Abstract
The velocity field that represents the motion of object points across an image is called the optical flow field. Optical flow results from relative motion between a camera and objects in the scene. One class of techniques for the estimation of optical flow utilizes a relationship between the motion of surfaces and the derivatives of image brightness (Limb and Murphy, 1975; Cafforio and Rocca, 1976; Fennema and Thompson, 1979; Netravali and Robbins, 1979; Schalkoff, 1979; Lucas and Kanade, 1981; Schunck and Horn, 1981; Thompson and Barnard, 1981; and Schalkoff and McVey, 1982). The major difficulty with gradient-based methods is their sensitivity to conditions commonly encountered in real imagery. Highly textured regions, motion boundaries, and depth discontinuities can all be troublesome for gradient-based methods. Fortunately, the areas characterized by these difficult conditions are usually small and localized.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Barnard, S.T., (1979) ‘The image correspondence problem,’ Computer Science Department, University of Minnesota, PhD. Dissertation.
Cafforio, C., and Rocca, F., (1976) ‘Methods for measuring small displacements of television images,’ IEEE Trans. on Information Theory vol. IT-22, pp. 573–579.
Cornelius, N., and Kanade, T., (1983) ‘Adapting optical-flow to measure object motion in reflectance and X-ray image sequences,’ ACM Interdisciplinary Workshop on Motion: Representation and PerceptionToronto, Canada.
Fennema, C.L., and Thompson, W.B., (1979) ‘Velocity determination in scenes containing several moving objects,’ Computer Graphics and Image Processingvol. 9, pp. 301–315.
Horn, B.K.P., and Schunck, B., (1981) ‘Determining optical flow,’ Artificial Intelligence, vol. 17, pp. 185–203.
Ikeuchi, K., and Horn, B.K.P., (1979) ‘An application of the photometric stereo method,’ Proc. 6th Int. Joint Conf on Artificial Intelligence, pp. 413–415.
Kearney, J.K., (1983) ‘Gradient-based estimation of optical flow,’ University of Minnesota, PhD. Dissertation.
Kearney, J.K., Thompson, W.B., and Boley, D.L., (1982) ‘Gradient-based estimation of disparity,’ Proc. IEEE Conf. on Pattern Recognition and Image Processing.
Limb, J.O., and Murphy, J.A., (1975) ‘Estimating the velocity of moving images in television signals,’ Computer Graphics and Image Processing, vol. 4, pp. 311–327.
Lucas, B.D., and Kanade, T., (1981) ‘An iterative image registration technique with an application to stereo vision,’ Proc. of the 5th Joint Conf. on Artificial Intelligence pp. 674–679.
Moravec, H.P., (1980) ‘Obstacle avoidance and navigation in the real world by a seeing robot rover,’ Stanford University, PhD. Dissertation.
Netravali, A.N., and Robbins, J.D., (1979) ‘Motion-compensated television coding: part I,’ The Bell System Tech. J., vol. 58 no. 3.
Paquin, R., and Dubois, E., (1983) ‘A spatio-temporal gradient method for estimating the displacement field in time-varying imagery,’ Computer Vision, Graphics and Image Processing vol. 21, no. 2, pp. 205–221.
Schalkoff, R.J., (1979) ‘Algorithms for a real-time automatic video tracking system,’ University of Virginia, PhD. Dissertation.
Schalkoff, R.J., and McVey, E.S., (1982) ‘A model and tracking algorithm for a class of video targets,’ IEEE Trans. on Pattern Analysis and Machine Intelligence vol. PAMI 1–4, no. 1, pp. 2–10.
Schunck, B.G., (1985) ‘Image flow: Fundamentals and future research,’ Proc. IEEE Conf. on Pattern Recognition and Image Processing, pp. 560–571.
Schunck, B.G., and Horn, B.K.P., (1981) ‘Constraints on optical flow,’ Proc. IEEE Conf. on Pattern Recognition and Image Processing, pp. 205–210.
Thompson, W.B., and Barnard, S.T., (1981) ‘Low-level estimation and interpretation of visual motion,’ Computer, vol. 14, no. 8, pp. 20–28.
Yachida, M., (1983) ‘Determining velocity maps by spatio-temporal neighborhoods from image sequences,’ Computer Vision, Graphics and Image Processing, vol. 21 no. 2, pp. 262–279.
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1988 Kluwer Academic Publishers
About this chapter
Cite this chapter
Kearney, J.K., Thompson, W.B. (1988). Bounding Constraint Propagation for Optical Flow Estimation. In: Martin, W.N., Aggarwal, J.K. (eds) Motion Understanding. The Kluwer International Series in Engineering and Computer Science, vol 44. Springer, Boston, MA. https://doi.org/10.1007/978-1-4613-1071-6_1
Download citation
DOI: https://doi.org/10.1007/978-1-4613-1071-6_1
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4612-8413-0
Online ISBN: 978-1-4613-1071-6
eBook Packages: Springer Book Archive