17.7 Surface Reconstruction from Optical Flow

Chapter Contents (Back)
Structure from Motion. Shape from Optical Flow. Surface Reconstruction. Motion, Surface Reconstruction. See also Structure, Depth, and Shape from Motion. See also Structure from Motion - Other.

Prazdny, K.,
On the Information in Optical Flows,
CVGIP(22), No. 2, May 1983, pp. 239-259.
WWW Link. 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.
WWW Link. 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.
WWW Link. 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.
WWW Link. 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.
WWW Link. 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.
WWW Link. 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.
WWW Link. 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.
WWW Link. 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.],
Recovery of Ego-Motion and Segmentation of Independent Object Motion Using the EM Algorithm,
BMVC94(xx).
PDF File. 9409
BibRef

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.
WWW Link. 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.
WWW Link. BibRef 9308

Shi, Y.Q., Shu, C.Q., Pan, J.N.,
Unified Optical-Flow Field Approach To Motion Analysis from a Sequence of Stereo Images,
PR(27), No. 12, December 1994, pp. 1577-1590.
WWW Link. 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.
WWW Link. BibRef 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 BibRef 9702

Nagle, M.G., Srinivasan, M.V.,
Structure-from-Motion: Determining the Range and Orientation of Surfaces by Image Interpolation,
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.
WWW Link. Rigid planar shapes. BibRef 8800

Raviv, D., Albus, J.S.,
A Closed-Form Massively-Parallel Range-from-Image-Flow Algorithm,
SMC(22), 1992, pp. 322-327. BibRef 9200

Weber, J.W.[Joseph W.], Malik, J.[Jitendra],
Rigid-Body Segmentation and Shape-Description from Dense Optical-Flow Under Weak Perspective,
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 Imaging Cameras,
MVA(9), No. 4, 1997, pp. 160-165.
Springer DOI Stereo, Motion. Optical Flow. Register cloud fields from widely separated cameras. Use optical flow techniques. BibRef 9700

Lasenby, J., Fitzgerald, W.J., 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
And: 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 imaging,
MedImg(16), No. 5, October 1997, pp. 630-641.
IEEE Top Reference. 0205
BibRef

Muzzolini, R.E., 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 Filter Framework,
WTRCV02(346-355). 0204
BibRef

Spies, H., Jahne, B., Barron, J.L.,
Regularised Range Flow,
ECCV00(II: 785-799).
Springer DOI 0003
BibRef

Spies, H.[Hagen], Barron, J.L.[John L.],
Estimating Expansion Rates from Range Data Sequences,
VI02(339).
PDF File. 0208
BibRef

Barron, J.L.[John L.], Spies, H.[Hagen],
The Fusion of Image and Range Flow,
WTRCV01(171). 0103
BibRef

Liu, H.Y.[Hai-Ying], Chellappa, R.[Rama], Rosenfeld, A.[Azriel],
Fast two-frame multiscale dense optical flow estimation using discrete wavelet filters,
JOSA-A(20), No. 8, August 2003, pp. 1505-1515.
WWW Link. 0308
BibRef

Liu, H.Y.[Hai-Ying], Chellappa, R., Rosenfeld, A.,
Accurate dense optical flow estimation using adaptive structure tensors and a parametric model,
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 flow,
Motion02(214-219).
IEEE DOI 0303
BibRef

Oliensis, J.[John],
The Least-Squares Error for Structure from Infinitesimal Motion,
IJCV(61), No. 3, February-March 2005, pp. 259-299.
DOI Link 0412
Structure from optical flow. BibRef

Calway, A.D.[Andrew D.],
Recursive Estimation of 3D Motion and Surface Structure from Local Affine Flow Parameters,
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 smoothing,
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 Approach,
BMVC92(xx-yy).
PDF File. 9209
BibRef

Tan, S.[Sovira], Dale, J.L.[Jason L.], Anderson, A.[Andrew], Johnston, A.[Alan],
Inverse perspective mapping and optic flow: A calibration method and a quantitative analysis,
IVC(24), No. 2, 1 February 2006, pp. 153-165.
WWW Link. 0604
Inverse perspective mapping; Calibration methods BibRef

Liang, X.F.[Xue-Feng], McOwan, P.W.[Peter W.], Johnston, A.[Alan],
Biologically inspired framework for spatial and spectral velocity estimations,
JOSA-A(28), No. 4, April 2011, pp. 713-723.
WWW Link. 1104
Process in color space, not each separately. BibRef

