Simulation of neural contour mechanisms: representing anomalous contours

https://doi.org/10.1016/S0262-8856(97)00083-8Get rights and content

Abstract

We present a computational model of a contour mechanism first identified by neurophysiological methods in monkey visual cortex. The scope is the definition of occluding contours in static monocular images. The model employs convolutions and non-linear operations, but does not require feedback loops. Contours are defined by the local response maxima of a contour operator applied in six orientations. The operator sums the activities of a ‘C-operator’, sensitive to contrast borders and a ‘grouping operator’ that integrates collinear aggregations of termination features, such as line-ends and corners. The grouping process is selective for termination features which are consistent with the interpretation of occlusion. Contrast edges are represented by C-operators simulating the function of cortical complex cells, termination features by ES-operators simulating the function of cortical end-stopped cells. The concepts of ortho and para curvilinear grouping are introduced. Ortho grouping applies to terminations of the background, which tend to be orthogonal to the occluding contour. Para grouping applies to discontinuities of the foreground and is used to interpolate the contour in the direction of termination. Both grouping modes also identify the direction of figure and ground at such contours. The simulation reproduces well-known illusory figures, including curved Kanizsa triangles and the circular disk of the four-armed Ehrenstein figure. Further, it improves the definition of occluding contours in natural, gray value images.

References (30)

  • F. Heitger et al.

    Simulation of neural contour mechanisms: From simple to endstopped cells

    Vision Research

    (1992)
  • P.J. Kellman et al.

    A theory of visual interpolation in object perception

    Cognitive Psychology

    (1991)
  • G.W. Lesher et al.

    The role of edges and line-ends in illusory contour formation

    Vision Research

    (1993)
  • M. Soriano et al.

    The abutting grating illusion

    Vision Research

    (1996)
  • F. Heitger et al.

    A computational model of neural contour processing: figure-ground segregation and illusory contours

  • R. von der Heydt et al.

    Mechanisms of contour perception in monkey visual cortex. I. Lines of pattern discontinuity

    Journal of Neuroscience

    (1989)
  • E. Peterhans et al.

    Mechanisms of contour perception in monkey visual cortex. II. Contours bridging gaps

    Journal of Neuroscience

    (1989)
  • E. Peterhans et al.

    Neuronal responses to illusory contour stimuli reveal stages of visual cortical processing

  • S. Ullman

    Filling-in the gaps: the shape of subjective contours and a model for their generation

    Biological Cybernetics

    (1976)
  • S. Grossberg et al.

    Neural dynamics of perceptual grouping: textures, boundaries, and emergent segmentations

    Perception and Psychophysics

    (1985)
  • S. Grossberg et al.

    Neural dynamics of form perception: boundary completion, illusory figures, and neon color spreading

    Psychological Review

    (1985)
  • L.H. Finkel et al.

    Integration of distributed cortical systems by re-entry: a computer simulation of interactive functionally segregated visual areas

    Journal of Neuroscience

    (1989)
  • J. Skrzypek et al.

    Neural network models for illusory contour perception

  • L.R. Williams et al.

    Stochastic completion fields: a neural model of illusory contour shape and salience

  • C.H. Granlund

    In search of a general picture processing operator

    Computer Graphics and Image Processing

    (1978)
  • Cited by (91)

    • Possible functions of contextual modulations and receptive field nonlinearities: Pop-out and texture segmentation

      2014, Vision Research
      Citation Excerpt :

      This is the focus of the remainder of the review. One possibility is that V1 neurons respond to local orientation discontinuities independent of the orientation of the texture boundary, and that V2 neurons combine the output of those V1 neurons in a way that orientation cue-invariance is achieved (Heitger et al., 1998; Nothdurft, 1991; Peterhans & von der Heydt, 1989; Schmid, 2008). This model is different from the FRF model in that the first processing stage is responsible for detection of local orientation discontinuities as well as standard luminance boundaries.

    View all citing articles on Scopus

    Part of this work has been presented at the 4th International Conference on Computer Vision (ICCV'93) in Berlin, Germany [1].

    2

    Current address: Centre Swiss d'Electronique et de Microtechnique SA, Neuchâtel, Switzerland.

    View full text