JOHANNES M. ZANKER (2005) Movement-induced motion signal distributions in outdoor scenes. Network: Computation in Neural Systems, 16 (4). pp. . ISSN 0954-898X
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The movement of an observer generates a characteristic field of velocity vectors on the retina (Gibson 1950). Because such optic flow-fields are useful for navigation, many theoretical, psychophysical and physiological studies have addressed the question how egomotion parameters such as direction of heading can be estimated from optic flow. Little is known, however, about the structure of optic flow under natural conditions. To address this issue, we recorded sequences of panoramic images along accurately defined paths in a variety of outdoor locations and used these sequences as input to a two-dimensional array of correlation-based motion detectors (2DMD). We find that (a) motion signal distributions are sparse and noisy with respect to local motion directions; (b) motion signal distributions contain patches (motion streaks) which are systematically oriented along the principal flowfield directions; (c) motion signal distributions showa distinct, dorso-ventral topography, reflecting the distance anisotropy of terrestrial environments; (d) the patiotemporal tuning of the local motion detector we used has little influence on the structure of motion signal distributions, at least for the range of conditions we tested; and (e) environmental motion is locally noisy throughout the visual field, with little spatial or temporal correlation; it can therefore be removed by temporal averaging and is largely over-ridden by image motion caused by observer movement. Our results suggest that spatial or temporal integration is important to retrieve reliable information on the local direction and size of motion vectors, because the structure of optic flowis clearly detectable in the temporal average of motion signal distributions. Egomotion parameters can be reliably retrieved from such averaged distributions under a range of environmental conditions. These observations raise a number of questions about the role of specific environmental and computational constraints in the processing of natural optic flow.
This is a Accepted version This version's date is: 12/2005 This item is peer reviewed
https://repository.royalholloway.ac.uk/items/5f45ce3f-0984-ccaa-1d52-d4be2f5a6f28/1/
Deposited by Al Dean (ZSRA118) on 22-Mar-2010 in Royal Holloway Research Online.Last modified on 03-Jan-2014
(C) 2005 Taylor & Francis, whose permission to mount this version for private study and research is acknowledged. The repository version is the author's final draft.
Adelson EH, Bergen JR. 1985. Spatiotemporal energy models for the perception of motion. J Opt Soc Am A2:284–299.
Adelson EH, Movshon JA. 1982. Phenomenal coherence of moving visual patterns. Nature 300:523–525.
Baddeleyn R, Abbottm LF, Booth MCA, Sengpiel F, Freeman T, Wakeman EA, Rolls ET. 1997. Responses of neurons in primary and inferior temporal visual cortices to natural scenes. Proc Royal Soc London B 264:1775– 1783.
Barlow HB, Olshausen BA. 2004. Convergent evidence for the visual analysis of optic flow through anisotropic attenuation of high spatial frequencies. J Vision 4:415–426.
Barron JL, Fleet DJ, Beauchemin SS. 1994. Performance of optical flow techniques. Int J Comp Vision 12:43–77.
Bayerl P, Neumann H. 2004. Disambiguating visual motion through contextual feedback modulation. Neural Computation 16:2041–2066.
Betsch BY, Einh¨auser W, K¨ording KP, K¨onig P. 2004. The world from a cat’s perspective—statistics of natural videos. Biol Cybernetics 90:41–50.
Boeddeker N, Lindemann JP, Egelhaaf M, Zeil J. 2005. Responses of blowfly motion-sensitive neurons to reconstructed optic flow along outdoor flight paths. J Comparative Physiol A xx:yy–zz .
Borst A, Egelhaaf M. 1989. Principles of visual motion detection. Trends Neurosci 12:297–306.
Borst A, Egelhaaf M. 1993. Detecting visual motion: Theory and models. In: Miles FA, Wallman J, editors. Visual motion and its role in the stabilization of gaze. Amsterdam: Elsevier. pp 3–27.
Burr DC. 1980. Motion smear. Nature 284:164–165.
Burr DC, Morgan MJ. 1997. Motion deblurring in human vision. Proc Royal Soc London, B 264:431–436.
Burr DC, Ross J. 2002. Direct evidence that ”speedlines” influence motion mechanisms. J Neurosci 22:8661–8664.
Chahl JS, Srinivasan MV. 1997. Reflective surfaces for panoramic imaging. App Optics 36:8275–8285.
Dahmen HJ, Franz MO, Krapp HG. 2001. Extracting egomotion from optic flow: limits of accuracy and neu585 ral matched filters. In: Zanker JM, Zeil J, editors. Motion vision—Computational, neural, and ecological constraints. New York: Springer. pp 143–168.
