Abstract
Optical flow is the field of optical velocity vectors of the projected environmental surfaces whenever a viewing system moves relative to the viewed environment.
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References
J.J. Gibson, The perception of the visual world (Houghton Mifflin, Boston, 1950)
J.J. Gibson, Optical motions and transformations as stimuli for visual perception. Psychol. Rev. 64, 288–295 (1957)
K. Prazdny, On the information in optical flows. Comput. Vis. Graph. Image Process. 22, 239–259 (1983)
J.K. Aggarwal, N. Nandhakumar, On the computation of motion from a sequence of images: a review. Proc. IEEE 76, 917–935 (1988)
T. Huang, A. Netravali, Motion and structure from feature correspondences: a review. Proc. IEEE 82, 252–268 (1994)
A. Mitiche, Computational Analysis of Visual Motion (Plenum Press, New York, 1994)
R. Chellapa, A.A. Sawchuk, Digital Image Processing and Analysis: Volume 2: Digital Image Analysis (IEEE Computer Society Press, New York, 1985)
J.W. Roach, J.K. Aggarwal, Determining the movement of objects from a sequence of images. IEEE Trans. Pattern Anal. Mach. Intell. 2(6), 554–562 (1980)
R.Y. Tsai, T.S. Huang, Uniqueness and estimation of three-dimensional motion parameters of rigid objects with curved surfaces. IEEE Trans. Pattern Anal. Mach. Intell. 6(1), 13–27 (1984)
J. Aloimonos, C. Brown, Direct processing of curvilinear sensor motion from a sequence of perspective images, in IEEE Workshop on Computer Vision: Representation and Analysis, Annapolis, MD, 1984, pp. 72–77
S. Negahdaripour, B. Horn, Direct passive navigation. IEEE Trans. Pattern Anal. Mach. Intell. 9(1), 168–176 (1987)
B. Horn, E. Weldon, Direct methods for recovering motion. Int. J. Comput. Vis. 2(2), 51–76 (1988)
H.C. Longuet-Higgins, K. Prazdny, The interpretation of a moving retinal image. Proc. R. Soc. Lond. B 208, 385–397 (1980)
H.C. Longuet-Higgins, A computer algorithm for reconstructing a scene from two projections. Nature 293, 133–135 (1981)
X. Zhuang, R. Haralick, Rigid body motion and the optical flow image. in First International Conference on Artificial Intelligence Applications, 1984, pp. 366–375
S. Ullman, The interpretation of structure from motion. Proc. R. Soc. Lond. B 203, 405–426 (1979)
B. Horn, B. Schunck, Determining optical flow. Artif. Intell. 17, 185–203 (1981)
S. Solimini, J.M. Morel, Variational Methods in Image Segmentation (Springer, Boston, 2003)
S. Osher, N. Paragios (eds.), Geometric Level Set Methods in Imaging, Vision, and Graphics (Springer, New York, 2003)
G. Aubert, P. Kornpbrost, Mathematical Problems in Image Processing: Partial Differential Equations and the Calculus of Variations (Springer, New York, 2006)
A. Mitiche, I. Ben Ayed, Variational and Level Set Methods in Image Segmentation (Springer, New York, 2010)
A. Bruss, B. Horn, Passive navigation. Comput. Graph. Image Process. 21, 3–20 (1983)
G. Adiv, Determining three-dimensional motion and structure from optical flow generated by several moving objects. IEEE Trans. Pattern Anal. Mach. Intell. 7(4), 384–401 (1985)
B. Shahraray, M. Brown, Robust depth estimation from optical flow, in International Conference on Computer Vision, 1988, pp. 641–650
D. Heeger, A. Jepson, Subspace methods for recovering rigid motion I: algorithm and implementation. Int. J. Comput. Vis. 7(2), 95–117 (1992)
M. Taalebinezhaad, Direct recovery of motion and shape in the general case by fixation. IEEE Trans. Pattern Anal. Mach. Intell. 14(8), 847–853 (1992)
E. De Micheli, F. Giachero, Motion and structure from one dimensional optical flow, in IEEE International Conference on Computer Vision and, Pattern Recognition, 1994, pp. 962–965
N. Gupta, N. Kanal, 3-D motion estimation from motion field. Artif. Intell. 78, 45–86 (1995)
Y. Xiong, S. Shafer, Dense structure from a dense optical flow, in International Conference on Computer Vision and Image Understanding, 1998, pp. 222–245
Y. Hung, H. Ho, A Kalman filter approach to direct depth estimation incorporating surface structure. IEEE Trans. Pattern Anal. Mach. Intell. 21(6), 570–575 (1999)
S. Srinivasan, Extracting structure from optical flow using the fast error search technique. Int. J. Comput. Vis. 37(3), 203–230 (2000)
T. Brodsky, C. Fermuller, Y. Aloimonos, Structure from motion: beyond the epipolar constraint. Int. J. Comput. Vis. 37(3), 231–258 (2000)
H. Liu, R. Chellapa, A. Rosenfeld, A hierarchical approach for obtaining structure from two-frame optical flow, in IEEE Workshop on Motion and Video, Computing, 2002
W. MacLean, A. Jepson, R. Frecher, Recovery of egomotion and segmentation of independent object motion using the em algorithm. British Mach. Vis. Conf. BMVC 94, 13–16 (1994)
S. Fejes,L. Davis, What can projections of flow fields tell us about visual motion, in International Conference on Computer Vision, 1998, pp. 979–986
J. Weber, J. Malik, Rigid body segmentation and shape description from dense optical flow under weak perspective. IEEE Trans. Pattern Anal. Mach. Intell. 19(2), 139–143 (1997)
A. Mitiche, S. Hadjres, MDL estimation of a dense map of relative depth and 3D motion from a temporal sequence of images. Pattern Anal. Appl. 6, 78–87 (2003)
