Figure-ground segregation: A fully nonlocal approach

•Figure-ground segregation can be computed without relying on image contours.•Figural status estimates result from a multidirectional linear voting process.•For each point, multiple line-shaped neighborhoods participate to the voting process.•Figural status's coherence in homogeneous region is enforced by nonlinear diffusion.•Nonlocal estimations require the involvement of cells in higher cortical visual areas.

We present a computational model that computes and integrates in a nonlocal fashion several configural cues for automatic figure-ground segregation. Our working hypothesis is that the figural status of each pixel is a nonlocal function of several geometric shape properties and it can be estimated without explicitly relying on object boundaries. The methodology is grounded on two elements: multi-directional linear voting and nonlinear diffusion. A first estimation of the figural status of each pixel is obtained as a result of a voting process, in which several differently oriented line-shaped neighborhoods vote to express their belief about the figural status of the pixel. A nonlinear diffusion process is then applied to enforce the coherence of figural status estimates among perceptually homogeneous regions. Computer simulations fit human perception and match the experimental evidence that several cues cooperate in defining figure-ground segregation. The results of this work suggest that figure-ground segregation involves feedback from cells with larger receptive fields in higher visual cortical areas.