Abstract
A generative theory of shape represents a given data set by a program that generates the set. This program must be inferrable from the set. We shall say that the program is recoverable from the data set. Recoverability of the generative program places strong constraints on the inference rules by which recovery takes place, and on the programs that will be inferred. This, in turn, produces a theory of geometry that is very different from the current theories of geometry.
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© 2001 Springer-Verlag Berlin Heidelberg
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(2001). Recoverability. In: A Generative Theory of Shape. Lecture Notes in Computer Science, vol 2145. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45488-8_2
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DOI: https://doi.org/10.1007/3-540-45488-8_2
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Publisher Name: Springer, Berlin, Heidelberg
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Online ISBN: 978-3-540-45488-5
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