Distinguishing line detection from texture segregation using a modular network-based model

An important early vision problem on how a bank of local spatial filters can be common to both line- and edge detection, and texture segregation is discussed. The authors introduce a network-based model for line- and edge detection and texture segregation. The network is based on the entropy driven artificial neural network (EDANN) model, a previously developed network module. Using a hierarchy of different instantiations of the same EDANN module, the authors were able not only to resolve the major ambiguities with line- and edge detection and texture segregation, but also to distinguish these tasks and to discount for the effect of the illuminant without relying on a diffusive filling-in process