Skip to main content
Log in

An image-interpolation technique for the computation of optic flow and egomotion

  • Published:
Biological Cybernetics Aims and scope Submit manuscript

Abstract

A technique for measuring the motion of a rigid, textured plane in the frontoparallel plane is developed and tested on synthetic and real image sequences. The parameters of motion — translation in two dimensions, and rotation about a previously unspecified axis perpendicular to the plane — are computed by a single-stage, non-iterative process which interpolates the position of the moving image with respect to a set of reference images. The method can be extended to measure additional parameters of motion, such as expansion or shear. Advantages of the technique are that it does not require tracking of features, measurement of local image velocities or computation of high-order spatial or temporal derivatives of the image. The technique is robust to noise, and it offers a simple, novel way of tackling the ‘aperture’ problem. An application to the computation of robot egomotion is also described.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  • Adelson EH, Bergen JR (1985) Spatiotemporal energy models for the perception of motion. J Opt Soc Am [A] 2:284–299

    Google Scholar 

  • Adelson EH, Movshon JA (1982) Phenomenal coherence of moving visual patterns. Nature 300:523–525

    Google Scholar 

  • Alliney S (1993) Digital analysis of rotated images. IEEE Trans Patt Anal Mach Intell 15:499–504

    Google Scholar 

  • Bouzerdoum A, Pinter RB (1989) Image motion processing in biological and computer vision systems. Proc SPIE 1199, IV:1230–1240

    Google Scholar 

  • Chahl JS, Srinivasan MV (1993) Visual computation of egomotion using an image interpolation technique. Proc. Australian and New Zealand Conference on Intelligent Information Processing Systems, Perth, pp 372—376

  • De Castro E, Morandi C (1987) Registration of translated and rotated images using finite fourier transforms. IEEE Trans Patt Anal Mach Intell 9:700–703

    Google Scholar 

  • De Micheli E, Torre V, Uras S (1993) The accuracy of the computation of optical flow and of the recovery of motion parameters. IEEE Trans Patt Anal Mach Intell 15:434–447

    Google Scholar 

  • Fenema CL, Thompson WB (1979) Velocity determination in scenes containing several moving objects. Comput Graph Image Process 9:301–315

    Google Scholar 

  • Frost BJ, Wylie DR, Wang YC (1990) The processing of object and self-motion in the tectofugal and accessory optic pathways of birds. Vision Res 30:1677–1688

    Google Scholar 

  • Gibson JJ (1950) The perception of the visual world. Houghton Mifflin, Boston

    Google Scholar 

  • Goshtasby A (1985) Template matching in rotated images. IEEE Trans Patt Anal Mach Intell 7:338–344

    Google Scholar 

  • Heeger DJ (1987) Model for the extraction of image flow. J Opt Soc Am [A] 4:1455–1471

    Google Scholar 

  • Hildreth EC (1984) The measurement of visual motion. MIT Press, Cambridge

    Google Scholar 

  • Hildreth EC, Koch C (1987) The analysis of visual motion: from computational theory to neuronal mechanisms. Annu Rev Neurosci 10:477–533

    Google Scholar 

  • Hong J, Tan X, Pinette B, Weiss R, Riseman EM (1991) Image-based homing. Proc IEEE International Conference on Robotics and Automation, pp 620–625

  • Horn BKP, Schunck B (1981) Determining optical flow. Artif Intell 17:185–203

    Google Scholar 

  • Horridge GA (1987) The evolution of visual processing and the construction of seeing systems. Proc R Soc Lond B 230:279–292

    Google Scholar 

  • Kearney JK, Thompson WB, Boley DL (1987) Optical flow estimation: An error analysis of gradient-based methods with local optimization. IEEE Trans Patt Anal Mach Intell PAMI-8:229–244

    Google Scholar 

  • Kersten D, O'Toole AJ, Sereno ME, Knill DC, Anderson JA (1987) Associative learning of scene parameters from images Appl Optics 26:4999–5006

    Google Scholar 

  • Limb JO, Murphy JA (1975) Estimating the velocity of moving objects in television signals. Comput Graph Image Process 4:311–327

    Google Scholar 

  • Marr D, Ullman S (1981) Directional selectivity and its use in early visual processing. Proc R Soc Lond [Biol] 211:151–180

    Google Scholar 

  • Murray DW, Buxton BF (1990) Experiments in the machine interpretation of visual motion. MIT Press, Cambridge, Mass.

