18.5.1 Optical Flow Field -- Boundaries

Chapter Contents (Back)
Boundaries, Optical Flow. Edges, Motion. Optical Flow, Boundaries. Optical Flow, Multiple Layers. Multiple Motions. Motion, Multiple. Motion, Discontinuity. Motion, Segmentation.

Heitz, F., and Bouthemy, P.,
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Earlier:
Multimodal Motion Estimation and Segmentation Using Markov Random Fields,
ICPR90(I: 378-383).
IEEE DOI BibRef

Crivelli, T.[Tomás], Bouthemy, P.[Patrick], Cernuschi-Frías, B.[Bruno], Yao, J.F.[Jian-Feng],
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Crivelli, T.[Tomás], Piriou, G.[Gwenaelle], Bouthemy, P.[Patrick], Cernuschi-Frías, B.[Bruno], Yao, J.F.[Jian-Feng],
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Crivelli, T.[Tomás], Cernuschi-Frías, B.[Bruno], Bouthemy, P.[Patrick], Yao, J.F.[Jian-Feng],
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Crivelli, T., Cernuschi-Frias, B., Bouthemy, P., Yao, J.F.,
Mixed-State Markov Random Fields for Motion Texture Modeling and Segmentation,
ICIP06(1857-1860). 0610

IEEE DOI BibRef

Crivelli, T.[Tomas], Fradet, M.[Matthieu], Conze, P.H.[Pierre-Henri], Robert, P.[Philippe], Perez, P.[Patrick],
Robust Optical Flow Integration,
IP(24), No. 1, January 2015, pp. 484-498.
IEEE DOI 1502
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Earlier: A1, A3, A4, A2, A5:
Multi-step flow fusion: Towards accurate and dense correspondences in long video shots,
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image motion analysis BibRef

Crivelli, T.[Tomas], Conze, P.H.[Pierre-Henri], Robert, P.[Philippe], Perez, P.[Patrick],
From optical flow to dense long term correspondences,
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IEEE DOI 1302
BibRef

Heitz, F., Perez, P., Bouthemy, P.,
Multiscale Minimization of Global Energy Functions in Some Visual Recovery Problems,
CVGIP(59), No. 1, January 1994, pp. 125-134.
DOI Link BibRef 9401
Earlier:
Parallel Visual Motion Analysis Using Multiscale Markov Random Fields,
Motion91(30-35). Multigrid approach to avoid local minimums, pyramid of primitives, not observations. BibRef

Odobez, J.M., and Bouthemy, P.,
Robust Multiresolution Estimation of Parametric Motion Models,
JVCIR(6), No. 4, December 1995, pp. 348-365. For software:
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And:
MRF-based motion segmentation exploiting a 2D motion model robust estimation,
ICIP95(III: 628-631).
IEEE DOI 9510
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Earlier:
Detection of multiple moving objects using multiscale MRF with camera motion compensation,
ICIP94(II: 257-261).
IEEE DOI 9411
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Memin, E., Perez, P.,
Dense Estimation and Object-Based Segmentation of the Optical-Flow with Robust Techniques,
IP(7), No. 5, May 1998, pp. 703-719.
IEEE DOI 9805
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Mémin, E.[Etienne], Pérez, P.[Patrick],
Hierarchical Estimation and Segmentation of Dense Motion Fields,
IJCV(46), No. 2, February 2002, pp. 129-155.
DOI Link 0201

See also Dense Estimation of Fluid Flows. BibRef

Memin, E., Perez, P.,
Joint Estimation-Segmentation of Optic Flow,
ECCV98(II: 563).
Springer DOI BibRef 9800
And:
A Multigrid Approach for Hierarchical Motion Estimation,
ICCV98(933-938).
IEEE DOI BibRef
And:
Robust Discontinuity-Preserving Model for Estimating Optical Flow,
ICPR96(I: 920-924).
IEEE DOI 9608
(IRISA/INRIA, F) BibRef

Avenel, C.[Christophe], Mémin, E.[Etienne], Pérez, P.[Patrick],
Stochastic Level Set Dynamics to Track Closed Curves Through Image Data,
JMIV(49), No. 2, June 2014, pp. 296-316.
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BibRef
Earlier:
Stochastic Filtering of Level Sets for Curve Tracking,
ICPR10(3553-3556).
IEEE DOI 1008
BibRef
Earlier:
Tracking Closed Curves with Non-linear Stochastic Filters,
SSVM09(576-587).
Springer DOI 0906
BibRef

