What is it about?
We developed a neuro-computational model for "border-ownership" signals that underlie figure-ground organization (perception of depth order). By reflecting the global consistency of image elements, it shows robust responses at an unprecedented level.
Photo by Sam Moqadam on Unsplash
Why is it important?
The visual system performs remarkably well to perceive the depth order of separate areas in the surrounding enviroenment. In this, figure-ground organization based on pictorial cues plays an important role. To understand how figure-ground organization emerges through image signal processing, it is essential how the global configuration of the image is reflected. In the past, many neuro-computational models implemented algorithms to give a bias to convex shapes and were based on the geometriy of borderlines. However, in certain conditions, this approach is bound to fail. We argue that the long-range consistency of surface properties is reflected in the computational processes of border-ownership (or edge assignement) . Our model shows exteremely robust responses unprecedented by previous models. It is possible that a class of border-ownership-sensitive neurons that are also sensitive to contrast polarity (Zhou et al., 2000, J. Neurosci.) underlie this computation process.
Read the Original
This page is a summary of: Emergence of border-ownership by large-scale consistency and long-range interactions: Neuro-computational model to reflect global configurations., Psychological Review, July 2021, American Psychological Association (APA), DOI: 10.1037/rev0000293.
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border-ownership computation of an image with holes and occlusion
The response of the model. The blue dots indicate border segments and the red lines indicate the side of the ownership of the bordersegements. The image (Figure 12B in the paper) has holes, occluded object, and a circular object on top. The computed border ownership corresponds to perceived depth orders at the borders.
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