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物体感知的贝叶斯模型。

Bayesian models of object perception.

作者信息

Kersten Daniel, Yuille Alan

机构信息

Department of Psychology, University of Minnesota, 75 East River Road, Minneapolis, MN 55455, USA.

出版信息

Curr Opin Neurobiol. 2003 Apr;13(2):150-8. doi: 10.1016/s0959-4388(03)00042-4.

Abstract

The human visual system is the most complex pattern recognition device known. In ways that are yet to be fully understood, the visual cortex arrives at a simple and unambiguous interpretation of data from the retinal image that is useful for the decisions and actions of everyday life. Recent advances in Bayesian models of computer vision and in the measurement and modeling of natural image statistics are providing the tools to test and constrain theories of human object perception. In turn, these theories are having an impact on the interpretation of cortical function.

摘要

人类视觉系统是已知最为复杂的模式识别装置。视觉皮层以尚未被完全理解的方式,对来自视网膜图像的数据得出一种简单且明确的解释,这对日常生活中的决策和行动很有用。计算机视觉的贝叶斯模型以及自然图像统计量的测量与建模方面的最新进展,正在提供工具来检验和限制关于人类物体感知的理论。反过来,这些理论也正在对皮层功能的解释产生影响。

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