Driving vision by topology

Recently, vision research has centred on the extraction and organization of geometric features, and on geometric relations. It is largely assumed that topological structure, that is linked edgel chains and junctions, cannot be extracted reliably from image intensity data. In this paper we demonstrate that this view is overly pessimistic and that visual tasks, such as perceptual grouping, can be carried out much more efficiently and reliably if well-formed topological structures are available. Towards this end, we describe an edge detection algorithm designed to recover much better scene topology than previously considered possible. In doing this we need make no sacrifice to geometric accuracy of the edge description.