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Reading List:
Basri,
R., and Ullman,
S.,
The Alignment of Objects with Smooth Surfaces,
CVGIP(57),
No. 3, May 1993, pp. 331-345. BibRef9305
Earlier: ICCV88(482-488).
BibRef
And: MIT
AI Memo-1060, July 1988. Matching,
Alignment. Recognition by alignment of the 3-D surface contour
with 3 views of the object (30deg apart). Recognition and alignment works
for views between the model views.
BibRef
Basri,
R.[Ronen],
The Alignment of Objects with Smooth Surfaces:
Error Analysis of the Curvature Method,
CVPR92(341-346).
BibRef9200
And: MIT
AIMemo 1332, November 1991. Postscript
Version. BibRef
Ullman,
S.,
An Approach to Object Recogniton: Aligning Pictorial
Descriptions,
Cognition(32),
1989, pp. 193-254. BibRef8900
And: MIT
AI Memo-931, December 1986. BibRef
Shoham,
D., Ullman,
S.,
Aligning a Model to an Image Using Minimal Information,
ICCV88(259-263).
BibRef8800
Ullman,
S., and Basri,
R.,
Recognition by Linear Combinations of Models,
PAMI(13),
No. 10, October 1991, pp. 992-1005. BibRef9110
And: MIT
AI Memo-1052, August 1989. Recognize from different viewpoint given
2 images. Postscript
Version. BibRef
Basri,
R., Rivlin,
E.,
Localization and Homing Using Combinations of
Model Views,
AI(78),
No. 1-2, October 1995, pp. 327-354. BibRef9510
Earlier:
Localization Using Combinations of Model Views,
ICCV93(226-230).
BibRef
And:
Localization and Positioning Using Combinations
of Model Views,
DARPA93(377-386).
BibRef
And: MIT
AIM-1376, July 1992. Postscript
Version. Positioning and pose estimation. Represent the scene as a
series of 2-D views and predict the apperaence of novel views by linear
combinations of the model views.
BibRef
Basri,
R.,
Recognition by Prototypes,
IJCV(19),
No. 2, August 1996, pp. 147-167. 9609BibRef
Earlier: CVPR93(161-167).
BibRef
And: MIT
AIMemo 1391, December 1992. Postscript
Version. Matching by aligning the contour maps.
BibRef
Kriegman,
D.J., and Ponce,
J.,
On Recognizing and Positioning Curved 3-D Objects
from Image Contours,
PAMI(12),
No. 12, December 1990, pp. 1127-1137. BibRef9012
Earlier: A2, A1: DARPA89(461-470).
BibRef And: 3DWS89(61-67). Matching,
Contours. The projections of surface discontinuities and occluding
contours are used to determine the position and orientation. This is done
by finding the best fit between the theoretical contour and the observed
data.
BibRef
Chen,
J.L.[Jin-Long], Stockman,
G.C.[George C.],
3D Free-Form Object Recognition Using Indexing
by Contour Features,
CVIU(71),
No. 3, September 1998, pp. 334-355. BibRef9809
Earlier:
Indexing to 3D Model Aspects using 2D Contour
Features,
CVPR96(913-920).
BibRef
Fairney,
P.T., Fairney,
D.P.,
3-D Object Recognition and Orientation from Single
Noisy 2-D Images,
PRL(17),
No. 7, June 10 1996, pp. 785-793. 9607BibRef
Sheu,
R.D., Bond,
A.H.,
A Generalized Method for 3D Object Location from
Single 2D Images,
PR(25),
1992, pp. 771-786.
BibRef9200
Or,
S.H., Luk,
W.S., Wong,
K.H., King,
I.,
An Efficient Iterative Pose Estimation Algorithm,
IVC(16),
No. 5, April 27 1998, pp. 353-362. 9805BibRef
Haralick,
R.M., Joo,
H., Lee,
C.N., Zhuang,
X., Vaidya,
V.G., and Kim,
M.B.,
Pose Estimation from Corresponding Point Data,
SMC(19),
No. 6, November/December 1989, pp. 1426-1446. BibRef8911
Earlier: Without A2: CVWS87(258-263). Pose
estimation, evaluation. Closed form solutions for 2-D to 2-D and
3-D to 3-D pose estimations. For perspective 2-D to 3-D, a convergent iterative
solution is given, for 2-D perspective to 2-D perspective, a linear solution
is given. This is also an argument for error analysis and error propagation
analysis.
BibRef
Haralick,
R.M., Joo,
H.,
2D-3D Pose Estimation,
ICPR88(385-391).
BibRef8800
Oberkampf,
D., DeMenthon,
D.F., and Davis,
L.S.,
Iterative Pose Estimation Using Coplanar Feature
Points,
CVIU(63),
No. 3, May 1996, pp. 495-511. 9606BibRef
Earlier:
Iterative Pose Estimation Using Coplanar Points,
CVPR93(626-627).
BibRef
Wlczek,
P., Maccato,
A., DeFigueiredo,
R.J.P.,
Pose Estimation Of 3-Dimensional Objects From
Single Camera Images,
DSP(5),
No. 3, July 1995, pp. 176-183.
BibRef9507