Prazdny, K.,
On the Information in Optical Flows,
CVGIP(22), No. 2, May 1983, pp. 239-259.
Elsevier DOI
Optical Flow, Evaluation. An evaluation of what can be determined from optical flow. Relative
depth and local surface orientation is possible, but ego motion or
object motion relative to the observer is not directly possible.
BibRef
8305
Williams, T.D.,
Depth from Camera Motion in a Real World Scene,
PAMI(2), No. 6, November 1980, pp. 511-515.
BibRef
8011
And:
Ph.D.Thesis (CS).
Derivation of distance measures for surfaces from optical flow,
restricted to horizontal or vertical surfaces (from static
segmentations). Predicts the image then refines the scene
model.
BibRef
Clocksin, W.F.,
Perception of Surface Slant and Edge Labels from Optical Flow:
A Computational Approach,
Perception(9), 1980, pp. 253-269.
Compute the slant of the surface from the optical flow. The
analysis is for observer translation.
BibRef
8000
Sugihara, K.[Kokichi],
Sugie, N.[Noboru],
Recovery of Rigid Structure from Orthographically Projected
Optical Flow,
CVGIP(27), No. 3, September 1984, pp. 309-320.
Elsevier DOI Velocity field is ambiguous for 3D interpretation.
BibRef
8409
Waxman, A.M., and
Ullman, S.,
Surface Structure and Three-Dimensional Motion from
Image Flow Kinematics,
IJRR(4), No. 3, 1985, pp. 72-94.
BibRef
8500
Earlier:
Surface Structure and 3-D Motion from Image Flow: A Kinematic Analysis,
MarylandCAR-TR-24, October 1983.
BibRef
Waxman, A.M.[Allen M.], (UMd),
Kinematics of Image Flows,
DARPA83(175-181).
Generating observer motion from the optic flow pattern.
BibRef
8300
Waxman, A.M.,
Kamgar-Parsi, B., and
Subbarao, M.,
Closed-Form Solutions to Image Flow Equations for
3D Structure and Motion,
IJCV(1), No. 3, October 1987, pp. 239-258.
Springer DOI
BibRef
8710
Earlier:
ICCV87(12-24).
Some extensions of the next paper for curved surface patches.
BibRef
Subbarao, M.[Muralidhara],
Waxman, A.M.,
Closed Form Solutions to Image Flow Equations for
Planar Surfaces in Motion,
CVGIP(36), No. 2/3, November/December 1986, pp. 208-228.
Elsevier DOI
BibRef
8611
Earlier:
On the Uniqueness of Image Flow Solutions for Planar Surfaces in Motion,
CVWS85(129-140).
BibRef
And:
MarylandCAR-TR-114, April 1985.
Even more equations, the titles tell it all.
BibRef
Verri, A., and
Poggio, T.A.,
Motion Field and Optical Flow: Qualitative Properties,
PAMI(11), No. 5, May 1989, pp. 490-498.
IEEE DOI
BibRef
8905
Earlier:
Qualitative Information in the Optical Flow,
DARPA87(825-834). Or:
BibRef
Against Quantitative Optical Flow,
ICCV87(171-180).
BibRef
Earlier:
Motion Field and Optical Flow: Differences and Qualitative Properties,
MIT AI Memo-917, December 1986.
Optical flow is not directly the same as the 3D velocity field.
This derives several properties of the motion field that give
information about the 3-D flow and 3-D structure. Good theory.
BibRef
Kanatani, K.I.[Ken-Ichi],
3-D Interpretation of Optical-Flow by Renormalization,
IJCV(11), No. 3, December 1993, pp. 267-282.
Springer DOI
BibRef
9312
Kanatani, K.I.[Ken-Ichi],
Structure and Motion from Optical Flow under Perspective Projection,
CVGIP(38), No. 2, May 1987, pp. 122-146.
Elsevier DOI Explicit form of the surface from the parameters of the flow. The
same kind of paper as the 1986 on on orthographic projections.
BibRef
8705
Kanatani, K.I.[Ken-Ichi],
Structure and Motion from Optical Flow under Orthographic Projection,
CVGIP(35), No. 2, August 1986, pp. 181-199.
Elsevier DOI Gunma U. Japan, Then at UMd.
From the flow divide the image into planar regions and determine
their structure and motion. Spurious solutions caused by more than
1 region from the same object. Analytic solutions, no real
results.
See also Tracing Planar Surface Motion from a Projection without Knowing the Correspondence.