Hellier, P., Barillot, C., Memin, E., Perez, P.,
Hierarchical estimation of a dense deformation field for 3-D robust registration,
MedImg(20), No. 5, May 2001, pp. 388-402.
IEEE Top Reference. 0110
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Giachetti, A.[Andrea], Torre, V.[Vincent],
The Use of Optical-Flow for the Analysis of Nonrigid Motions,
IJCV(18), No. 3, June 1996, pp. 255-279.
Springer DOI 9608
BibRef
Earlier:
Refinement of Optical Flow Estimation and Detection of Motion Edges,
ECCV96(II:151-160).
Springer DOI BibRef
Earlier:
Optical Flow and Deformable Objects,
ICCV95(706-711).
IEEE DOI Multi-scale technique. Simple edge detection from flow field. BibRef

Giachetti, A., Campani, M., Torre, V.,
The Use of Optical Flow for the Autonomous Navigation,
ECCV94(A:146-151).
Springer DOI BibRef 9400

Ghosal, S., Vanek, P.,
A Fast Scalable Algorithm for Discontinuous Optical-Flow Estimation,
PAMI(18), No. 2, February 1996, pp. 181-194.
IEEE DOI Multi-Scale. Regularization. Interpolation and regularization depends on the strength and direction of the gradient. BibRef 9602

Ghosal, S.[Sugata], Mehrotra, R.[Rarv],
Robust Optical-Flow Estimation Using Semi-Invariant Local Features,
PR(30), No. 2, February 1997, pp. 229-237.
Elsevier DOI 9704
BibRef
Earlier:
Robust Optical Flow Estimation,
ICIP94(II: 780-784).
IEEE DOI 9411
moments as invariants. BibRef

Worring, M., Smeulders, A.W.M., Staib, L.H., Duncan, J.S.,
Parameterized Feasible Boundaries in Gradient Vector-Fields,
CVIU(63), No. 1, January 1996, pp. 135-144.
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Mizuki, M.M.[Marcelo M.], Masaki, I.[Ichiro], Chandrakasan, A.[Anantha], Horn, B.K.P.[Berthold K.P.],
Method and apparatus for motion estimation in a video signal,
US_Patent5,838,828, Nov 17, 1998
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Desai, U.Y.[Ujjaval Y.], Mizuki, M.M.[Marcelo M.], Masaki, I.[Ichiro], Horn, B.K.P.[Berthold K.P.],
Edge and Mean Based Image Compression,
MIT AI Memo-1584, 1995.
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Patras, I., Worring, M., van den Boomgaard, R.[Rein],
Dense Motion Estimation Using Regularization Constraints on Local Parametric Models,
IP(13), No. 11, November 2004, pp. 1432-1443.
IEEE DOI 0411
Optic flow. Motion within segmented patches parameterized. Three frame approach. BibRef

Patras, I., Worring, M.,
Regularized patch motion estimation,
ICPR02(I: 323-326).
IEEE DOI 0211
BibRef

Patras, I., Hendriks, E.A., Lagendijk, R.L.,
Probabilistic Confidence Measures for Block Matching Motion Estimation,
CirSysVideo(17), No. 8, August 2007, pp. 988-995.
IEEE DOI 0710
BibRef
Earlier:
Confidence measures for block matching motion estimation,
ICIP02(II: 277-280).
IEEE DOI 0210
BibRef

Francois, E., Chupeau, B.,
Depth-Based Segmentation,
CirSysVideo(7), No. 1, February 1997, pp. 237-240.
IEEE Top Reference. 9703
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Francois, E., Vial, J.F., Chupeau, B.,
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CirSysVideo(7), No. 1, February 1997, pp. 97-108.
IEEE Top Reference. 9703
BibRef

Zetzsche, C., Barth, E.,
Direct Detection of Flow Discontinuities by 3D Curvature Operators,
PRL(12), 1991, pp. 771-779. BibRef 9100

Murray, D.W., Williams, N.S.,
Detecting the Image Boundaries Between Optical Flow Fields from Several Moving Planar Facets,
PRL(4), 1986, pp. 87-92. BibRef 8600

Hartley, R.I.,
Segmentation of Optical Flow Fields by Pyramid Linking,
PRL(3), 1985, pp. 253-262. BibRef 8500

Pas, S.F.T., Kappers, A.M.L., Koenderink, J.J.,
Detection of Second-Order Structure in Optical-Flow Fields,
JOSA-A(14), No. 4, April 1997, pp. 767-778. 9704

