Bayesian models of object perception

被引：179
|
作者：
Kersten, D
Yuille, A
机构：
[1] Univ Minnesota, Dept Psychol, Minneapolis, MN 55455 USA
[2] Univ Calif Los Angeles, Dept Stat, Los Angeles, CA 90095 USA
[3] Univ Calif Los Angeles, Dept Psychol, Los Angeles, CA 90095 USA
来源：
CURRENT OPINION IN NEUROBIOLOGY | 2003年 / 13卷 / 02期
关键词：
D O I：
10.1016/S0959-4388(03)00042-4
中图分类号：
Q189 [神经科学];
学科分类号：
071006 ;
摘要：
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.
引用
收藏
页码：150 / 158
页数：9
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