BibRef
8608
Lippert, T.M.[Thomas M.],
Single sensor three dimensional imaging,
US_Patent4,754,327, Jun 28, 1988
WWW Link. Motion parallax.
BibRef
8806
Mitiche, A.[Amar],
Three-Dimensional Space from Optical Flow Correspondence,
CVGIP(42), No. 3, June 1988, pp. 306-317.
Elsevier DOI
BibRef
8806
Earlier:
Interpretation of Optical Flow Correspondence,
ICPR88(II: 1097-1099).
IEEE DOI Given the optical flow, compute the relative displacement of the
view points and the position and motion of the points in space. It
uses both OF methods and feature point methods.
See also On Kineopsis and Computation of Structure and Motion.
BibRef
Mitiche, A.,
Computation of Optical Flow and Rigid Motion,
CVWS84(63-71).
Gradient based approach to optical flow.
BibRef
8400
Mitiche, A.,
Zhuang, X., and
Haralick, R.M.,
Interpretation of Optical Flow by Rotational Decoupling,
CVWS87(195-200).
This tries to relate three different methods by considering optical
flow after removing the rotational component and the standard
thing of recovery of motion and structure from optical flow.
BibRef
8700
Jiang, F.[Fan],
Weymouth, T.E.[Terry E.],
Depth from Relative Normal Flows,
PR(23), No. 9, 1990, pp. 1011-1022.
Elsevier DOI
BibRef
9000
Earlier:
Depth from Dynamic Stereo Images,
CVPR89(250-255).
IEEE DOI Given 2 known camera and a sequence
find the depth. Given a lot of information, simplify the problem.
BibRef
de Micheli, E.,
Giachero, F.,
Motion and Structure from One Dimensional Optical Flow,
CVPR94(962-965).
IEEE DOI
BibRef
9400
Tistarelli, M.[Massimo],
Sandini, G.[Giulio],
Dynamic Aspects in Active Vision,
CVGIP(56), No. 1, July 1992, pp. 108-129.
Elsevier DOI
BibRef
9207
Earlier:
Properties from the motion.
See also Active Dynamic Stereo Vision.
BibRef
Tistarelli, M.[Massimo],
Sandini, G.[Giulio],
Estimation of Depth from Motion Using an Anthropomorphic Visual Sensor,
IVC(8), No. 4, December 1990, pp. 271-278.
BibRef
9012
Earlier:
On the Estimation of Depth from Motion Using an Anthropomorphic
Visual Sensor,
ECCV90(209-225).
Springer DOI
9004
Active Vision.
Depth from Motion. <
Log-Polar Sensor.
BibRef
Barron, J.L.,
Jepson, A.D., and
Tsotsos, J.K.,
The Feasibility of Motion and Structure from Noisy Time-Varying
Image Velocity Information,
IJCV(5), No. 3, December 1990, pp. 239-270.
Springer DOI
BibRef
9012
Earlier:
The Feasibility Of Motion And Structure Computations,
ICCV88(651-657).
IEEE DOI
BibRef
Earlier:
The Sensitivity of Motion and Structure Computations,
AAAI-87(700-705).
Sensitivity given flow fields, moving observer and
stationary environment.
BibRef
Barron, J.L.,
Jepson, A.D., and
Tsotsos, J.K.,
Determination of Egomotion and Environmental Layout from Noisy
Time-Varying Velocity in Binocular Image Sequences,
IJCAI87(822-825).
BibRef
8700
And:
Determination of Egomotion and Environmental Layout from Noisy
Time-Varying Velocity in Monocular Image Sequences,
CIAP87(XX-YY).
BibRef
MacLean, W.J.[W. James],
Jepson, A.D.[Allan D.],
Frecker, R.C.[Richard C.],
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BMVC94(xx).
PDF File.
9409
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Barron, J.L.,
Motion and Structure in rigid Multi-Surfaced Stationary Environments
Using Time-Varying Image Velocity: Linear Solutions,
VF91(39-46).
Given Optical flow, generate the observer R and T.
BibRef
9100
Barron, J.L.,
Computing Motion and Structure from Noisy,
Time-Varying Image Velocity Information,
RBCV-TR-88-24, Toronto, August 1989,
BibRef
8908
Ph.D.Thesis (CS).
Survey, Motion.
Motion, Survey. It appears that all you would want to know about structure
given optical flow is given here.
BibRef
Shu, C.Q.,
Shi, Y.Q.,
On Unified Optical Flow Field,
PR(24), No. 6, 1991, pp. 579-586.
