17.2.3 Optical Flow Along Contours

Chapter Contents (Back)
Contours. Optical Flow. Optical Flow, Contours.

Bergholm, F., and Carlsson, S.,
A 'Theory' of Optical Flow,
CVGIP(53), No. 2, March 1991, pp. 171-188.
Elsevier DOI BibRef 9103
Earlier: A1 only: ISRN KTH/NA/P--88/10--SE, 1988. BibRef
Earlier:
Global Structure of Velocity Fields and the Aperture Problem in the Large,
ISRN KTH/NA/P-87/15-SE, 1987. Analysis of curves in motion with normal flow and a few estimates at feature points, produce a catalog of ambiguous curves and also derive field lines of optical flow. Theory is appropriate in the title. BibRef

Bergholm, F.,
Motion from Flow Along Contours: A Note on Robustness and Ambiguous Cases,
IJCV(2), No. 4, April 1989, pp. 395-415.
Springer DOI BibRef 8904
And: ` ISRN KTH/NA/P--87/07--SE. Ambiguous curves: contours without unique motion from normal velocity. Must use more global information since local information is almost always ambiguous. BibRef

Bergholm, F.,
On the Content of Information in Edges and Optical Flow,
Ph.D.Dept. of Numerical Analysis and Computing Science, Royal Institute of Technology, May 1989. BibRef 8905 ISRN KTH/NA/P--89/04--SE. BibRef

Bergholm, F.,
Decomposition Theory and Transformations of Visual Directions,
ICCV90(85-90).
IEEE DOI BibRef 9000

Hildreth, E.C., (MIT),
Computing the Velocity Field along Contours,
Motion83(26-32). BibRef 8300
Earlier:
The Integration of Motion Information along Contours,
CVWS82(83-91). Requires application of local constraints, since the problem is inherently ambiguous. The use of the moving contour is important. Compare to Davis paper. See also Computation of the Velocity Field, The. BibRef

Davis, L.S.[Larry S.], Wu, Z.Q.[Zhong-Quan], and Sun, H.[Hanfang],
Contour-Based Motion Estimation,
CVGIP(23), No. 3, September 1983, pp. 313-326.
Elsevier DOI BibRef 8309
And: Correction: CVGIP(28), No. 1, October 1984, pp. 134. BibRef
Earlier: DARPA82(124-131). A contour based approach to motion, compute motion at corners, then propagate along the contours to reach a steady state based on a local 2.5-D motion assumption. Compare to Hildreth BibRef

Faugeras, O.D.,
On the Motion of 3D Curves and Its Relationship to Optical Flow,
ECCV90(105-117).
Springer DOI BibRef 9000
And: INRIA-Sophia AntipolisNo. 1183, March 1990. Establish equations given that the curves do not change much. BibRef

Faugeras, O.D., Papadopoulo, T.,
A Theory of the Motion Fields of Curves,
IJCV(10), 1993, pp. 125-156.
Springer DOI
PS File. BibRef 9300

Papadopoulo, T.[Theo], Faugeras, O.D.[Olivier D.],
Computing Structure and Motion of General 3D Curves from Monocular Sequences of Perspective Images,
ECCV96(II:696-708).
Springer DOI BibRef 9600
And:
Motion Field of Curves: Applications,
ECCV94(A:71-82).
Springer DOI BibRef

Wohn, K.[Kwangyoen], Waxman, A.M.[Allen M.],
The Analytic Structure of Image Flows: Deformation and Segmentation,
CVGIP(49), No. 2, February 1990, pp. 127-151.
Elsevier DOI From local and global flow structure, determine the analytic boundaries and thus motion based segmentations. Multiple frame extensions are suggested. See also Binocular Image Flows: Steps Toward Stereo-Motion Fusion. BibRef 9002

Waxman, A.M.[Allen M.], Wohn, K.[Kwangyoen],
Contour Evolution, Neighbourhood Deformation and Global Image Flow: Planar Surfaces in Motion,
IJRR(4), 1985, pp. 95-108. BibRef 8500
Earlier: UMD-CAR-TR-58, April, 1984. Introduces the Taylor series expansion of the motion equations. BibRef

