18.6.1 Motion with Optical Flow and Depth

Chapter Contents (Back)
Optical Flow. Fusion. Motion and Depth.

Haralick, R.M.[Robert M.], and Zhuang, X.H.[Xin-Hua],
A Note on 'Rigid Body Motion from Depth and Optical Flow,',
CVGIP(34), No. 3, June 1986, pp. 372-387.
WWW Link. A comment on ( See also Rigid Body Motion from Depth and Optic Flow. ) that computed the motion parameters. The claim is that there is a theoretical solution given 4 uncoplanar points, with optical flow and depth. BibRef 8606

Zhuang, X.H.[Xin-Hua], Haralick, R.M.[Robert M.], and Zhao, Y.,
From Depth and Optical Flow to Rigid Body Motion,
IEEE DOI Builds on the other papers by the authors. BibRef 8800

Haralick, R.M.[Robert M.], and Lee, J.S.[Jong Soo],
The Facet Approach to Optic Flow,
DARPA83(84-93). Facet approach: filter the local neighborhood and interpret the processing in terms of how it affects the best fit function (from the filtering). By assuming gray tone partial in row, column and time being equal for the corresponding point you can find the matching point (also intensity is the same). (Of course you can.) See also On the Estimation of Optical Flow: Relations between Different Approaches and Some New Results. BibRef 8300

Zhuang, X.H.[Xin-Hua], Huang, T.S.[Thomas S.], Ahuja, N.[Narendra], Haralick, R.M.[Robert M.],
A Simplified Linear Optical Flow-Motion Algorithm,
CVGIP(42), No. 3, June 1988, pp. 334-344.
WWW Link. BibRef 8806
Rigid Body Motion and the Optic Flow Image,
CAIA84(366-375). Given the optical flow, establish the rigid body motion with only instantaneous rotation and translation. BibRef

Ballard, D.H.[Dana H], Kimball, O.A.,
Rigid Body Motion from Depth and Optic Flow,
CVGIP(22), No. 1, April 1983, pp. 95-115.
WWW Link. Hough. Hough technique to determine body motion. See the Haralick comment above. This uses 3-D optical flow and acceleration of points. The 5-D Hough space is generated. BibRef 8304

Scheuing, A., Niemann, H.,
Computing Depth from Stereo Images by Using Optical Flow,
PRL(4), 1986, pp. 205-212. BibRef 8600

Tistarelli, M., and Vernon, D.,
Using Camera Motion to Estimate Range for Robotic Parts Manipulation,
RA(6), No. 5, December 1990, pp. 509-521. Depth from Motion. Active Vision. Estimate depth and structure from controlled motion of a robot arm. BibRef 9012

Grosso, E., Tistarelli, M.,
Active Dynamic Stereo Vision,
PAMI(17), No. 11, November 1995, pp. 1117-1128.
IEEE Top Reference. Originally printed as: BibRef 9511 PAMI(17), No. 9, September 1995, pp. 868-879.
IEEE DOI But there were errors in the printing. Motion and stereo to determine structure and free space. See also Dynamic Aspects in Active Vision. BibRef

Grosso, E., Sandini, G., and Tistarelli, M.,
3-D Object Reconstruction Using Stereo and Motion,
SMC(19), No. 6, Nov/Dec 1989, pp. 1465-1476. Shape from Motion. 3D Reconstruction. Control the motion and extract depth from both stereo and motion. This gives more constraints on the final results. BibRef 8911

Grosso, E., Tistarelli, M., and Sandini, G.,
Active/Dynamic Stereo for Navigation,
Springer DOI Dynamic Stereo. BibRef 9200

Tistarelli, M., Grosso, E., and Sandini, G.,
Dynamic Stereo in Visual Navigation,
IEEE DOI Depth from stereo and Optical flow. BibRef 9100

Grosso, E., and Tistarelli, M.,
Active/Dynamic Stereo: A General Framework,
IEEE DOI BibRef 9300

