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Download the Paper (434 K, PDF file) - January 1, 2006 Tell a colleague about it. Printing Tips: Select 'print as image' in the Acrobat print dialog if you have trouble printing.

ABSTRACT:
We argue that the study of human vision should be aimed at determining how humans perform natural tasks on natural images. Attempts to understand the phenomenology of vision from artificial stimuli, though worthwhile as a starting point, risk leading to faulty generalizations about visual systems. In view of the enormous complexity of natural images, they are similar to trying to evaluate the performance of a soldier in battle from his ability at playing with a water pistol. Dealing with this complexity is daunting, but Bayesian inference on structured probability distributions offers the ability to design theories of vision that can deal with the complexity of natural images and which use analysis by synthesis strategies with intriguing similarities to the brain.

SUGGESTED CITATION:
Alan L. Yuille and Daniel Kersten, "Vision as Bayesian Inference: Analysis by Synthesis?" (January 1, 2006). Department of Statistics, UCLA. Department of Statistics Papers. Paper 2006010107.
http://repositories.cdlib.org/uclastat/papers/2006010107