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A multistage neural network for color constancy and color induction

机译:用于色彩恒定和色彩归纳的多级神经网络

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摘要

A biologically-based multistage neural network is presented which produces color constant responses to a variety of color stimuli. The network takes advantage of several mechanisms in the human visual system, including retinal adaptation, spectral opponency, and spectrally-specific long-range inhibition. This last stage is a novel mechanism based on cells which have been described in cortical area V4. All stages include nonlinear response functions. The model emulates human performance in several psychophysical paradigms designed to test color constancy and color induction. We measured the amount of constancy achieved with both natural and artificial simulated illuminants, using homogeneous grey backgrounds and more complex backgrounds, such as Mondrians. On average, the model performs as well or better than the average human color constancy performance under similar conditions. The network simulation also displays color induction and assimilation behavior consistent with human perceptual data.
机译:提出了一种基于生物学的多级神经网络,该网络可产生对各种颜色刺激的颜色常数响应。该网络利用了人类视觉系统中的多种机制,包括视网膜适应,光谱对立和光谱特异性远距离抑制。最后阶段是基于在皮层区域V4中描述的细胞的新型机制。所有阶段都包括非线性响应函数。该模型在旨在测试颜色恒定性和颜色归纳性的几种心理物理范例中模拟人类的表现。我们使用均质的灰色背景和更复杂的背景(例如蒙德里安)测量了自然和人工模拟光源所达到的恒定性。平均而言,该模型在相似条件下的表现要好于或优于平均人类色彩的稳定性。网络模拟还显示与人类感知数据一致的颜色诱导和同化行为。

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