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This paper describes a number of computer vision systems that we have constructed, and which are firmly based on knowledge of diverse sorts. However, that knowledge is often represented in a way that is only accessible to a limited set of processes, that make limited use of it, and though the knowledge is amenable to change, in practice it can only be changed in rather simple ways. The rest of the paper addresses the questions: (i) what knowledge is mobilized in the furtherance of a perceptual task?; (ii) how is that knowledge represented?; and (iii) how is that knowledge mobilized? First we review some cases of early visual processing where the mobilization of knowledge seems to be a key contributor to success yet where the knowledge is deliberately represented in a quite inflexible way. After considering the knowledge that is involved in overcoming the projective nature of images, we move the discussion to the knowledge that was required in programs to match, register, and recognize shapes in a range of applications. Finally, we discuss the current state of process architectures for knowledge mobilization.

Original publication




Journal article


Philos Trans R Soc Lond B Biol Sci

Publication Date





1241 - 1248


Humans, Neural Networks (Computer), Pattern Recognition, Automated, Visual Perception