See also Structure of Locally Orderless Images, The. BibRef

Karlsson, S.M.[Stefan M.], Pont, S.C.[Sylvia C.], Koenderink, J.J.[Jan J.],
Illuminance flow over anisotropic surfaces with arbitrary viewpoint,
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Karlsson, S.M.[Stefan M.], Pont, S.C.[Sylvia C.], Koenderink, J.J.[Jan J.], Zisserman, A.[Andrew],
Illuminance Flow Estimation by Regression,
IJCV(90), No. 3, December 2010, pp. 304-312.
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BibRef

Pont, S.C.[Sylvia C.], Koenderink, J.J.[Jan J.],
Surface Illuminance Flow,
3DPVT04(2-9).
IEEE DOI 0412
BibRef
Earlier:
Illuminance Flow,
CAIP03(90-97).
Springer DOI 0311
BibRef

Liu, H.C.[Hong-Che], Hong, T.H.[Tsai-Hong], Herman, M., Chellappa, R.,
A General Motion Model and Spatiotemporal Filters for Computing Optical-Flow,
IJCV(22), No. 2, March 1997, pp. 141-172.
DOI Link 9706
BibRef
And: UMDTR-3365, November 1994.
WWW Link. BibRef
Earlier:
Spatio-temporal filters for transparent motion segmentation,
ICIP95(III: 464-467).
IEEE DOI 9510

See also Motion-Model-Based Boundary Extraction and a Real-Time Implementation. BibRef

Ong, E.P., Spann, M.,
Robust Optical Flow Computation Based on Least-Median-of-Squares Regression,
IJCV(31), No. 1, February 1999, pp. 51-82.
DOI Link BibRef 9902
Earlier:
Robust Multiresolution Computation of Optical Flow,
ICASSP96(XX) BibRef
Earlier:
Robust Computation of Optical Flow,
BMVC95(xx-yy).
PDF File. 9509
School of Elec. & Elect. Eng.. University of Birmingham. Affine model, find discontinutities. BibRef

Gibson, D., Spann, M.,
Robust optical flow estimation based on a sparse motion trajectory set,
IP(12), No. 4, April 2003, pp. 431-445.
IEEE DOI 0306
BibRef

Kruse, S.M.,
Scene segmentation from dense displacement vector fields using randomized Hough transform,
SP:IC(9), No. 1, November 1996, pp. 29-41.
Elsevier DOI Rough displacement with Hough, merge similar, refine. BibRef 9611

Eua-Anant, N., Udpa, L.,
Boundary Detection Using Simulation of Particle Motion in a Vector Image Field,
IP(8), No. 11, November 1999, pp. 1560-1571.
IEEE DOI 9911
BibRef
Earlier:
Boundary Extraction Algorithm Based on Particle Motion in a Vector Image Field,
ICIP97(II: 732-735).
IEEE DOI BibRef

Black, M.J.[Michael J.], Fleet, D.J.[David J.],
Probabilistic Detection and Tracking of Motion Boundaries,
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PDF File. BibRef
Earlier:
Probabilistic Detection and Tracking of Motion Discontinuities,
ICCV99(551-558).
IEEE DOI
HTML Version. Award, Marr Prize, HM. Bayesian framework for local motion. Generative model for discontinuities.
See also Design and Use of Linear Models for Image Motion Analysis. BibRef

Fleet, D.J.[David J.], Black, M.J.[Michael J.], Nestares, O.,
Bayesian Inference of Visual Motion Boundaries,
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Lim, K.P.[Keng Pang], Das, A.[Amitabha], Chong, M.N.[Man Nang],
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PAMI(24), No. 5, May 2002, pp. 712-718.
IEEE DOI 0205
BibRef

Lim, K.P., Chong, M.N., Das, A.,
Low-Bit-Rate Video Coding Using Dense Motion Field and Uncovered Background Prediction,
IP(10), No. 1, January 2001, pp. 164-166.
IEEE DOI 0101
BibRef
Earlier:
A New MRF Model for Robust Estimate of Occlusion and Motion Vector Fields,
ICIP97(II: 843-846).
IEEE DOI 9710
BibRef

Wei, J.[Jie], Gertner, I.[Izidor],
MRF-MAP-MFT visual object segmentation based on motion boundary field,
PRL(24), No. 16, December 2003, pp. 3125-3139.
Elsevier DOI 0310
Segmentation, not motion field. BibRef