Dahmen HJ,W¨ ust RM, Zeil J. 1997. Extracting egomotion parameters from optic flow: principal limits for animals and machines. In: Srinivasan MV, Venkatesh S editors. From living eyes to seeing machines. Oxford: Oxford University Press. pp 174–198.
Dror RO, O’Carroll D, Laughlin SB. 2001. Accuracy of velocity estimation by Reichardt correlators. J Opt Soc Am A 18:241–252.
Eckert MP, Zeil J. 2001. Towards an ecology of motion vision. In:. Zanker JM, Zeil J, editors. Motion vision: Computational, neural and ecological constraints. New York: Springer Verlag. pp 333–369.
Egelhaaf M. 1991. How Do flies use visual motion information to control their course? Zool J Physiol 95:287–296.
Egelhaaf M, Borst A, Reichardt W. 1989. Computational structure of a biological motion-detection system as revealed by local detector analysis in the fly’s nervous system. J Opt Soc Am A 6:1070–1087.
Egelhaaf M, Grewe J, Kern R, Warzecha A-K. 2001. Outdoor performance of a motion-sensitive neuron in the blowfly. Vision Res 41:3627–3637
Fleet DJ, Langley K. 1995. Recursive filters for optical flow. IEEE Trans Pattern Analysis Machine Intell 17:61– 600 67.
Franzn MO, Chahl JS, Krapp HG. 2004. Insect-inspired estimation of egomotion. Neural Comput 16:2245–2260.
Franz MO, Krapp HG. 2000. Wide-field, motion-sensitive neurons and matched filters for optic flow fields. Biol Cybernetics 83:185–197.
Geisler WS. 1999. Motion streaks provide a spatial code for motion direction. Nature 400:65–69. Gibson JJ. 1950. The perception of the visual world. Cambridge, MA: The Riverside Press.
Hausen K, Egelhaaf M. 1989. Neural mechanisms of visual course control in insects. In: Stavenga DG, Hardie RC,editors. Facets of vision. Berlin: Springer Verlag. pp 391–424.
Heeger DJ. 1987. Model for the extraction of image flow. J Opt Soc Am A 4:1455–1471.
Hengstenberg R. 1993. Visual Stabilization in arthropods. In: Miles FA, Wallman J, editors. Multisensory control in insect oculomotor systems. Amsterdam: Elsevier Science Publishers. pp 285–298.
Hildreth E-C. 1984. The computation of the velocity field. Proc Royal Soc London B 221: 189–220. Hildreth E-C, Koch C. 1987. The analysis of visual motion: From computational theory to neuronal mechanisms.Ann Rev Neurosci 10:477–533.
Johnston A, McOwan PW, Benton CP. 1999. Robust velocity computation from a biologically motivated model of motion perception. Proc Royal Soc London B 266:509–518.
JungerW, Dahmen HJ. 1991. Response to self-motion in waterstriders: Visual discrimination between rotation and translation. J Comparative Physiol A 169:641–646. Koenderink JJ, Van Doorn AJ. 1987. Facts on optic flow. Biol Cybernetics 56:247–254.
Krapp HG, Hengstenberg R. 1996. Estimation of self-motion by optic flow processing in single visual interneurons.Nature 384:463–466.
Lappe M. 2000. Neuronal processing of optic flow. San Diego: Academic Press.
Lewen GD, Bialek W, De Ruyter van Steveninck R. 2001. Neural coding of naturalistic motion stimuli. Network: Comp Neural Systems 12:317–329.
Lindemann JP, Kern R,Michaelis C, Meyer P, Van Hateren JH, Egelhaaf M. 2003. FliMax, a novel stimulus device for panoramic and highspeed presentation of behaviourally generated optic flow. Vision Res 43:779–791.
Longuet-Higgins HC, Prazdny K. 1980. The interpretation of a moving retinal image. Proc Royal Soc London B 208:385–397.
Nakayama K, Silverman GH. 1988. The aperture problem I. Perception of nonrigidity and motion direction in translating sinusoidal lines. Vision Res 28:739–746.
Nalbach H-O. 1989. Three temporal frequency channels constitute the dynamics of the optokinetic system of the crab, Carcinus maenas. Biol Cybernetics 61:59–70.
Nalbach H-O, Nalbach G. 1987. Distribution of optokinetic sensitivity over the eye of crabs: its relation to habitat and possible role in flow-field analysis. J Comparative Phsiol A 160:127–135.
Nelson R, Aloimonos Y. 1988. Finding motion parameters from spherical flow fields (or the advantages of having eyes in the back of your head). Biol Cybernetics 58:261–273.
