Vision as Bayesian inference: analysis by synthesis? Export

@article{Yuille.2006, abstract = {We argue that the study of human vision should be aimed at determining how humans perform natural tasks with natural images. Attempts to understand the phenomenology of vision from artificial stimuli, although worthwhile as a starting point, can lead to faulty generalizations about visual systems, because of the enormous complexity of natural images. 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 that use `analysis by synthesis' strategies with intriguing similarities to the brain. We examine these strategies using recent examples from computer vision, and outline some important imlications for cognitive science.}, author = {Yuille, Alan and Kersten, Daniel}, booktitle = {Probabilistic models of cognition}, citeulike-article-id = {2860122}, citeulike-linkout-0 = {http://dx.doi.org/10.1016/j.tics.2006.05.002}, doi = {10.1016/j.tics.2006.05.002}, journal = {Trends in Cognitive Sciences}, month = {July}, number = {7}, pages = {301--308}, posted-at = {2008-06-04 00:53:31}, priority = {2}, title = {Vision as Bayesian inference: analysis by synthesis?}, url = {http://dx.doi.org/10.1016/j.tics.2006.05.002}, volume = {10}, year = {2006} }

TY - JOUR ID - Yuille.2006 L3 - citeulike-article-id:2860122 N2 - We argue that the study of human vision should be aimed at determining how humans perform natural tasks with natural images. Attempts to understand the phenomenology of vision from artificial stimuli, although worthwhile as a starting point, can lead to faulty generalizations about visual systems, because of the enormous complexity of natural images. 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 that use `analysis by synthesis' strategies with intriguing similarities to the brain. We examine these strategies using recent examples from computer vision, and outline some important imlications for cognitive science. IS - 7 JF - Trends in Cognitive Sciences EP - 308 TI - Vision as Bayesian inference: analysis by synthesis? VL - 10 T2 - Probabilistic models of cognition SP - 301 AU - Yuille, Alan AU - Kersten, Daniel PY - 2006/July// UR - http://dx.doi.org/10.1016/j.tics.2006.05.002 ER -