Abstract
The design of a system that understands visual motion, whether biological or machine, must adhere to certain constraints. These constraints include the types and numbers of available processors, how those processors are arranged, the nature of the task, as well as the characteristics of the input itself. This chapter examines some of these constraints, and in two parts, presents a framework for research in this area. The first part, Section 11.2, involves time complexity arguments demonstrating that the common attack to this problem, namely, an approach that is spatially parallel (as least conceptually), with temporal considerations strictly subsequent to the spatial ones, cannot possibly succeed. The essence of this claim is not a new one, and was motivated by similar comments by Neisser (1967), among others. What Neisser and others did not do, however, is provide a framework that is plausible. Expanding on the time complexity argument, we show that in addition to spatial parallelism, the basic system elements include hierarchical organization through abstraction of both prototypical visual knowledge as well as early representations of the input, and the separation of input measurements into logical feature maps.
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Tsotsos, J.K., Fleet, D.J., Jepson, A.D. (1988). Towards a Theory of Motion Understanding in Man and Machine. In: Martin, W.N., Aggarwal, J.K. (eds) Motion Understanding. The Kluwer International Series in Engineering and Computer Science, vol 44. Springer, Boston, MA. https://doi.org/10.1007/978-1-4613-1071-6_11
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DOI: https://doi.org/10.1007/978-1-4613-1071-6_11
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