Elsevier DOI Unified OFF.
See also Unified Optical-Flow Field Approach To Motion Analysis from a Sequence of Stereo Images.
BibRef
9100
Shu, C.Q.,
Shi, Y.Q.,
Direct Recovering of Nth Order Surface Structure Using
Unified Optical Flow Field,
PR(26), No. 8, August 1993, pp. 1137-1148.
Elsevier DOI
BibRef
9308
Shi, Y.Q.,
Shu, C.Q.,
Pan, J.N.,
Unified Optical-Flow Field Approach To Motion Analysis
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PR(27), No. 12, December 1994, pp. 1577-1590.
Elsevier DOI
See also On Unified Optical Flow Field.
BibRef
9412
Pan, J.N.,
Shi, Y.Q.,
Shu, C.Q.,
A Kalman filter in motion analysis from stereo image sequences,
ICIP94(III: 63-67).
IEEE DOI
9411
BibRef
Simpson, W.A.,
Optic Flow and Depth Perception,
SV(7), 1993, pp. 35-75.
BibRef
9300
Simpson, W.A.,
The Cross-ratio and the Perception of Motion and Structure,
Motion83(125-129).
(Toronto),
Based on some ideas from Gibson.
BibRef
8300
Gupta, N.C.[Naresh C.],
Kanal, L.N.[Laveen N.],
3-D Motion Estimation from Motion Field,
AI(78), No. 1-2, October 1995, pp. 45-86.
Elsevier DOI
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9510
Gupta, N.C.[Naresh C.],
Kanal, L.N.[Laveen N.],
Gradient Based Image Motion Estimation Without Computing Gradients,
IJCV(22), No. 1, February 1997, pp. 81-101.
DOI Link
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9702
Nagle, M.G.,
Srinivasan, M.V.,
Structure-from-Motion:
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JOSA-A(13), No. 1, January 1996, pp. 25-34.
BibRef
9601
Mitiche, A.,
Computational Analysis of Visual Motion,
PlenumPress, New York, 1994. ISBN 0-306-44786-X.
3-D interpretation of measured motion from point correspondences,
line correspondences and optical flow, with reduced emphasis on
measuring the motion.
BibRef
9400
Mitiche, A.,
A Computational Approach to the Fusion of Stereopsis and Kineopsis,
MU88(81-99).
BibRef
8800
Earlier:
On Combining Stereopsis And Kineopsis For Space Perception,
CAIA84(156-160).
3-D motion in terms of depth, optical flow and steroscopy parameters.
All this information makes it reasonable (but harder to obtain).
See also On Kineopsis and Computation of Structure and Motion.
BibRef
Lindenbaum, M.,
Bruckstein, A.M.,
Determining Object Shape from Local Velocity Measurements,
PR(21), No. 6, 1988, pp. 591-606.
Elsevier DOI Rigid planar shapes.
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8800
Raviv, D.,
Albus, J.S.,
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SMC(22), 1992, pp. 322-327.
BibRef
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Weber, J.W.[Joseph W.],
Malik, J.[Jitendra],
Rigid-Body Segmentation and Shape-Description from
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PAMI(19), No. 2, February 1997, pp. 139-143.
IEEE DOI
9703
BibRef
Earlier:
ICCV95(251-256).
IEEE DOI Shape from optical flow. Identify and track independently moving objects
from the optical flow. Rather than discontinuities in the flow field,
use the fact that the epipolar constraint of the individual objects is
different.
BibRef
Allmen, M.C.,
Kegelmeyer, W.P.,
The Computation of Cloud-base Height from Paired Whole-Sky
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Springer DOI
Stereo, Motion.
Optical Flow. Register cloud fields from widely separated cameras. Use optical
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Lasenby, J.,
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Lasenby, A.N.,
Doran, C.J.L.,
New Geometric Methods for Computer Vision:
An Application to Structure and Motion Estimation,
IJCV(26), No. 3, March 1998, pp. 191-213.
DOI Link
9804
BibRef
Xiong, Y.L.[Ya-Lin],
Shafer, S.A.[Steven A.],
Dense Structure from a Dense Optical Flow Sequence,
CVIU(69), No. 2, February 1998, pp. 222-245.
DOI Link
BibRef
9802
Earlier:
SCV95(1-6).
IEEE DOI
BibRef
Earlier:
CMU-RI-TR-95-11, March 1995.