Sandini, G., and Tistarelli, M.,
Active Tracking Strategy for Monocular Depth Inference over Multiple Frames,
PAMI(12), No. 1, January 1990, pp. 13-27.
IEEE DOI Depth is inferred from motion of edge contours over time (axial motion). Uses two sampling rates, one to get instantaneous optical flow, the other to derive depth. BibRef 9001

Akgul, Y.S.[Yusuf Sinan], Kambhamettu, C.[Chandra],
A coarse-to-fine deformable contour optimization framework,
PAMI(25), No. 2, February 2003, pp. 174-186.
A New Multi-Level Framework for Deformable Contour Optimization,
CVPR99(II: 465-470).
A Scale-Space Approach for Deformable Contour Optimization,
ScaleSpace99(410-422). Used in: See also Extracting Nonrigid Motion and 3D Structure of Hurricanes from Satellite Image Sequences without Correspondences. BibRef

Palaniappan, K., Kambhamettu, C.[Chandra], Hasler, F.[Frederick], Goldgof, D.[Dmitry],
Structure and Semi-Fluid Motion Analysis of Stereoscopic Satellite Images for Cloud Tracking,
IEEE DOI BibRef 9500

Khamene, A.[Ali], Negahdaripour, S.[Shahriar],
Motion and structure from multiple cues; image motion, shading flow, and stereo disparity,
CVIU(90), No. 1, April 2003, pp. 99-127.
WWW Link. 0306

Zhang, Y.[Ye], Kambhamettu, C.[Chandra],
On 3-D scene flow and structure recovery from multiview image sequences,
SMC-B(33), No. 4, August 2003, pp. 592-606.
IEEE Abstract. 0308
On 3D Scene Flow and Structure Estimation,
Integrated 3D Scene Flow and Structure Recovery from Multiview Image Sequences,
CVPR00(II: 674-681).

Demirdjian, D., Darrell, T.J.,
Using Multiple-Hypothesis Disparity Maps and Image Velocity for 3-D Motion Estimation,
IJCV(47), No. 1-3, April-June 2002, pp. 219-228.
DOI Link 0203
Earlier: SMBV01(xx-yy). 0110

Schmudderich, J., Willert, V.[Volker], Eggert, J.[Julian], Rebhan, S., Goerick, C., Sagerer, G., Korner, E.,
Estimating Object Proper Motion Using Optical Flow, Kinematics, and Depth Information,
SMC-B(37), No. 4, August 2008, pp. 1139-1151.

Hung, C.H.[Chun Ho], Xu, L.[Li], Jia, J.Y.[Jia-Ya],
Consistent Binocular Depth and Scene Flow with Chained Temporal Profiles,
IJCV(102), No. 1-3, March 2013, pp. 271-292.
WWW Link. 1303
input of a binocular video BibRef

Lukins, T.C., Fisher, R.B.,
Colour Constrained 4D Flow,
HTML Version. 0509
Add color to flow computations. BibRef

Yoda, I.[Ikushi], and Sakaue, K.[Katsuhiko],
Utilization of Stereo Disparity and Optical Flow Information for Human Interaction,
IEEE DOI BibRef 9800

Sudhir, G., Banerjee, S., Biswas, K.K., Bahl, R.,
A Cooperative Integration of Stereopsis and Optic Flow Computation,
IEEE DOI BibRef 9400

Moezzi, S., Bartlett, S.L., and Weymouth, T.E.,
The Camera Stability Problem and Dynamic Stereo Vision,
IEEE DOI Match depth maps. BibRef 9100

Moezzi, S., and Weymouth, T.E.,
A model for fusion of spatial information in dynamic vision,
CRA90(II: 1148-1153). BibRef 9000

Weymouth, T.E., Moezzi, S.,
Wide Base-Line Dynamic Stereo: Approximation and Refinement,
IEEE DOI BibRef 8800

Chapter on Motion Analysis -- Low-Level, Image Level Analysis, Mosaic Generation, Super Resolution, Shape from Motion continues in
Motion, Shape from Motion for RGB-D Sensors, Kinect Motion .

Last update:Jun 22, 2017 at 17:22:14