H. Sekkati, A. Mitiche, A variational method for the recovery of dense 3D structure from motion. Robotics and Autonomous Systems 55(7), 597–607 (2007)
H. Sekkati, A. Mitiche, Concurrent 3D-motion segmentation and 3D interpretation of temporal sequences of monocular images. IEEE Trans. Image Process. 15(3), 641–653 (2006)
A. Mitiche, H. Sekkati, Optical flow 3D segmentation and interpretation: a variational method with active curve evolution and level sets. IEEE Trans. Pattern Anal. Mach. Intell. 28(11), 1818–1829 (2006)
H. Sekkati, A. Mitiche, Joint optical flow estimation, segmentation, and 3D interpretation with level sets. Comput. Vis. Image Underst. 103(2), 89–100 (2006)
Y.G. Leclerc, Constructing simple stable descriptions for image partitioning. Int. J. Comput. Vis. 3(1), 73–102 (1989)
S. Vedula, S. Baker, P. Rander, R. Collins, T. Kanade, Three-dimensional scene flow. IEEE Trans. Pattern Anal. Mach. Intell. 27, 475–480 (2005)
J.-P. Pons, R. Keriven, O. Faugeras, G. Hermosillo, Variational stereovision and 3D scene flow estimation with statistical similarity measures, in International Conference on Computer Vision, Nice, France, 2003, pp. 597–602
Y. Zhang, C. Kambhamettu, Integrated 3D scene flow and structure recovery from multiview image sequences, in IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 674–681 (2000)
F. Huguet, F. Devernay, A variational method for scene flow estimation from stereo sequences, in Proceedings of the International Conference on Computer Vision, Rio de Janeiro, Brasil: IEEE, Oct 2007
A. Wedel, C. Rabe, T. Vaudrey, T. Brox, U. Franke, D. Cremers, Efficient dense scene flow from sparse or dense stereo data, in European Conference on Computer Vision (ECCV), Marseille, France, Oct. 2008
F. Raudies, H. Neumann, A review and evaluation of methods estimating ego-motion. Comput. Vis. Image Underst. 116(5), 606–633 (2012)
F. Raudies, H. Neumann, An efficient linear method for the estimation of ego-motion from optical flow, in DAGM-Symposium, 2009, pp. 11–20
G.E. Forsyth, A.A. Malcolm, C.B. Moler, Computer Methods for Mathematical Computations (Prentice Hall, Englewood Cliffs, 1977)
S. Negahdaripour, B. Horn, Direct Passive Navigation (Massachusetts Institute of Technology Memo 821, Cambridge, 1985)
S. Negahdaripour, A. Yuille, Direct Passive Navigation: Analytical Solution for Quadratic Patches (Massachusetts Institute of Technology Memo 876, Cambridge, 1986)
G. Aubert, G. Deriche, P. Kornprobst, Computing optical flow via variational thechniques. SIAM J. Appl. Math. 60(1), 156–182 (1999)
H. Sekkati, A. Mitiche, Dense 3D interpretation of image sequences: A variational approach using anisotropic diffusion, in International Conference on Image Analysis and Processing, Mantova, Italy, 2003, pp. 424–429
T. Nir, A.M. Bruckstein, R. Kimmel, Over-parameterized variational optical flow. Int. J. Comput. Vis. 76(2), 205–216 (2008)
R. Deriche, P. Kornprobst, G. Aubert, Optical-flow estimation while preserving its discontinuities: a variational approach, in Asian Conference on Computer Vision, 1995, pp. 71–80
J. Oliensis, A critique of structure-from-motion algorithms. Comput. Vis. Image Underst. 80(2), 172–214 (2000)
S. Bougnoux, From projective to euclidean space under any practical situation, a criticism of self-calibration, in ICCV, 1998, pp. 790–798
E. Dubois, A projection method to generate anaglyph stereo images, in International Conference on Acoustics, Speech, and Signal Processing, 2001 vol. III, pp. 1661–1664
S. Osher, N. Paragios, Geometric Level Set Methods in Imaging, Vision, and Graphics (Birkhauser, Berlin, 1995)
R. Feghali, A. Mitiche, Fast computation of a boundary preserving estimate of optical flow. SME Vis. Q. 17(3), 1–4 (2001)
J. Stoer, P. Burlisch, Introduction to Numerical Methods, 2nd ed. (Springer, New York, 1993)
P. Ciarlet, Introduction à l’analyse numérique matricielle et à l’optimisation (Masson, Fifth, 1994)
C. Debrunner, N. Ahuja, Segmentation and factorization-based motion and structure estimation for long image sequences. IEEE Trans. Pattern Anal. Mach. Intell. 20(2), 206–211 (1998)
A. Mitiche, On combining stereopsis and kineopsis for space perception, in IEEE Conference on Artificial Intelligence Applications, 1984, pp. 156–160
A. Mitiche, A computational approach to the fusion of stereopsis and kineopsis, in Motion Understanding: Robot and Human Vision, ed. by W.N. Martin, J.K. Aggarwal ( Kluwer Academic Publishers, Norwell1988), pp. 81–99
A. Mitiche, Y. Mathlouthi, I. Ben Ayed, A linear method for scene flow estimation from a single image sequence, in INRS-EMT Technical report, 2011
A. Mitiche, A. Mansouri, On convergence of the Horn and Schunck optical flow estimation method. IEEE Trans. Image Process. 13(6), 848–852 (2004)
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Mitiche, A., Aggarwal, J. (2014). Optical Flow Three-Dimensional Interpretation. In: Computer Vision Analysis of Image Motion by Variational Methods. Springer Topics in Signal Processing, vol 10. Springer, Cham. https://doi.org/10.1007/978-3-319-00711-3_6
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