  Figure Ground Organization (FGO) -- inferring spatial depth ordering of
objects in a visual scene -- involves determining which side of an occlusion
boundary is figure (closer to the observer) and which is ground (further away
from the observer). A combination of global cues, like convexity, and local
cues, like T-junctions are involved in this process. We present a biologically
motivated, feed forward computational model of FGO incorporating convexity,
surroundedness, parallelism as global cues and Spectral Anisotropy (SA),
T-junctions as local cues. While SA is computed in a biologically plausible
manner, the inclusion of T-Junctions is biologically motivated. The model
consists of three independent feature channels, Color, Intensity and
Orientation, but SA and T-Junctions are introduced only in the Orientation
channel as these properties are specific to that feature of objects. We study
the effect of adding each local cue independently and both of them
simultaneously to the model with no local cues. We evaluate model performance
based on figure-ground classification accuracy (FGCA) at every border location
using the BSDS 300 figure-ground dataset. Each local cue, when added alone,
gives statistically significant improvement in the FGCA of the model suggesting
its usefulness as an independent FGO cue. The model with both local cues
achieves higher FGCA than the models with individual cues, indicating SA and
T-Junctions are not mutually contradictory. Compared to the model with no local
cues, the feed-forward model with both local cues achieves $\geq 8.78$%
improvement in terms of FGCA.
