Berkeley, G. (1709)‘An Essay towards a New Theory of Vision’, inThe Works of George Berkeley, Bishop of Cloyne, vol. 1, ed.
Luce and T. E.
Jessop, Edinburgh: Thomas Nelson, 9 vols,1948–57.
Biederman, I. (1995)‘Higher-Level Vision’, in S. M.
Kosslyn and D. N.
Osherson (eds) An Invitation to Cognitive Science, 2nd edn, vol. 2, Visual Cognition, Cambridge, MA: MIT Press.
(A discussion of the decomposition approach to object recognition.)
Descartes, R. (1637) ‘Optics’, in The Philosophical Writings of Descartes, trans.
Stoothoff and D.
Murdoch, Cambridge: Cambridge University Press, 1985, vol. 1, 152–175.
(Discourses 5 and 6 are particularly relevant.)
Descartes, R. (1641)‘Author’s Replies to the Sixth Set of Objections’, inThe Philosophical Writings of Descartes, trans.
Stoothoff and D.
Murdoch, Cambridge: Cambridge University Press,1984, vol. 2, esp. §9: 294–296.
(Referred to in §1 – Descartes’ ‘intellectualist’ theory of vision.)
Fodor, J. A. and Pylyshyn, Z. (1981)‘How Direct is Visual Perception?: Some Reflections on Gibson’s “Ecological Approach”’, Cognition
(A critical discussion of Gibson’s direct theory of perception. Includes detailed argument but no technicality.)
Gibson, J. (1979) The Ecological Approach to Visual Perception, Boston: Houghton Mifflin.
(The most developed statement of Gibson’s theory of perception.)
Helmholtz, H. von (1950) Treatise on Physiological Optics, ed.
Southall, New York: Dover, 3 vols.
(Influential nineteenth-century account of perceptual processing as a species of inference.)
Hinton, G. E. (1992)‘How Neural Networks Learn from Experience’, Scientific American
267 (3): 144.
(Includes a discussion of connectionist models of shape recognition.)
Kersten, D., Mamassian, P., and Yuille, A. (2004)‘Object Perception as Bayesian Inference’, Annual Review of Psychology
(A general discussion of the Bayesian framework applied to object perception.)
Kersten, D. and Yuille, A. (2003)‘Bayesian Models of Object Perception’, Current Opinion in Neurobiology
(A useful short introduction to Bayesian models of vision.)
Marr, D. (1982) Vision, New York: Freeman Press.
(Somewhat technical, but includes a clear account of the rationale behind the computational approach to vision.)
Paragios, N., Chen, Y. and Faugeras, O. (2006) Handbook of Mathematical Models in Computer Vision, New York: Springer.
(A comprehensive survey of recent work in computational vision. Very technical.)
Rao, R., Olshausen, B. and Lewicki, M. (2002) Probabilistic Models of the Brain, Cambridge, MA: MIT Press.
(A survey of probabilistic models of perception and neural function, including Bayesian models.)
Rock, I. (1983) The Logic of Perception, Cambridge, MA: MIT Press.
(An account of perceptual processing as a form of hypothesis formation and testing.)
Schwartz, R. (1994) Vision: Variations on Some Berkelian Themes, Oxford: Blackwell.
(A useful discussion of historical work on the problems of vision. Also includes a chapter on Gibson’s theory.)
Ullman, S. (1979) The Interpretation of Visual Motion, Cambridge, MA: MIT Press.
(A detailed analysis of the computations involved in visual motion perception. Cited in §4.)