Hostname: page-component-586b7cd67f-t7czq Total loading time: 0 Render date: 2024-11-23T23:01:53.598Z Has data issue: false hasContentIssue false
  • English
  • Français

Contour statistics in natural images: Grouping across occlusions

Published online by Cambridge University Press:  01 January 2009

WILSON S. GEISLER  and
JEFFREY S. PERRY
WILSON S. GEISLER*
Affiliation:
Center for Perceptual Systems and Department of Psychology, University of Texas at Austin, Austin TX
JEFFREY S. PERRY
Affiliation:
Center for Perceptual Systems and Department of Psychology, University of Texas at Austin, Austin TX
*
*Address correspondence and reprint requests to: Wilson S. Geisler, Center for Perceptual Systems, 1 University Station A8000, University of Texas at Austin, Austin, TX 78712. E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

Correctly interpreting a natural image requires dealing properly with the effects of occlusion, and hence, contour grouping across occlusions is a major component of many natural visual tasks. To better understand the mechanisms of contour grouping across occlusions, we (a) measured the pair-wise statistics of edge elements from contours in natural images, as a function of edge element geometry and contrast polarity, (b) derived the ideal Bayesian observer for a contour occlusion task where the stimuli were extracted directly from natural images, and then (c) measured human performance in the same contour occlusion task. In addition to discovering new statistical properties of natural contours, we found that naïve human observers closely parallel ideal performance in our contour occlusion task. In fact, there was no region of the four-dimensional stimulus space (three geometry dimensions and one contrast dimension) where humans did not closely parallel the performance of the ideal observer (i.e., efficiency was approximately constant over the entire space). These results reject many other contour grouping hypotheses and strongly suggest that the neural mechanisms of contour grouping are tightly related to the statistical properties of contours in natural images.

Type
Natural Scene Statistics and Natural Tasks
Information
Visual Neuroscience , Volume 26 , Issue 1 , January 2009 , pp. 109 - 121
Copyright
Copyright © Cambridge University Press 2009

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Barrow, H.G. & Tenenbaum, J.M. (1986). Computational approaches to vision. In Handbook of Perception and Human Performance, Vol. II: Cognitive Processes and Performance, ed. Boff, K.R., Kaufman, L. & Thomas, J.P., pp. 38:31–38:68. New York: John Wiley and Sons.Google Scholar
Beck, J., Rosenfeld, A. & Ivry, R. (1989). Line segregation. Spatial Vision 4, 75–101.Google ScholarPubMed
Brunswik, E. & Kamiya, J. (1953). Ecological cue-validity of ‘proximity’ and other Gestalt factors. American Journal of Psychology 66, 20–32.CrossRefGoogle ScholarPubMed
Elder, J.H. & Goldberg, R.M. (2002). Ecological statistics of Gestalt laws for the perceptual organization of contours. Journal of Vision 2, 324–353.CrossRefGoogle ScholarPubMed
Feldman, J. (2001). Bayesian contour integration. Perception & Psychophysics 63, 1171–1182.CrossRefGoogle ScholarPubMed
Field, D.J., Hayes, A. & Hess, R.F. (1993). Contour integration by the human visual system: Evidence for a local ‘association field’. Vision Research 33, 173–193.CrossRefGoogle ScholarPubMed
Field, D.J., Hayes, A. & Hess, R.F. (2000). The role of polarity and symmetry in perceptual grouping of contour fragments. Spatial Vision 13, 51–66.Google Scholar
Geisler, W.S. (2008). Visual perception and the statistical properties of natural scenes. Annual Review of Psychology 59, 10.11–10.26.CrossRefGoogle ScholarPubMed
Geisler, W.S., Perry, J.S. & Ing, A.D. (2008). Natural systems analysis. In Human Vision and Electronic Imaging III, ed. Rogowitz, B.E. & Pappas, T.N., Proc. of SPIEIS & T Electronic Imaging, SPIE Vol. 68060M-1–11.Google Scholar
Geisler, W.S., Perry, J.S., Super, B.J. & Gallogly, D.P. (2001). Edge co-occurrence in natural images predicts contour grouping performance. Vision Research 41, 711–724.CrossRefGoogle ScholarPubMed
Grossberg, C.M. & Mingolla, E. (1985). Neural dynamics of form perception: Boundary completion, illusory figures, and neon color spreading. Psychological Review 92, 173–211.CrossRefGoogle ScholarPubMed
Kellman, P.J. (2003). Interpolation processes in the visual perception of objects. Neural Networks 16, 915–923.CrossRefGoogle ScholarPubMed
Kellman, P.J. & Shipley, T.F. (1991). A theory of visual interpolation in object perception. Cognitive Psychology 23, 141–221.CrossRefGoogle ScholarPubMed
Kovacs, I. & Julesz, B. (1993). A closed curve is much more than an incomplete one: Effect of closure in figure-ground segmentation. Proceedings of the National Academy of Sciences 90, 7495–7497.CrossRefGoogle ScholarPubMed
Martin, D., Fowlkes, C. & Malik, J. (2004). Learning to detect natural image boundaries using local brightness, color and texture cues. IEEE Transactions on Pattern Analysis and Machine Intelligence 26, 530–549.CrossRefGoogle ScholarPubMed
Neumann, H. & Mingolla, E. (2001). Computational models of spatial integration in perceptual grouping. In From Fragments to Objects: Grouping and Segmentation in Vision, ed. Shipley, T.F. & Kellman, P.J., pp. 353–400. Amsterdam, the Netherlands: Elsevier.CrossRefGoogle Scholar
Parent, P. & Zucker, S. (1989). Trace inference, curvature consistency and curve detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 11, 823–839.CrossRefGoogle Scholar
Rock, I. (1975). An Introduction to Perception. New York: Macmillan.Google Scholar
Sha'ashua, S. & Ullman, S. (1988). Structural saliency: The detection of globally salient structures using a locally connected network. Proceedings of the Second International Conference on Computer Vision, 321–327.Google Scholar
Sigman, M., Cecchi, G.A., Gilbert, C.D. & Magnasco, M.O. (2001). On a common circle: Natural scenes and Gestalt rules. Proceedings of the National Academy of Sciences 98, 1935–1940.CrossRefGoogle ScholarPubMed
Singh, M. & Fulvio, J.M. (2005). Visual extrapolation of contour geometry. PNAS 102, 939–944.CrossRefGoogle ScholarPubMed
Singh, M. & Fulvio, J.M. (2007). Bayesian contour extrapolation: Geometric determinants of good continuation. Vision Research 47, 783–798.CrossRefGoogle ScholarPubMed
Tversky, T., Geisler, W.S. & Perry, J.S. (2004). Contour grouping: Closure effects are explained by good continuation and proximity. Vision Research 44, 2769–2777.CrossRefGoogle ScholarPubMed
Wertheimer, M. (1958). Principles of perceptual organization. In Readings in Perception, ed. Beardslee, D.C. & Wertheimer, M., pp. 103–123. Princeton, NJ: Van Nostrand (Original work published 1923).Google Scholar