Benoit, S.[Stephen], Ferrie, F.P.[Frank P.],
Towards direct recovery of shape and motion parameters from image sequences,
CVIU(105), No. 2, February 2007, pp. 145-165.
WWW Link. 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], Baillet, S.[Sylvain],
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. BibRef

Khan, S.[Sheraz], Lefevre, J.[Julien], Ammari, H.[Habib], Baillet, S.[Sylvain],
Feature detection and tracking in optical flow on non-flat manifolds,
PRL(32), No. 15, 1 November 2011, pp. 2047-2052.
Elsevier DOI 1112
Optical flow; Helmholtz-Hodge decomposition; Feature detection; Image processing; Riemannian formalism; Vector fields BibRef

Yuan, D.[Ding], Chung, R.[Ronald],
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], Schrater, P.R.[Paul R.],
Rapid classification of specular and diffuse reflection from image velocities,
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 BibRef

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

Zang, D.[Di], Schrater, P.R.[Paul R.], Doerschner, K.[Katja],
Object rigidity and reflectivity identification based on motion analysis,
ICIP10(4573-4576).
IEEE DOI 1009
BibRef

Bagnato, L.[Luigi], Frossard, P.[Pascal], Vandergheynst, P.[Pierre],
A Variational Framework for Structure from Motion in Omnidirectional Image Sequences,
JMIV(41), No. 3, November 2011, pp. 182-193.
WWW Link. 1110
BibRef
Earlier:
Optical flow and depth from motion for omnidirectional images using a TV-L1 variational framework on graphs,
ICIP09(1469-1472).
IEEE DOI 0911
See also Plenoptic based super-resolution for omnidirectional image sequences. BibRef

d'Angelo, E.[Emmanuel], Paratte, J.[Johan], Puy, G.[Gilles], Vandergheynst, P.[Pierre],
Fast TV-L1 optical flow for interactivity,
ICIP11(1885-1888).
IEEE DOI 1201
BibRef

Bouchafa, S.[Samia], Zavidovique, B.[Bertrand],
c-Velocity: A Flow-Cumulating Uncalibrated Approach for 3D Plane Detection,
IJCV(97), No. 2, April 2012, pp. 148-166.
WWW Link. 1203
In driver assistance applications. Treat objects as planar (road, obstacles). Hough transform like space. BibRef

Bouchafa, S.[Samia], Patri, A.[Antoine], Zavidovique, B.[Bertrand],
Efficient plane detection from a single moving camera,
ICIP09(3493-3496).
IEEE DOI 0911
BibRef


Berger, J.[Johannes], Schnörr, C.[Christoph],
Joint Recursive Monocular Filtering of Camera Motion and Disparity Map,
GCPR16(233-244).
Springer DOI 1611
BibRef

Neufeld, A.[Andreas], Berger, J.[Johannes], Becker, F.[Florian], Lenzen, F.[Frank], Schnörr, C.[Christoph],
Estimating Vehicle Ego-Motion and Piecewise Planar Scene Structure from Optical Flow in a Continuous Framework,
GCPR15(41-52).
Springer DOI 1511
BibRef

Akimov, D., Shestov, A., Voronov, A., Vatolin, D.,
Occlusion refinement for stereo video using optical flow,
IC3D12(1-8)
IEEE DOI 1503
computer graphics BibRef

Li, H.S.[Hong-Song], Song, T.[Ting], Wu, Z.H.[Ze-Huan], Ma, J.D.[Jian-Dong], Ding, G.Y.[Gang-Yi],
Shape from Specular Flow with Near-Field Environment Motion Field,
ISVC14(I: 367-378).
Springer DOI 1501
BibRef

Mathlouthi, Y.[Yosra], Mitiche, A.[Amar], Ben Ayed, I.[Ismail],
Direct Estimation of Dense Scene Flow and Depth from a Monocular Sequence,
ISVC14(I: 107-117).
Springer DOI 1501
BibRef

Tian, Y.D.[Yuan-Dong], Narasimhan, S.G.[Srinivasa G.], Vannevel, A.J.[Alan J.],
Depth from optical turbulence,
CVPR12(246-253).
IEEE DOI 1208
BibRef

Nourani-Vatani, N.[Navid], Borges, P.V.K.[Paulo V. K.], Roberts, J.M.[Jonathan M.], Srinivasan, M.V.[Mandyam V.],
Topological localization using optical flow descriptors,
RobPerc11(1030-1037).
IEEE DOI 1201
BibRef

Meinhardt-Llopis, E.[Enric], d'Hondt, O.[Olivier], Facciolo, G.[Gabriele], Caselles, V.[Vicent],
Relative depth from monocular optical flow,
ICIP11(2085-2088).
IEEE DOI 1201
BibRef

