A Neurally Inspired Model of Figure Ground Organization with Local and Global Cues

Sudarshan Ramenahalli

doi:10.3390/ai1040028

Abstract

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. 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 is presented. 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. The effect of adding each local cue independently and both of them simultaneously to the model with no local cues is studied. Model performance is evaluated 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 ≥8.78% improvement in terms of FGCA.

Highlights

An important step in the visual processing hierarchy is putting together fragments of features into coherent objects and inferring the spatial relationship between them
It is important to note that figure-ground classification accuracy (FGCA) of the model with both local cues is always higher than the FGCAs of models with individual local cues
This suggests the local cues are mutually facilitatory, which is further validated by the fact that a statistically significant improvement is seen in FGCA when

Summary

Introduction

An important step in the visual processing hierarchy is putting together fragments of features into coherent objects and inferring the spatial relationship between them. The feature fragments can be based on color, orientation, texture, and so forth. Grouping [1,2] refers to the mechanism by which the feature fragments are put together to form perceptual objects. Such objects in the real world may be isolated, fully occluding one another or partially occluding, depending on the observer’s viewpoint. Gestalt psychologists have identified a variety of cues that mediate the process of FGO [3] In the context of partially occluding objects, Figure-ground organization (FGO) refers to determining which side of an occlusion boundary is the occluder, closer to the observer, referred to as figure and which side is the occluded, far away from the observer, termed as ground.

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