  It is known that the human visual system performs a hierarchical information
process in which early vision cues (or primitives) are fused in the visual
cortex to compose complex shapes and descriptors. While different aspects of
the process have been extensively studied, as the lens adaptation or the
feature detection, some other,as the feature fusion, have been mostly left
aside. In this work we elaborate on the fusion of early vision primitives using
generalizations of the Choquet integral, and novel aggregation operators that
have been extensively studied in recent years. We propose to use
generalizations of the Choquet integral to sensibly fuse elementary edge cues,
in an attempt to model the behaviour of neurons in the early visual cortex. Our
proposal leads to a full-framed edge detection algorithm, whose performance is
put to the test in state-of-the-art boundary detection datasets.