PS File. Carnegie Mellon University.
Reduced complexity to O(N). Only needs 2 frame flow, not long sequence
matching.
BibRef
Xiong, Y.L.[Ya-Lin],
Shafer, S.A.[Steven A.],
Hypergeometric Filters for Optical Flow and Affine Matching,
IJCV(24), No. 2, September 1997, pp. 163-177.
DOI Link
9710
BibRef
Earlier:
ICCV95(771-776).
IEEE DOI
Award, Marr Prize, HM. Formulate these a problems of extracting one of more parameters of a
transformation between images.
BibRef
Xiong, Y.L.[Ya-Lin],
Shafer, S.A.[Steven A.],
Moment and Hypergeometric Filters for
High Precision Computation of Focus, Stereo and Optical Flow,
IJCV(22), No. 1, February 1997, pp. 25-59.
DOI Link
BibRef
9702
Earlier:
CMU-RI-TR-94-28, September 1994.
PS File.
BibRef
Xiong, Y.L.[Ya-Lin],
High Precision Image Matching and Shape Recovery,
CMU-RI-TR-95-35, September 1995.
BibRef
9509
Ph.D.Thesis.
GAbor filters, moment filters, hypergeometric filters. EKF based
structrue from motion.
BibRef
Xiong, Y.L.[Ya-Lin],
Shafer, S.A.[Steven A.],
Variable Window Gabor Filters and Their Use in Focus and Correspondence,
CVPR94(668-671).
IEEE DOI
BibRef
9400
And:
CMU-RI-TR-94-06, March 1994.
PS File.
BibRef
And:
Recursive Filters For High Precision Computation of Focus, Stereo
and Optical Flow,
ARPA94(II:1637-1647).
BibRef
Earlier:
Depth from Focusing and Defocusing,
CVPR93(68-73).
IEEE DOI
BibRef
And:
DARPA93(967-).
BibRef
And:
CMU-RI-TR-93-07, March 1993.
PS File.
BibRef
Deshpande, S.G.,
Chaudhuri, S.,
Recursive Estimation of Illuminant Motion from Flow Field
and Simultaneous Recovery of Shape,
CVIU(72), No. 1, October 1998, pp. 10-20.
DOI Link
BibRef
9810
Earlier:
Recursive estimation of illuminant motion from flow field,
ICIP96(III: 771-774).
IEEE DOI
9610
BibRef
Brodský, T.[Tomás],
Fermüller, C.[Cornelia],
Aloimonos, Y.[Yiannis],
Structure from Motion: Beyond the Epipolar Constraint,
IJCV(37), No. 3, June 2000, pp. 231-258.
DOI Link
0008
BibRef
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UMD--TR4000, April 1999.
WWW Link.
BibRef
Brodský, T.,
Fermüller, C.,
Aloimonos, Y.,
Simultaneous estimation of viewing geometry and structure,
ECCV98(I: 342).
Springer DOI
BibRef
9800
Brodský, T.[Tomás],
Fermüller, C.[Cornelia],
Aloimonos, Y.[Yiannis],
Shape from Video,
CVPR99(II: 146-151).
IEEE DOI Static scene. First get camera motion, then derive structure.
BibRef
9900
Brodský, T.[Tomás],
Fermüller, C.[Cornelia],
Aloimonos, Y.[Yiannis],
Shape for Video: Beyond the Epipolar Constraint,
DARPA98(1003-1012).
BibRef
9800
Brodský, T.[Tomás],
Fermüller, C.[Cornelia],
Aloimonos, Y.[Yiannis],
Beyond the Epipolar Constraint: Integrating 3D Motion and Structure
Estimation,
SMILE98(xx-yy).
BibRef
9800
Brodsky, T.,
Fermüller, C.,
Aloimonos, Y.,
The Information in the Direction of Image Flow,
SCV95(461-466).
IEEE DOI University of Maryland.
Instead of the full motion field, only use the direction of the flow.
BibRef
9500
Xirouhakis, Y.[Yiannis],
Delopoulos, A.[Anastasios],
Least Squares Estimation of 3D Shape and Motion of Rigid Objects from
Their Orthographic Projections,
PAMI(22), No. 4, April 2000, pp. 393-399.
IEEE DOI
0006
Orthographic projection and flow field.
BibRef
Stein, G.P.[Gideon P.],
Shashua, A.[Amnon],
Model-Based Brightness Constraints:
On Direct Estimation of Structure and Motion,
PAMI(22), No. 9, September 2000, pp. 992-1015.
