Object recognition and image understanding: Theories of Everything?

Object recognition and image understanding: Theories of Everything? Spatial Vision , Vol. 13, No. 2,3, pp. 129– 135 (2000) Ó VSP 2000. Editorial Object recognition and image understanding: Theories of Everything? Theories of Everything is the title of a recent book that summarises current attempts made in physics to achieve a uniŽ ed theory of the universe (Barrow, 1991). In the history of epistemology, the question of how humans come to recognise objects is as old as the one about the nature of the universe. The quest for a uniŽ ed theory of visual object recognition, would, therefore, seem to be a reasonable enterprise. Taking this for granted, one then might guess which perspective could produce such a uniŽ ed theory. One possibility is a neural-computing approach involving an algorithmic view of brain function. This is, indeed, a good candidate for modeling the basic properties of the human brain, i.e. the ability to learn from past errors and correct itself quasi-automatically. However, the organization of the brain is extremely complex, and it is unlikely that neural nets without recurrent coupling offer more than descriptions of certain aspects of this complexity. This raises the question of how such micro-theories could be combined to produce a theory of http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Spatial Vision (continued as Seeing & Perceiving from 2010) Brill

Object recognition and image understanding: Theories of Everything?

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Publisher
Brill
Copyright
© 2000 Koninklijke Brill NV, Leiden, The Netherlands
ISSN
0169-1015
eISSN
1568-5683
D.O.I.
10.1163/156856800741054
Publisher site
See Article on Publisher Site

Abstract

Spatial Vision , Vol. 13, No. 2,3, pp. 129– 135 (2000) Ó VSP 2000. Editorial Object recognition and image understanding: Theories of Everything? Theories of Everything is the title of a recent book that summarises current attempts made in physics to achieve a uniŽ ed theory of the universe (Barrow, 1991). In the history of epistemology, the question of how humans come to recognise objects is as old as the one about the nature of the universe. The quest for a uniŽ ed theory of visual object recognition, would, therefore, seem to be a reasonable enterprise. Taking this for granted, one then might guess which perspective could produce such a uniŽ ed theory. One possibility is a neural-computing approach involving an algorithmic view of brain function. This is, indeed, a good candidate for modeling the basic properties of the human brain, i.e. the ability to learn from past errors and correct itself quasi-automatically. However, the organization of the brain is extremely complex, and it is unlikely that neural nets without recurrent coupling offer more than descriptions of certain aspects of this complexity. This raises the question of how such micro-theories could be combined to produce a theory of

Journal

Spatial Vision (continued as Seeing & Perceiving from 2010)Brill

Published: Jan 1, 2000

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