17.2.8 Parallel Optic Flow Computation, Efficient Computation

Chapter Contents (Back)
Parallel Algorithms. Optical Flow, Parallel. See also Real-Time Computation, Real-Time Implementation, Hardware for Optical Flow.

Li, H., Wang, J.,
Computing Optical Flow With A Recurrent Neural Network,
PRAI(7), 1993, pp. 801-814. BibRef 9300

Marcenaro, G., and Tistarelli, M.,
Analysis of Multi-dimensional Images on the Connection Machine System,
CPE(3), No. 6, December 1991, pp. 699-713. BibRef 9112

Shin, H.S.[Hyun-Soo],
Motion vector detecting method of a video signal,
US_Patent5,387,947, Feb 7, 1995
WWW Link. BibRef 9502

Kramer, J.,
Compact Integrated Motion Sensor With 3-Pixel Interaction,
PAMI(18), No. 4, April 1996, pp. 455-460.
IEEE DOI 9605
Hardware implementation in analog VLSI. BibRef

Sebok, T.J.[Thomas J.], Sebok, D.R.[Dale R.],
Optical flow detection system,
US_Patent5,627,905, May 6, 1997
WWW Link. BibRef 9705

Zelek, J.S.[John S.],
Towards Bayesian real-time optical flow,
IVC(22), No. 12, 1 October 2004, pp. 1051-1069.
WWW Link. 0409
BibRef
Earlier:
Bayesian Real-Time Optical Flow,
VI02(266).
PDF File. 0208
Camus algorithm ( See also Real-Time Quantized Optical Flow. ) problems with featureless areas. BibRef

Lodato, C.[Carmelo], Lopes, S.[Salvatore],
A New Parallel Differential Method for Optical Flow Estimation,
JMIV(26), No. 3, December 2006, pp. 345-356.
Springer DOI 0701
BibRef

Sosa, J.C.[Julio C.], Boluda, J.A.[Jose A.], Pardo, F.[Fernando], Gómez-Fabela, R.[Rocío],
Change-driven data flow image processing architecture for optical flow computation,
RealTimeIP(2), No. 4, December 2007, pp. 259-270.
Springer DOI 0712
BibRef

Pardo, F.[Fernando], Zuccarello, P.[Pedro], Boluda, J.A.[Jose A.], Vegara, F.[Francisco],
Advantages of Selective Change-Driven Vision for Resource-Limited Systems,
CirSysVideo(21), No. 10, October 2011, pp. 1415-1423.
IEEE DOI 1110
BibRef
Earlier: A3, A4, A1, A2:
Selective Change-Driven Image Processing: A Speeding-Up Strategy,
CIARP09(37-44).
Springer DOI 0911
BibRef
Earlier: A1, A3, A4, A2:
On the Advantages of Asynchronous Pixel Reading and Processing for High-Speed Motion Estimation,
ISVC08(I: 205-215).
Springer DOI 0812
BibRef

Selby, S.[Steve],
Apparatus and method for performing sub-pixel vector estimations using quadratic approximations,
US_Patent7,292,283, Nov 6, 2007
WWW Link. BibRef 0711

Han, X., Xu, C.Y., Prince, J.L.,
Fast numerical scheme for gradient vector flow computation using a multigrid method,
IET-IPR(1), No. 1, March 2007, pp. 48-55.
DOI Link 0905
BibRef

Wei, Z.Y.[Zhao-Yi], Lee, D.J.[Dah-Jye], Nelson, B.E.[Brent E.], Archibald, J.K.,
Hardware-Friendly Vision Algorithms for Embedded Obstacle Detection Applications,
CirSysVideo(20), No. 11, November 2010, pp. 1577-1589.
IEEE DOI 1011
BibRef

Wei, Z.Y.[Zhao-Yi], Lee, D.J.[Dah-Jye], Nelson, B.E.[Brent E.], Lillywhite, K.D.[Kirt D.],
Accurate Optical Flow Sensor for Obstacle Avoidance,
ISVC08(I: 240-247).
Springer DOI 0812
BibRef
Earlier: A1, A2, A3, Only:
A Hardware-Friendly Adaptive Tensor Based Optical Flow Algorithm,
ISVC07(II: 43-51).
Springer DOI 0711
BibRef

Wei, Z.Y.[Zhao-Yi], Lee, D.J.[Dah-Jye], Nelson, B.E.[Brent E.], Martineau, M.[Michael],
A Fast and Accurate Tensor-based Optical Flow Algorithm Implemented in FPGA,
WACV07(18-18).
IEEE DOI 0702
BibRef

Shiralkar, M.P.[Manish P.], Schalkoff, R.J.[Robert J.],
A self-organization based optical flow estimator with GPU implementation,
MVA(23), No. 6, November 2012, pp. 1229-1242.
WWW Link. 1210
BibRef

Frey, D.[Daniel], Ulrich, M.[Markus], Hinz, S.[Stefan],
Evaluation of Efficient Methods for Optical Flow Computation,
PFG(2010), No. 1, 2010, pp. 5-16.
WWW Link. 1211
BibRef