IEEE DOI
0010
Extend Horn and Weldon (
See also Direct Methods for Recovering Motion. ) to 3 views
allowing solve for motion and computing dense depth map from spatio-temporal
derivatives.
See also On Degeneracy of Linear Reconstruction From Three Views: Linear Line Complex and Applications.
BibRef
Stein, G.P.[Gideon P.],
Shashua, A.,
Direct Estimation of Motion and Extended Scene Structure
from a Moving Stereo Rig,
CVPR98(211-218).
IEEE DOI
BibRef
9800
Shashua, A.[Amnon],
Wexler, Y.[Yonatan],
Q-Warping: Direct Computation of Quadratic Reference Surfaces,
PAMI(23), No. 8, August 2001, pp. 920-925.
IEEE DOI
0109
BibRef
Earlier: A2, A1:
CVPR99(I: 333-338).
IEEE DOI
BibRef
And: A2, A1:
UMD--TR3993, February 1999.
WWW Link. Analysis based on a picture wrapped around a cylinder. Then apply this
assumption to real data, residual flow is proportional to the 3D of
the surface.
BibRef
Weng, N.,
Yang, Y.H.,
Pierson, R.,
Three-dimensional surface reconstruction using optical flow for medical
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MedImg(16), No. 5, October 1997, pp. 630-641.
IEEE Top Reference.
0205
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Yang, Y.H.[Yee-Hong],
Pierson, R.,
Three dimensional segmentation of volume data,
ICIP94(III: 488-492).
IEEE DOI
9411
BibRef
Spies, H.[Hagen],
Jähne, B.[Bernd],
Barron, J.L.[John L.],
Range Flow Estimation,
CVIU(85), No. 3, March 2002, pp. 209-231.
DOI Link
0211
BibRef
Earlier:
Dense Range Flow from Depth and Intensity Data,
ICPR00(Vol I: 131-134).
IEEE DOI
0009
BibRef
Barron, J.L.[John L.],
Ngai, W.K.J.[Wang Kay Jacky],
Spies, H.[Hagen],
Quantitative Depth Recovery from Time-Varying Optical Flow in a Kalman
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WTRCV02(346-355).
0204
BibRef
Spies, H.,
Jahne, B.,
Barron, J.L.,
Regularised Range Flow,
ECCV00(II: 785-799).
Springer DOI
0003
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Spies, H.[Hagen],
Barron, J.L.[John L.],
Estimating Expansion Rates from Range Data Sequences,
VI02(339).
PDF File.
0208
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Barron, J.L.[John L.],
Spies, H.[Hagen],
The Fusion of Image and Range Flow,
WTRCV01(171).
0103
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Chellappa, R.[Rama],
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Fast two-frame multiscale dense optical flow estimation using discrete
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0308
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Liu, H.Y.[Hai-Ying],
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IP(12), No. 10, October 2003, pp. 1170-1180.
IEEE DOI
0310
BibRef
Earlier:
ICPR02(I: 291-294).
IEEE DOI
0211
BibRef
Liu, H.Y.[Hai-Ying],
Chellappa, R.,
Rosenfeld, A.,
A hierarchical approach for obtaining structure from two-frame optical
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Motion02(214-219).
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0303
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Structure from optical flow.
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PAMI(27), No. 4, April 2005, pp. 562-574.
IEEE Abstract.
0501
BibRef
Earlier:
Estimating the Structure of Textured Surfaces Using Local Affine Flow,
BMVC00(xx-yy).
PDF File.
0009
Optical flow of planar patches on surface.
BibRef
Calway, A.D.,
Kruger, S.,
Tweed, D.S.,
Motion estimation using adaptive correlation and local directional
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ICIP98(III: 614-618).
IEEE DOI
9810
BibRef
Kruger, S.,
Calway, A.D.,
Image Registration using Multiresolution Frequency Domain Correlation,
BMVC98(xx-yy).
BibRef
9800
Earlier:
A Multiresolution Frequency Domain Method for
Estimating Affine Motion Parameters,
ICIP96(I: 113-116).
IEEE DOI
BibRef
Calway, A.D.[Andrew D.],
Knutsson, H.,
Wilson, A.,
Multiresolution Estimation of 2-D Disparity Using a Frequency Domain
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BMVC92(xx-yy).
PDF File.
9209
BibRef
Tan, S.[Sovira],
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Anderson, A.[Andrew],
Johnston, A.[Alan],
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A calibration method and a quantitative analysis,
IVC(24), No. 2, 1 February 2006, pp. 153-165.