Jodoin, P.M.[Pierre-Marc], Mignotte, M.[Max], Rosenberger, C.,
Segmentation Framework Based on Label Field Fusion,
IP(16), No. 10, October 2007, pp. 2535-2550.
IEEE DOI 0711
BibRef
Earlier: A1, A3, A2:
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PDF File. 0609
BibRef

Mignotte, M.,
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IP(19), No. 6, June 2010, pp. 1610-1624.
IEEE DOI 1006
Markov random field fusion model. Combine several segmentation results.
See also Multiresolution Markovian Fusion Model for the Color Visualization of Hyperspectral Images, A. BibRef

Jodoin, P.M.[Pierre-Marc], Mignotte, M.[Max],
Optical-flow based on an edge-avoidance procedure,
CVIU(113), No. 4, April 2009, pp. 511-531.
Elsevier DOI 0903
BibRef
Earlier: ICIP06(1253-1256). 0610

IEEE DOI Optical flow; Motion estimation; Information fusion; Mean-shift BibRef

Ben-Shahar, O.[Ohad], Zucker, S.W.[Steven W.],
General Geometric Good Continuation: From Taylor to Laplace via Level Sets,
IJCV(86), No. 1, January 2010, pp. xx-yy.
Springer DOI 1001
BibRef
Earlier:
Good Continuation of General 2D Visual Features: Dual Harmonic Models and Computational Inference,
ICCV05(II: 1643-1650).
IEEE DOI 0510
BibRef
Earlier:
On the Perceptual Organization of Texture and Shading Flows: From a Geometrical Model to Coherence Computation,
CVPR01(I:1048-1055).
IEEE DOI 0110
BibRef
And:
Flowing toward coherence: On the geometry of texture and shading flows,
PercOrg01(xx-yy). 0106
Locally parallel patterns. BibRef

Zucker, S.W.,
Hue flows and scene structure,
3DPVT04(704-704).
IEEE DOI 0412
BibRef

Ben-Shahar, O., Zucker, S.W.,
Hue fields and color curvatures: A Perceptual Organization Approach to Color Image Denoising,
CVPR03(II: 713-720).
IEEE DOI 0307
BibRef

Ben-Shahar, O.[Ohad], Huggins, P.S.[Patrick S.], Zucker, S.W.[Steven W.],
On Computing Visual Flows with Boundaries: The Case of Shading and Edges,
BMCV02(189 ff.).
Springer DOI 0303

See also Folds and Cuts: How Shading Flows Into Edges. BibRef

Ben-Shahar, O.[Ohad], Glaser, A.[Andreas], Zucker, S.W.[Steven W.],
Good Continuation in Layers: Shading flows, color flows, surfaces and shadows,
PercOrg06(176).
IEEE DOI 0609
BibRef

Jazi, M.H.[Marjan Hadian], Bab-Hadiashar, A.[Alireza], Hoseinnezhad, R.[Reza],
Statistical analysis of three-dimensional optical flow separability in volumetric images,
IET-CV(9), No. 6, 2015, pp. 895-902.
DOI Link 1512
BibRef
Earlier:
Statistical separability of local motions in volumetric images,
ICIP13(3855-3859)
IEEE DOI 1402
optical flow; segmentation; volumetric images BibRef

Rao, S.[Sana], Wang, H.Z.[Han-Zi],
Robust optical flow estimation via edge preserving filtering,
SP:IC(96), 2021, pp. 116309.
Elsevier DOI 2106
Robust optical flow estimation, Motion estimation, Edge-preserving, Weighted guided filtering, NLTV- model BibRef

Gothe, S.V.[Sourabh Vasant], Agarwal, V.[Vibhav], Ghosh, S.[Sourav], Vachhani, J.R.[Jayesh Rajkumar], Kashyap, P.[Pranay], Kandur, B.R.[Barath Raj],
What's in the Flow? Exploiting Temporal Motion Cues for Unsupervised Generic Event Boundary Detection,
WACV24(6926-6935)
IEEE DOI 2404
Solid modeling, Image motion analysis, Tracking, Motion segmentation, Computational modeling, Data models, Smartphones / end user devices BibRef

Abdullah, A.[Abdullah], Holler, M.[Martin], Kunisch, K.[Karl], Landman, M.S.[Malena Sabate],
Latent-space Disentanglement with Untrained Generator Networks for the Isolation of Different Motion Types in Video Data,
SSVM23(326-338).
Springer DOI 2307
Multiple motions in scene. BibRef