The paper is organized as follows. Section 2 discusses relations to other work
on detection. Section 3 provides a description of the detection algorithm, the type
of local features employed, and how they are identified in training. In section 4
the neural architecture for such detection is described. In section 5 the biological
analogies of the model are discussed, and how the architecture can be used to
explain certain experiments in visual selection reported in the literature.