Globally consistent depth sorting of overlapping 2D surfaces in a model using local recurrent interactions

The human visual system utilizes depth information as a major cue to group together visual items constituting an object and to segregate them from items belonging to other objects in the visual scene. Depth information can be inferred from a variety of different visual cues, such as disparity, occlusions and perspective. Many of these cues provide only local and relative information about the depth of objects. For example, at occlusions, T-junctions indicate the local relative depth precedence of surface patches. However, in order to obtain a globally consistent interpretation of the depth relations between the surfaces and objects in a visual scene, a mechanism is necessary that globally propagates such local and relative information. We present a computational framework in which depth information derived from T-junctions is propagated along surface contours using local recurrent interactions between neighboring neurons. We demonstrate that within this framework a globally consistent depth sorting of overlapping surfaces can be obtained on the basis of local interactions. Unlike previous approaches in which locally restricted cell interactions could merely distinguish between two depths (figure and ground), our model can also represent several intermediate depth positions. Our approach is an extension of a previous model of recurrent V1–V2 interaction for contour processing and illusory contour formation. Based on the contour representation created by this model, a recursive scheme of local interactions subsequently achieves a globally consistent depth sorting of several overlapping surfaces. Within this framework, the induction of illusory contours by the model of recurrent V1–V2 interaction gives rise to the figure-ground segmentation of illusory figures such as a Kanizsa square.