A model of figure ground organization incorporating local and global cues
This work addresses the challenge of depth ordering in computer vision, but it is incremental as it builds on existing cues and methods.
The paper tackled the problem of figure-ground organization in visual scenes by developing a biologically motivated computational model that incorporates both local and global cues, achieving an improvement of at least 8.78% in figure-ground classification accuracy compared to a baseline model without local cues.
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.