Waxman, A.M.[Allen M.], Wohn, K.[Kwangyoen],
Contour Evolution, Neighborhood Deformation and Image Flow: Textured Surfaces in Motion,
IU87(72-98). BibRef 8700

Waxman, A.M.[Allen M.], Wohn, K.[Kwangyoen],
Image Flow Theory: A Framework for 3-D Inference from Time-Varying Imagery,
ACV88(I 165-224). BibRef 8800

Waxman, A.M.[Allen M.], (UMd),
An Image Flow Paradigm,
CVWS84(49-57). BibRef 8400
And: RCV87(145-168). A general paper to address several issues of what is required for using optic flow data, and generating 3-D descriptions from the 2-D input data. BibRef

Wu, J.[Jian], and Wohn, K.,
On the Deformation of Image Intensity and Zero-Crossing Contours under Motion,
CVGIP(53), No. 1, January 1991, pp. 66-75.
Elsevier DOI BibRef 9101

Waxman, A.M., Wu, J., Bergholm, F.,
Convected Activation Profiles and the Measurement of Visual Motion,
CVPR88(717-723).
IEEE DOI BibRef 8800

Waxman, A.M., and Bergholm, F.,
Convected Activation Profiles and Image Flow Extraction,
ISRN KTH/NA/P-87/10-SE, August 1987. BibRef 8708

Bhanu, B.[Bir], Burger, W.[Wilhelm],
Approximation of Displacement Fields Using Wavefront Region Growing,
CVGIP(41), No. 3, March 1988, pp. 306-322.
Elsevier DOI BibRef 8803
And:
Estimation of Image Motion Using Wavefront Region Growing,
ICCV87(428-432). BibRef
And:
System for computing the self-motion of moving images devices,
US_Patent4,969,036, Nov 6, 1990
WWW Link. It might really be motion, but it seems to be contour matching. Match the contours through a sequence and get the corresponding points along the contour. BibRef

Wu, J., Brockett, R., and Wohn, K.,
A Contour-Based Recovery of Image Flow: Iterative Transformation Method,
PAMI(13), No. 8, August 1991, pp. 746-760.
IEEE DOI BibRef 9108
Earlier:
A Contour-based Recovery of Image Flow: Iterative Method,
CVPR89(124-129).
IEEE DOI Start from the (normal velocity) flow of the contour and smooth it across the image to get a complete flow field. BibRef

Brockett, R.W.,
Gramians, Generalized Inverses, and the Least-Squares Approximation of Optical Flow,
JVCIR(1), 1990, pp. 3-11. BibRef 9000

Wohn, K., and Wu, J.,
3-D Motion Recovery from Time-Varying Optical Flows,
AAAI-86(670-675). BibRef 8600

d'Haeyer, J.P.F.[Johan P.F], Bruyland, I.[Ignace],
Parallel Computation of Image Curve Velocity Fields,
CVGIP(43), No. 2, August 1988, pp. 239-255.
WWW Link. Parallel solution of a regularization problem. BibRef 8808

d'Haeyer, J.P.F.[Johan P.F],
Determining Motion of Image Curves from Local Pattern Changes,
CVGIP(34), No. 2, May 1986, pp. 166-188.
WWW Link. (Univ. of Ghent). The velocity field along a contour is found using a differential equation. A minimum dilation principle is used to find nonelastic motion or 2-D rigid motion. Applied to sign language images. BibRef 8605

Arnspang, J.,
On the Use of the Horizon of a Translating Planar Curve,
PRL(10), 1989, pp. 61-69. BibRef 8900

Park, J.S.[Jong Seung], and Han, J.H.[Joon Hee],
Estimating Optical Flow by Tracking Contours,
PRL(18), No. 7, July 1997, pp. 641-648. 9711
BibRef
Earlier:
A Curvature-Based Approach to Contour Motion Estimation,
ICCV98(1018-1023).
IEEE DOI See also Contour Matching: A Curvature-Based Approach. BibRef

