Computer vision systems are, on most counts, poor performers, when compared to their biological counterparts. The reason for this may be that computer vision is handicapped by an unreasonable assumption regarding what it means to see, which became prevalent as the notions of intrinsic images and of representation by reconstruction took over the field in the late 1970’s. Learning from biological vision may help us to overcome this handicap.

Unsupervised statistical learning is the standard setting for the development of the only advanced visual system that is both highly sophisticated and versatile, and extensively studied: that of monkeys and humans. In this extended abstract, we invoke philosophical observations, computational arguments, behavioral data and neurobiological findings to explain why computer vision researchers should care about (1) unsupervised learning, (2) statistical inference, and (3) the visual brain. We then outline a neuromorphic approach to structural primitive learning motivated by these considerations, survey (...) a range of neurobiological findings and behavioral data consistent with it, and conclude by mentioning some of the more challenging directions for future research. (shrink)