Statistical regularities in natural scenes that support figure-ground segregation by neural populations
Fig 2
Simulated receptive fields (sRFs) and fixation points were used to sample from both datasets.
This figure illustrates these sRFs and how the associated fixation point can affect retinal speed and disparity (it is not a real scene from our datasets). After an sRF (gray circles) was identified, we assigned a random fixation point based on the scene’s saliency map (green circles) at the appropriate eccentricity (white dashed lines). A,B) For the motion maps, the calculated motion vector at the point of fixation was subtracted from the motion at all other points to simulate retinal motion (inset). In this example, the annotated figure region is moving leftward and the rest of the scene is stationary. Panel A illustrates the average speeds in the resulting figure and ground regions (sf and sg) for a stationary fixation point (such that the figure region speed is faster) and panel B illustrates the same for a moving fixation point on the figure (such that the ground region speed is faster). C,D) For the distance map dataset, the calculated distance at the point of fixation was used to determine the binocular retinal disparity. In this example, the annotated figure region is closer than the rest of the scene. Panel C illustrates average disparities in the resulting figure and ground regions (df and dg) for a far fixation point and panel D illustrates the same for a near fixation point on the figure. In both cases, the figure region has a more positive disparity than the ground. Images are modified from artwork obtained on Pixabay.