef
And:
MIT AI Memo-1302, October 1991.
BibRef
And:
Feature Matching for Object Localization in the Presence of Uncertainty,
ICCV90(360-364).
IEEE DOI
BibRef
And:
MIT AI Memo-1133, May 1990.
Pose space search type of recognition, search the parameter space.
By using convex regions reduce it to a polynomial time algorithm that
matches the maximum number of consistent features.
BibRef
Cass, T.A.[Todd A.],
Robust Geometric Matching for 3D Object Recognition,
ICPR94(A:477-482).
IEEE DOI
BibRef
9400
Earlier:
Robust 2-D Model-Based Object Recognition,
MIT AI-TR-1132, May 1988.
WWW Link.
BibRef
And:
A Robust Parallel Implementation of 2D Model-Based Recognition,
CVPR88(879-884).
IEEE DOI
BibRef
Cass, T.A.,
Robust Affine Structure Matching for 3d Object Recognition,
PAMI(20), No. 11, November 1998, pp. 1265-1274.
IEEE DOI
BibRef
9811
And:
ECCV96(I:492-503).
Springer DOI
Indexing. Line descriptions of objects matched to the image.
BibRef
Gros, P.[Patrick],
Bournez, O.[Olivier],
Boyer, E.[Edmond],
Using Local Planar Geometric Invariants to Match and Model Images
of Line Segments,
CVIU(69), No. 2, February 1998, pp. 135-155.
DOI Link
BibRef
9802
Yi, X.L.[Xi-Lin],
Camps, O.I.[Octavia I.],
Line-Based Recognition Using a Multidimensional Hausdorff Distance,
PAMI(21), No. 9, September 1999, pp. 901-916.
IEEE DOI
BibRef
9909
Earlier:
Robust Occluding Contour Detection Using the Hausdorff Distance,
CVPR97(962-968).
IEEE DOI
9704
Recognition by 4-D Hausdorff distance. Minimize the distance betweeen
two sets of points.
BibRef
Yi, X.,
Camps, O.I.,
Line Feature-Based Recognition Using Hausdorff Distance,
SCV95(79-84).
IEEE DOI The Pennsylvania State University. Matching line features so that
rotation, scale and translation can be separated.
BibRef
9500
Chang, C.C.[Chin-Chun],
Tsai, W.H.[Wen-Hsiang],
Reliable Determination of Object Pose from Line Features by Hypothesis
Testing,
PAMI(21), No. 11, November 1999, pp. 1235-1241.
IEEE DOI
9912
Find the pose, then test whether the match is sufficient.
BibRef
Kang, D.J.[Dong-Joong],
Ha, J.E.[Jong-Eun],
Kweon, I.S.[In-So],
Fast object recognition using dynamic programming from combination of
salient line groups,
PR(36), No. 1, January 2003, pp. 79-90.
Elsevier DOI
0210
BibRef
Chung, R.[Ronald],
Wong, H.S.[Hau-San],
Polyhedral Object Localization in an Image by Referencing to a Single
Model View,
IJCV(51), No. 2, February 2003, pp. 139-163.
DOI Link
0301
BibRef
Chung, R.,
Object Locating Using a Single Model View,
SCV95(545-550).
IEEE DOI The Chinese University of Hong Kong.
Matches nodes (junctions) then arcs (using active contours to fit).
BibRef
9500
Gleeson, R.,
Grosshans, F.,
Hirsch, M.,
Williams, R.M.,
Algorithms for the Recognition of 2D Images of M Points and N Lines
in 3D,
IVC(21), No. 6, June 2003, pp. 497-504.
Elsevier DOI
0306
BibRef
Guerra, C.[Concettina],
Pascucci, V.[Valerio],
Line-based object recognition using Hausdorff distance:
From Range Images to Molecular Secondary Structures,
IVC(23), No. 4, 1 April 2005, pp. 405-415.
