Statistical Models of Linear and Nonlinear Contextual Interactions in Early Visual Processing
Statistical Models of Linear and Nonlinear Contextual Interactions in Early Visual Processing
复制标题
早期视觉处理中线性和非线性上下文交互的统计模型
DOI：
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发表时间：
2009
期刊：
影响因子：
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通讯作者：
O. Schwartz
中科院分区：
文献类型：
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作者：
R. C. Cagli;P. Dayan;O. Schwartz
关于早期视觉处理的一个中心假设是，它在一个与自然场景的统计数据相匹配的坐标系中表示输入。简单的版本，这导致了类似于Gabor的感受野和来自局部环境的分裂性增益调制;这些导致了视觉处理的有影响力的神经和心理模型。然而，这些帐户是基于对每个点周围的视觉环境的不完整的看法。在这里，我们考虑一个近似模型的线性和非线性之间的相关性的空间分布的Gabor样感受野，其中，当训练的合奏自然场景，统一的空间背景效应的范围。完整的模型解释了初级视觉皮层（V1）中的神经环绕数据，为与Li（2002）假设相关的感知现象提供了统计基础，即V1构建了显着图，并拟合了倾斜错觉的数据。
A central hypothesis about early visual processing is that it represents inputs in a coordinate system matched to the statistics of natural scenes. Simple versions of this lead to Gabor-like receptive fields and divisive gain modulation from local surrounds; these have led to influential neural and psychological models of visual processing. However, these accounts are based on an incomplete view of the visual context surrounding each point. Here, we consider an approximate model of linear and non-linear correlations between the responses of spatially distributed Gabor-like receptive fields, which, when trained on an ensemble of natural scenes, unifies a range of spatial context effects. The full model accounts for neural surround data in primary visual cortex (V1), provides a statistical foundation for perceptual phenomena associated with Li's (2002) hypothesis that V1 builds a saliency map, and fits data on the tilt illusion.
影响因子：
1.8
作者：
Zhang L;Tong MH;Marks TK;Shan H;Cottrell GW
通讯作者：
Cottrell GW
DOI：
10.1364/josaa.4.002379
发表时间：
1987-12-01
影响因子：
1.9
作者：
FIELD, DJ
通讯作者：
FIELD, DJ