Gottfried, J.M.[Jens-Malte], Fehr, J.[Janis], Garbe, C.S.[Christoph S.],
Computing Range Flow from Multi-modal Kinect Data,
ISVC11(I: 758-767).
Springer DOI 1109
BibRef

Fu, S.C.[Shih Ching], Kovesi, P.[Peter],
Robust Extraction of Optic Flow Differentials for Surface Reconstruction,
DICTA10(468-473).
IEEE DOI 1012
BibRef

Birkbeck, N.[Neil], Cobzas, D.[Dana], Jagersand, M.[Martin],
Basis constrained 3D scene flow on a dynamic proxy,
ICCV11(1967-1974).
IEEE DOI 1201
BibRef
Earlier:
Depth and Scene Flow from a Single Moving Camera,
3DPVT10(xx-yy).
WWW Link. 1005
BibRef

Ruttle, J., Manzke, M., Dahyot, R.,
Estimating 3D Scene Flow from Multiple 2D Optical Flows,
IMVIP09(1-6).
IEEE DOI 0909
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Moving plane detection under translational camera motion using the c-velocity concept,
IPTA08(1-8).
IEEE DOI 0811
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Rotational flows for interpolation between sampled surfaces,
MMBIA08(1-8).
IEEE DOI 0806
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Wieneke, B.[Bernhard],
Method of determining a three-dimensional velocity field in a volume,
US_Patent7,382,900, Jun 3, 2008
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A Variational Approach to Adaptive Correlation for Motion Estimation in Particle Image Velocimetry,
DAGM08(xx-yy).
Springer DOI 0806
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Slesareva, N.[Natalia], Bruhn, A.[Andrés], Weickert, J.[Joachim],
Optic Flow Goes Stereo: A Variational Method for Estimating Discontinuity-Preserving Dense Disparity Maps,
DAGM05(33).
Springer DOI 0509
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Zhou, D.X.[Dong-Xiang], Zhang, H.[Hong],
2D Shape Measurement of Multiple Moving Objects by GMM Background Modeling and Optical Flow,
ICIAR05(789-795).
Springer DOI 0509
BibRef

Stevens, M.R., Snorrason, M.S., Eaton, R.S., McBride, J.C.,
Motion imagery navigation using terrain estimates,
ICPR04(IV: 272-275).
IEEE DOI 0409
See also Single Camera Stereo for Mobile Robot Surveillance. BibRef

Agarwal, S.[Sameer], Mallick, S.P.[Satya P.], Kriegman, D.J.[David J.], Belongie, S.J.[Serge J.],
On Refractive Optical Flow,
ECCV04(Vol II: 483-494).
Springer DOI 0405
Recover the refractive structure of an object from a video sequence acquired as the background behind the refracting object moves. E.g. a lens. BibRef

Lopez, J., Markel, M., Siddiqi, N., Gebert, G.,
Performance of passive ranging from image flow,
ICIP03(I: 929-932).
IEEE DOI 0312
BibRef

Wei, T.G.[Tian-Gong], Klette, R.[Reinhard],
Depth Recovery from Noisy Gradient Vector Fields Using Regularization,
CAIP03(116-123).
Springer DOI 0311
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Zucchelli, M., Santos-Victor, J., Christensen, H.I.,
Maximum likelihood structure and motion estimation integrated over time,
ICPR02(IV: 260-263).
IEEE DOI 0211
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Zucchelli, M., Santos-Victor, J., Christensen, H.I.,
Constrained structure and motion estimation from optical flow,
ICPR02(I: 339-342).
IEEE DOI 0211
BibRef

Lang, J., Pai, D.K.,
Estimation of elastic constants from 3D range-flow,
3DIM01(331-338).
IEEE DOI 0106
BibRef

Bayro-Corrochano, E.[Eduardo],
Computing Depth, Shape and Motion Using Invariants and Incidence Algebra,
ICPR00(Vol I: 881-884).
IEEE DOI 0009
BibRef

Oisel, L., Memin, E., Morin, L.,
Geometric Driven Optical Flow Estimation and Segmentation for 3D Reconstruction,
ECCV00(II: 849-863).
Springer DOI 0003
BibRef

Zhong, H., Cornilleau-Pérčs, V.[Valérie], Cheong, L., Droulez, J.[Jacques],
Visual Encoding of Tilt from Optic Flow: Psychophysics and Computational Modelling,
ECCV00(II: 800-816).
Springer DOI 0003
BibRef

