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Machine perception: What makes it so hard for computers to see

In W. Savage (ed.), Perception and Cognition. University of Minnesota Press. pp. 65--87 (1978)

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  1. Bridging language with the rest of cognition: computational, algorithmic and neurobiological issues and methods.Shimon Edelman - unknown
    The computational program for theoretical neuroscience initiated by Marr and Poggio (1977) calls for a study of biological information processing on several distinct levels of abstraction. At each of these levels — computational (defining the problems and considering possible solutions), algorithmic (specifying the sequence of operations leading to a solution) and implementational — significant progress has been made in the understanding of cognition. In the past three decades, computational principles have been discovered that are common to a wide range of (...)
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  • Unsupervised statistical learning in vision: computational principles, biological evidence.Shimon Edelman - unknown
    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 (...)
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