    Google Scholar 

  • Nagel H, Enkelmann W (1986) An investigation of smoothness constraints for the estimation of displacement vector fields from image sequences. Comput Vis Graph Image Process 21:85–117

    Google Scholar 

  • Nakayama K (1985) Biological image motion processing: a review. Vision Res 25:625–660

    Google Scholar 

  • Nelson RC, Aloimonos J (1988) Finding motion parameters from spherical motion fields (or the advantages of having eyes in the back of your head). Biol Cybern 58:261–273

    Google Scholar 

  • Perrone JA (1990) Simple technique for optical flow estimation. J Opt Soc Am [A] 7:264–278

    Google Scholar 

  • Perrone JA (1992) Model for the computation of self-motion in biological systems. J Opt Soc Am [A] 9:177–194

    Google Scholar 

  • Pichon JM, Blanes C, Franceschini N (1989) Visual guidance of a mobile robot equipped with a network of self-motion sensors. Proc SPIE 1195:44–53

    Google Scholar 

  • Reichardt W, Egelhaaf M, Schloegl RW (1988) Movement detectors of the correlation type provide sufficient information for local computation of 2D velocity field. Naturwissenschaften 75:313–315

    Google Scholar 

  • Sereno MI, Sereno ME (1990) Learning to discriminate senses of rotation and dilation with a Hebb rule. Invest Ophthalmol Vis Sci [Suppl] 31:528

    Google Scholar 

  • Sobey P, Srinivasan MV (1991) Measurement of optical flow using a generalized gradient scheme. J Opt Soc Am [A] 8:1488–1498

    Google Scholar 

  • Srinivasan MV (1990) Generalized gradient schemes for the measurement of two-dimensional image motion. Biol Cybern 63:421–431

    Google Scholar 

  • Srinivasan MV (1993) An image-interpolation technique for the computation of 2-D motion. Proc. Australian and New Zealand Conference on Intelligent Information Processing Systems, Perth, pp 367–371

  • Srinivasan MV (1994) Generalised gradient versus image interpolation: A critical evaluation of two schemes for measurement of image motion. Aust J Intell Info Proc Syst 1:41–50

    Google Scholar 

  • Taichi Wang H, Mathur B, Koch C (1989) Computing optical flow in the primate visual system. Neural Comput 1:92–103

    Google Scholar 

  • Tanaka K, Yoshiro F, Saito H (1989) Underlying mechanisms of the response specificity of expansion/contraction and rotation cells in the dorsal part of the medial superior temporal area of the macaque monkey. J Neurophysiol 62:642–656

    Google Scholar 

  • Ullman S (1979) The interpretation of visual motion. MIT Press, Cambridge, Mass.

    Google Scholar 

  • Uras S, Girosi F, Verri A, Torre V (1988) A computational approach to motion perception. Biol Cybern 60:79–87

    Google Scholar 

  • Wallach H (1976) On perceived identity. 1. The direction of motion of straight lines. In: Wallach H (ed) On perception. Quadrangle Press, New York

    Google Scholar 

  • Watson AB, Ahumada AJ (1987) Model of human visual-motion sensing. J Opt Soc Am [A] 2:284–299

    Google Scholar 

  • Wehner R, Srinivasan MV (1981) Searching behaviour of desert ants, genus Cataglyphis. J Comp Physiol 142:315–338

    Google Scholar 

  • Werkhoven P, Koenderink JJ (1990) Extraction of motion parallax structure in the visual system. I, II. Biol Cybern 63:193–199

    Google Scholar 

  • Wolberg G (1990) Digital image warping. IEEE Computer Society Press Monograph, Los Alamitos

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

About this article

Cite this article

Srinivasan, M.V. An image-interpolation technique for the computation of optic flow and egomotion. Biol. Cybern. 71, 401–415 (1994). https://doi.org/10.1007/BF00198917

Download citation

  • Received:

  • Accepted:

  • Issue Date:

  • DOI: https://doi.org/10.1007/BF00198917

Keywords

Navigation