Line-end detection and boundary gap completion in an EDANN module for orientation

Explores two sources of inaccuracies originating from the use of local line detectors for inferring curve and boundary traces: (1) due to the position uncertainty of the local line detectors, ends of thin lines are not easily detected, even if cross-orientation inhibition is applied; and (2) due to the limited ability of the local line detectors to assess more global trace information gaps appear in the curve and boundary extracted. It is shown how a single EDANN (entropy drive artificial neural networks) module processing the orientation of illumination contrast compensates for these inaccuracies by performing a two-stage detection process, a competitive and a cooperative one. In the competitive stage, a vector field of tangents to curves and boundaries is extracted by using elongated receptive fields. In the cooperative stage, line-ends are extracted and boundary gaps are bridged by broadening the neuron´s orientation tuning curves