Fig. 4.

Capacities of the model. Each capacity is added incrementally to all previous ones. (A) Figure-ground segmentation of moving regions (motion direction indicated by the green arrow). The model constructs and stores a simple representation of the region (in blue), based on the region’s low-level features. (B) Detection of familiar regions in a static scene. Based on the representation obtained in (A), the model can detect a familiar region (in blue) and separate it from its background in a static scene. (C) Detection of boundaries and their ownership at motion discontinuities. The model detects object boundaries of a moving region (motion indicated by the green arrow) by identifying motion discontinuities (red contours), and determines ‘ownership’ direction (red arrows) of the boundary (which side belongs to the object). The model extends the representation of the moving region in (A) to include the ownership along its boundaries. (D) Detection of internal boundaries of a moving region (motion indicated by the green arrow). Such internal boundaries are typically produced at a container’s rim. The model represents the internal boundary as a part of the object (boundary ownership direction indicated by red arrows). (E) Detection of familiar boundaries in a static scene. Based on the representation in (C, D), external and internal boundaries of familiar objects can be detected in a static scene. (F) Detection of familiar internal regions in a static scene. In a container, based on (B-E), the model can discriminate between the ‘front’ (blue) and ‘back’ (red) sides separated by the internal boundary. The ‘front’ side is the region owning the internal boundary. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)