O’CarrollD, Bidwell NJ, Laughlin SB,Warrant EJ. 1996. Insect motion detectorsmatched to visual ecology.Nature 382:63–66.
O’Carroll D, Laughlin SB, Bidwell NJ, HarrismSJ. 1997. Spatio-Temporal properties of motion detectors matched to low image velocities in hovering insects. Vision Res 37:3427–3439.
Orban GA, Lagae L, Verri A, Raiguel S, Xiao D, Maes H, Torre V. 1992. First-order analysis of optical flow in monkey brain. Proc Nat Academy Sciences USA 89:2595–2599.
Passaglia CL, Dodge F, Herzog E, Jackson S, Barlow RB. 1997. Deciphering a neural code for vision. Proc Nat Academy Sciences USA 94:12649–12654.
Perrone JA. 1992. Model for the computation of self-motion in biological systems. J Opt Soc Am A 9:177–194.
Peters RA, Clifford CWG, Evans C. 2002. Measuring the structure of dynamic visual signals. Animal Behav 64:131–146.
Peters RA, Evans CS. 2003. Design of the Jacky dragon visual display: signal and noise characteristics in a complex moving environment. J Comparative Psychol 189:447–459.
Raffi M, Siegel RM. 2004. Multiple cortical representation of optic flow processing. In: Vaina LM, Beardsley SA,Rushton SK, editors. Optic flow and beyond. Amsterdam: Kluwer Academic Publishers. pp 3–22.
Reichardt W. 1987. Evaluation of optical motion information by movement detectors. J Comparative Physiol A 161:533–547.
Reichardt W, Egelhaaf M. 1988. Properties of individual movement detectors as derived from behavioural experiments on the visual system of the fly. Biol Cybernetics 58:287–294.
Reichardt W, Schl¨ogl RW, Egelhaaf M. 1988. Movement Detectors of the correlation type provide sufficient information for local computation of the 2-D velocity field. In: Haken H, editor. Neural and synergetic computers.Berlin, Heidelberg: Springer. pp 170–179.
Ross J. 2004. The perceived direction and speed of global motion in Glass pattern sequences. Vision Res 44:441–448.
Srinivasan MV. 1990. Generalized gradient schemes for the measurement of two-dimensional image motion. Biol Cybernetics 63:421–431.
SrinivasanbMV, Dvorak DR. 1980. Spatial processing of visual information in the movement-detecting pathway of the fly. J Comparative Physiol 140:1–23.
Torre V, Poggio T. 1978. A synaptic mechanism possibly underlying directional selectivity to motion. Proc Royal Soc London B 202:409–416.
Van Hateren JH. 1997. Processing of natural time series of intensities byt he visual system of the blowfly. Vision Res 37:3407–3416.
Van Hateren JH, Van der Schaaf A. 1996. Temporal properties of natural scenes. SPIE 2657:139–143. Van Santen JPH, Sperling G. 1985. Elaborated Reichardt detectors. J Opt Soc Am A 2:300–321.
Verri A, Straforini M, Torre V. 1992. Computational aspects of motion perception in natural and artificial vision systems. Phil Trans Royal Soc Biol 337:429–443.
Watson AB, Ahumada AJ. 1985. Model of human visual-motion sensing. J Opt Soc Am, A 2:322–342.
Weckstr¨om M. 1989. Light and dark adaption in fly photoreceptors: duration and time integral of the impulse response. Vision Res 29:1309–1317.
Wylie DR, Kripalani T, Frost BJ. 1993. Responses of pigeon vestibulocerebellar neurons to optokinetic stimulation. I. Functional organization of neurons discriminating between translational and rotational visual flow. J Neurophysiol 70:2632–2646.
Zanker JM. 1997. Is facilitation responsible for the ”motion induction” effect? Vision Res, 37:1953–1959.
Zanker JM. 2004. Looking at op art from a computational viewpoint. Spatial Vision 17:75–94.
Zanker JM, Braddick OJ. 1999. How does noise influence the estimation of speed? Vision Res 39:2411–2420.
Zanker JM, Zeil J. 2002. An Analysis of the motion signal distributions emerging from locomotion through a natural environment. In: B¨ ulthoff HH, Lee S-W, Poggio T, Wallraven C, editors. Biologically motivated computer vision 2002, Lecture Notes on Computer Vision 2525. Berlin, Heidelberg: Springer Verlag. pp 146–156.
Zeil J, Zanker JM. 1997. A glimpse into crabworld. Vision Res 37:3417–3426.