Sommer, S.[Stefan],
Accelerating multi-scale flows for LDDKBM diffeomorphic registration,
CVGPU11(499-505).
IEEE DOI 1201
BibRef

Browne, T.A., Condell, J.V.[Joan V.], Prasad, G., McGinnity, T.M.,
An Investigation into Optical Flow Computation on FPGA Hardware,
IMVIP08(176-181).
IEEE DOI 0809
BibRef

Mizukami, Y.[Yoshiki], Tadamura, K.[Katsumi],
Optical Flow Computation on Compute Unified Device Architecture,
CIAP07(179-184).
IEEE DOI 0709
BibRef

Steinbrucker, F.[Frank], Pock, T.[Thomas], Cremers, D.[Daniel],
Large displacement optical flow computation without warping,
ICCV09(1609-1614).
IEEE DOI 0909
BibRef

Wedel, A.[Andreas], Cremers, D.[Daniel], Pock, T.[Thomas], Bischof, H.[Horst],
Structure- and motion-adaptive regularization for high accuracy optic flow,
ICCV09(1663-1668).
IEEE DOI 0909
BibRef

Werlberger, M.[Manuel], Pock, T.[Thomas], Unger, M.[Markus], Bischof, H.[Horst],
Optical Flow Guided TV-L1 Video Interpolation and Restoration,
EMMCVPR11(273-286).
Springer DOI 1107
BibRef

Ranftl, R.[René], Bredies, K.[Kristian], Pock, T.[Thomas],
Non-local Total Generalized Variation for Optical Flow Estimation,
ECCV14(I: 439-454).
Springer DOI 1408
BibRef

Werlberger, M.[Manuel], Pock, T.[Thomas], Bischof, H.[Horst],
Motion estimation with non-local total variation regularization,
CVPR10(2464-2471).
IEEE DOI 1006
BibRef

Santner, J.[Jakob], Werlberger, M.[Manuel], Mauthner, T.[Thomas], Paier, W.[Wolfgang], Bischof, H.[Horst],
FlowGames,
CVCGI10(25-31).
IEEE DOI 1006
BibRef

Werlberger, M.[Manuel], Trobin, W.[Werner], Pock, T.[Thomas], Wedel, A.[Andreas], Cremers, D.[Daniel], Bischof, H.[Horst],
Anisotropic Huber-L1 Optical Flow,
BMVC09(xx-yy).
PDF File. 0909
BibRef

Trobin, W.[Werner], Pock, T.[Thomas], Cremers, D.[Daniel], Bischof, H.[Horst],
An Unbiased Second-Order Prior for High-Accuracy Motion Estimation,
DAGM08(xx-yy).
Springer DOI 0806
BibRef

Zach, C.[Christopher], Pock, T.[Thomas], Bischof, H.[Horst],
A Globally Optimal Algorithm for Robust TV-L1 Range Image Integration,
ICCV07(1-8).
IEEE DOI 0710
BibRef
Earlier:
A Duality Based Approach for Realtime TV-L1 Optical Flow,
DAGM07(214-223).
Springer DOI 0709
BibRef

Zach, C.[Christopher],
A Novel Tree Block-Coordinate Method for MAP Inference,
GCPR15(320-330).
Springer DOI 1511
BibRef
And:
A Principled Approach for Coarse-to-Fine MAP Inference,
CVPR14(1330-1337)
IEEE DOI 1409
BibRef

Zach, C.[Christopher],
Gain-Adaptive KLT Tracking and TV-L1 optical flow on the GPU,
2010 Code, Optic Flow.
HTML Version. BibRef

Vidholm, E.[Erik], Sundqvist, P.[Per], Nystrom, I.[Ingela],
Accelerating the Computation of 3D Gradient Vector Flow Fields,
ICPR06(III: 677-680).
IEEE DOI 0609
BibRef

Bruhn, A.[Andrées], Weickert, J.[Joachim],
Towards Ultimate Motion Estimation: Combining Highest Accuracy with Real-Time Performance,
ICCV05(I: 749-755).
IEEE DOI 0510
Hierarchical application of technique of Brox ( See also High Accuracy Optical Flow Estimation Based on a Theory for Warping. ). BibRef

Niitsuma, H.[Hiroaki], Maruyama, T.[Tsutomu],
High Speed Computation of the Optical Flow,
CIAP05(287-295).
Springer DOI 0509
BibRef

Gong, S., and Brady, M.,
Parallel Computation of Optic Flow,
ECCV90(124-133).
Springer DOI Compute tangent and normal components using the Hessian. Propagate along curves. BibRef 9000

Wang, H., Brady, J.M., Page, I.,
A fast algorithm for computing optic flow and its implementation on a Transputer array,
BMVC90(xx-yy).
PDF File. 9009
BibRef

Chapter on Optical Flow Field Computations and Use continues in
Optic Flow Computation and Use, Other Approaches .


Last update:Sep 18, 2017 at 11:34:11