Park, J.S.[Jong Seung], Han, J.H.[Joon Hee],
Contour Motion Estimation from Image Sequences Using Curvature Information,
PR(31), No. 1, January 1998, pp. 31-39.
WWW Link. 9802
BibRef

Guerrero, J.J., Sagues, C.,
Camera motion from brightness on lines. Combination of features and normal flow,
PR(32), No. 2, February 1999, pp. 203-216.
WWW Link. Straight lines and flow for camera motion. BibRef 9902

Kalmoun, E.[El_Mostafa], Garrido, L.[Luis], Caselles, V.[Vicent],
Line Search Multilevel Optimization as Computational Methods for Dense Optical Flow,
SIIMS(4), No. 2, 2011, pp. 695-722.
WWW Link. 1110
BibRef

Garrido, L.[Lluís], Kalmoun, E.[El_Mostafa],
A Line Search Multilevel Truncated Newton Algorithm for Computing the Optical Flow,
IPOL(5), 2015, pp. 124-138.
DOI Link 1508
Code, Optical Flow. BibRef

Bao, L.C.[Lin-Chao], Yang, Q.X.[Qing-Xiong], Jin, H.L.[Hai-Lin],
Fast Edge-Preserving PatchMatch for Large Displacement Optical Flow,
IP(23), No. 12, December 2014, pp. 4996-5006.
IEEE DOI 1402
BibRef
Earlier: CVPR14(3534-3541)
IEEE DOI 1409
edge detection. Bilateral Filter BibRef


Sanchez, J.[Javier], Salgado, A.[Agustin], Monzon, N.[Nelson],
Preserving accurate motion contours with reliable parameter selection,
ICIP14(209-213)
IEEE DOI 1502
Adaptive optics BibRef

Mahabalagiri, A.[Anvith], Ozcan, K.[Koray], Velipasalar, S.[Senem],
Camera motion detection for mobile smart cameras using segmented edge-based optical flow,
AVSS14(271-276)
IEEE DOI 1411
Cameras BibRef

Artner, N.M.[Nicole M.], Kropatsch, W.G.[Walter G.],
Structural Cues in 2D Tracking: Edge Lengths vs. Barycentric Coordinates,
CIARP13(II:503-511).
Springer DOI 1311
BibRef

Barron, J.L.[John L.], Daniel, M., Mari, J.,
Using 3D Spline Differentiation to Compute Quantitative Optical Flow,
CRV06(11-11).
IEEE DOI 0607
BibRef

Estépar, R.S.J.[Raúl San José], Haker, S.[Steve], Westin, C.F.[Carl-Fredrik],
Riemannian Mean Curvature Flow,
ISVC05(613-620).
Springer DOI 0512
BibRef

Chamorro-Martinez, J., Fdez-Valdivia, J.,
Optical flow estimation based on the extraction of motion patterns,
ICIP03(I: 925-928).
IEEE DOI 0312
BibRef

Neckels, K.[Kai],
Fast Local Estimation of Optical Flow Using Variational and Wavelet Methods,
CAIP01(349 ff.).
Springer DOI 0210
BibRef

El-Feghali, R., Mitiche, A.,
Fast Computation of a Boundary Preserving Estimate of Optical Flow,
BMVC00(xx-yy).
PDF File. 0009
BibRef

Otsuka, K., Horikoshi, T., Suzuki, S.,
Image Velocity Estimation from Trajectory Surface in Spatiotemporal Space,
CVPR97(200-205).
IEEE DOI 9704
Spatio-temporal space use edges. BibRef

Bergholm, F.,
A Theory on Optical Velocity Fields and Ambiguous Motion of Curves,
ICCV88(165-176).
IEEE DOI BibRef 8800

Chapter on Optical Flow Field Computations and Use continues in
Optical Flow Field Computation -- Gradient Techniques .


Last update:Sep 25, 2017 at 16:36:46