Bergen, L., Meyer, F.,
A Novel Approach to Depth Ordering in Monocular Image Sequences,
CVPR00(II: 536-541).
IEEE DOI 0005
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Earlier:
Motion Segmentation and Depth Ordering Based on Morphological Segmentation,
ECCV98(II: 531).
Springer DOI approximate reconstruction See also Morphological Segmentation. BibRef

Cucka, P.[Peter], Rosenfeld, A.[Azriel],
Inferring Scene Depth Variations from Optical Flow Magnitudes,
UMDTR3741, February 1997.
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Takeda, N.[Nobuyuki], Watanabe, M.[Mutsumi],
Device and method for detection of moving obstacles,
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Watanabe, M., Takeda, N., Onoguchi, K.,
A Moving Object Recognition Method by Optical Flow Analysis,
ICPR96(I: 528-533).
IEEE DOI 9608
(Toshiba Kansai Research Lab., J) BibRef

Zheng, J.Y.[Jiang Yu], Kakinoki, H., Tanaka, K., Abe, N.,
Computing 3D Models of Rotating Objects from Moving Shading,
ICPR96(I: 800-804).
IEEE DOI 9608
(Kyushu Inst. of Technology, J) BibRef

Lindeberg, T.[Tony],
Direct Estimation of Affine Deformations Using Visual Front-End Operators with Automatic Scale Selection,
ICCV95(134-141).
IEEE DOI BibRef 9500
And:
Direct Estimation of Image Deformations Using Visual Front-End Operations with Automatic Scale Selection,
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Dijkstra, T.M.H., Snoeren, P.R., Gielen, C.C.A.M.,
Extraction of 3D Shape from Optic Flow: A Geometric Approach,
CVPR94(135-140).
IEEE DOI Discussion of use of invariants for different shapes BibRef 9400

Lawton, D.T.,
Optic Flow Field Structure and Processing Image Motion,
IJCAI81(700-703). BibRef 8100
Earlier:
Constraint-Based Inference from Image Motion,
AAAI-80(31-34). Uses rigidity to compute Z, then X and Y can be derived. BibRef

Carlsson, S.,
Sufficient Image Structure for 3-D Motion and Shape Estimation,
ECCV94(A:83-91).
Springer DOI BibRef 9400
And: ISRN KTH/NA/P-94/24-SE, August 1994.
PS File. BibRef

Carlsson, S.,
Recursive Estimation of Ego-Motion and Scene Structure from a Moving Platform,
SCIA91(958-965). BibRef 9100

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Information in the Geometric Structure of Retinal Flow Fields,
ICCV88(629-633).
IEEE DOI How to compute motion and surface information given perfect flow fields. See also Passive Navigation. BibRef 8800

Yasuno, T.[Takayuki], Suzuki, S.[Satoshi],
Occlusion Analysis of Spatiotemporal Images for Surface Reconstruction,
BMVC93(xx-yy).
PDF File. 9309
BibRef

Wildes, R.P.,
On the Qualitative Structure of Temporally Evolving Visual Motion Fields,
AAAI-93(844-850). BibRef 9300

Kersten, D.[Daniel], Bulthoff, H.H.[Heinrich H.],
Apparent Opacity Affects Perception of Structure from Motion,
MIT AI Memo-1285, January 1991. BibRef 9101

Kitahashi, T.[Tadahiro], and Endo, A.[Airoyuki],
A New Method of 3-D Motion Analysis Using a Concept of Projective Geometry,
IJCAI85(902-904). Vanishing point analysis for motion. BibRef 8500

Loomis, J.M., Eby, D.W.,
Relative Motion Parallax and the Perception of Structure from Motion,
Motion89(204-211). BibRef 8900
Earlier:
Perceiving Structure from Motion: Failure of Shape Constancy,
ICCV88(383-391).
IEEE DOI Psychology, not results. BibRef

Schott, J.P.[Jean-Pierre],
Three-Dimensional Motion Estimation Using Shading Information in Multiple Frames,
MIT AI-TR-1162, August 1989.
WWW Link. BibRef 8908

Shahraray, B., Brown, M.K.,
Robust Depth Estimation from Optical Flow,
ICCV88(641-650).
IEEE DOI BibRef 8800

Heeger, D.J., Pentland, A.P.,
Seeing Structure Through Chaos,
Motion86(131-136). BibRef 8600

Hoffman, D.D.,
Inferring Shape from Motion Fields,
MIT AI Memo-592, December 1980. BibRef 8012

Chapter on Optical Flow Field Computations and Use continues in
Real-Time Computation, Real-Time Implementation, Hardware for Optical Flow .


Last update:Nov 18, 2017 at 20:56:18