Elsevier DOI
0501
BibRef
Merckel, L.[Loic],
Nishida, T.[Toyoaki],
Accurate Object Recognition Using Orientation Sensor with Refinement on
the Lie Group of Spatial Rigid Motions,
IEICE(E91-D), No. 8, August 2008, pp. 2179-2188.
DOI Link
0804
3D Model using line segments.
Hypothesize and test pose.
BibRef
Tajima, J.[Johji],
Kono, H.[Hironori],
Natural Object/Artifact Image Classification Based on Line Features,
IEICE(E91-D), No. 8, August 2008, pp. 2207-2211.
DOI Link
0804
Line length ratio, line direction distribution, edge coverage.
Line length ratio performs best.
BibRef
Kamgar-Parsi, B.[Behzad],
Kamgar-Parsi, B.[Behrooz],
Matching 2D image lines to 3D models:
Two improvements and a new algorithm,
CVPR11(2425-2432).
IEEE DOI
1106
BibRef
Tateno, K.[Keisuke],
Kotake, D.[Daisuke],
Uchiyama, S.J.[Shin-Ji],
Model-Based 3D Object Tracking with Online Texture Update,
MVA09(261-).
PDF File.
0905
Matching edge points, then extended edges.
BibRef
Carmichael, O.T.,
Hebert, M.,
Object Recognition by a Cascade of Edge Probes,
BMVC02(Matching/Recognition).
0208
BibRef
Carmichael, O.T.,
Discriminative Techniques for the Recognition of Complex-Shaped Objects,
CMU-RI-TR-03-34, September, 2003.
BibRef
0309
Ph.D.Thesis.
HTML Version.
0501
BibRef
Carmichael, O.T.,
Discriminant Filters for Object Recognition,
CMU-RI-TR-02-09, March, 2002.
WWW Link.
0205
BibRef
Escobar, A.,
Laurendeau, D.[Denis],
Registration of Complex Free-form Objects from 3D Image Edge
Using the Hausdorff Distance,
MVA98(xx-yy).
BibRef
9800
Müller, W., and
Olson, J.,
Automatic Matching of 3-D Models to Imagery,
Ascona95(43-52).
Match the wireframe model of the building to the image for registration.
BibRef
9500
Hebert, M., and
Kanade, T.,
The 3D-Profile Method for Object Recognition,
CVPR85(458-463). (CMU)
Recognize Three-Dimensional Objects. From 3-D data get 3-D edges, match to 3-D models (polyhedral models).
BibRef
8500
Hoogs, A.[Anthony],
Pose Refinement Using a Parameter Hierarchy,
ARPA96(857-864).
Refine the extracted buildings (2D).
BibRef
9600
Hoogs, A.[Anthony],
Analysis of learning using segmentation models,
CAIP97(645-652).
Springer DOI
9709
BibRef
Hoogs, A.[Anthony],
Bajcsy, R.[Ruzena],
Model-Based Learning of Segmentations,
ICPR96(IV: 494-499).
IEEE DOI
9608
BibRef
Earlier:
Segmentation modeling,
CAIP95(808-813).
Springer DOI
9509
BibRef
Earlier:
Using Scene Context to Model Segmentations,
Context95(xx).
(Univ. of Pennsylvania, USA)
BibRef
Bajcsy, R.[Ruzena],
Hoogs, A.[Anthony],
Segmentation Characterization for Change Detection,
ARPA94(II:1555-1562).
Change Detection.
BibRef
9400
Bodington, R.M.,
Sullivan, G.D., and
Baker, K.D.,
Experiments on the Use of the ATMS to Lagel Features for
Object Recognition,
ECCV90(542-551).
Springer DOI
High Level Vision. Using a truth maintenance system for matching line structures.
BibRef
9000
Feng, P.,
A Face Based Algorithm for Matching Two Line Drawings of a Polyhedron,
ICPR88(II: 782-784).
IEEE DOI
BibRef
8800
Chapter on Registration, Matching and Recognition Using Points, Lines, Regions, Areas, Surfaces continues in
2-D/3-D Lines Accumuation Techniques .