Elsevier DOI
0604
Inverse perspective mapping; Calibration methods
BibRef
Liang, X.F.[Xue-Feng],
McOwan, P.W.[Peter W.],
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1104
Process in color space, not each separately.
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sequences,
CVIU(105), No. 2, February 2007, pp. 145-165.
Elsevier DOI
0702
BibRef
Earlier:
ICCV03(1395-1402).
IEEE DOI
0311
Structure from motion; Template matching; Optical flow;
Focus of expansion; Time to collision
Construct filters to recover shape and motion.
BibRef
Lefčvre, J.[Julien],
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Optical Flow and Advection on 2-Riemannian Manifolds:
A Common Framework,
PAMI(30), No. 6, June 2008, pp. 1081-1092.
IEEE DOI
0804
Optical flow for non-planar surfaces.
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Khan, S.[Sheraz],
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PRL(32), No. 15, 1 November 2011, pp. 2047-2052.
Elsevier DOI
1112
Optical flow; Helmholtz-Hodge decomposition; Feature detection; Image
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Yuan, D.[Ding],
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Correspondence-Free Stereo Vision:
Extension from Planar Scene Case to Polyhedral Scene Case,
MVA(21), No. 4, June 2010, pp. xx-yy.
Springer DOI
1006
BibRef
Earlier:
Determining Relative Geometry of Cameras from Normal Flows,
ACCV07(II: 301-310).
Springer DOI
0711
BibRef
Earlier:
Direct Estimation of the Stereo Geometry from Monocular Normal Flows,
ISVC06(I: 303-312).
Springer DOI
0611
See also Determining Shape and Motion from Monocular Camera: A Direct Approach Using Normal Flows.
BibRef
Doerschner, K.[Katja],
Kersten, D.[Dan],
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Rapid classification of specular and diffuse reflection from image
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PR(44), No. 9, September 2011, pp. 1874-1884.
Elsevier DOI
1106
BibRef
Earlier:
Rapid Classification of Surface Reflectance from Image Velocities,
CAIP09(856-864).
Springer DOI
0909
Specular flow; Rapid surface reflectance classification; Velocity
histogram; Material perception; Spatio-temporal filtering
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Zang, D.[Di],
Doerschner, K.[Katja],
Schrater, P.R.[Paul R.],
Rapid Inference of Object Rigidity and Reflectance Using Optic Flow,
CAIP09(881-888).
Springer DOI
0909
BibRef
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Schrater, P.R.[Paul R.],
Doerschner, K.[Katja],
Object rigidity and reflectivity identification based on motion
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ICIP10(4573-4576).
IEEE DOI
1009
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1110
BibRef
Earlier:
Optical flow and depth from motion for omnidirectional images using a
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ICIP09(1469-1472).
IEEE DOI
0911
See also Plenoptic based super-resolution for omnidirectional image sequences.
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Puy, G.[Gilles],
Vandergheynst, P.[Pierre],
Fast TV-L1 optical flow for interactivity,
ICIP11(1885-1888).
IEEE DOI
1201
BibRef
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ICCV13(505-512)
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1403
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2011
Video sequences, Optical network units, Feature extraction,
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Optical imaging, Estimation, Cameras, Optical fiber networks,
Optical computing, Robot vision systems, Training,
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2403
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2404
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2004
Code, 3D.
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GCPR16(233-244).
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Estimating Vehicle Ego-Motion and Piecewise Planar Scene Structure from
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GCPR15(41-52).
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1511
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Vasudevan, A.B.,
Muralidharan, S.,
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Raman, S.,
Dynamic Scene Classification Using Spatial and Temporal Cues,
VECTaR13(803-810)
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1403
feature extraction, spatial SIFT, temporal optical flow.
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Voronov, A.,
Vatolin, D.,
Occlusion refinement for stereo video using optical flow,
IC3D12(1-8)
IEEE DOI
1503
computer graphics
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Shape from Specular Flow with Near-Field Environment Motion Field,
ISVC14(I: 367-378).
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1501
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Direct Estimation of Dense Scene Flow and Depth from a Monocular
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ISVC14(I: 107-117).
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Computing Range Flow from Multi-modal Kinect Data,
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Robust Extraction of Optic Flow Differentials for Surface
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Depth and Scene Flow from a Single Moving Camera,
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Chapter on Optical Flow Field Computations and Use continues in
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