Object recognition by natural and artificial visual systems benefits from the identification of object boundaries. A useful cue for the detection of object boundaries is the superposition of luminance and color edges. To gain insight into the suitability of this cue for object recognition, we examined convolutional neural network models that had been trained to recognize objects in natural images. We focused specifically on units in the second convolutional layer whose activations are invariant to the spatial phase of a sinusoidal grating. Some of these units were tuned for a nonlinear combination of color and luminance, which is broadly consistent with a role in object boundary detection. Others were tuned for luminance alone, but very few were tuned for color alone. A literature review reveals that V1 complex cells have a similar distribution of tuning. We speculate that this pattern of sensitivity provides an efficient basis for object recognition, perhaps by mitigating the effects of lighting on luminance contrast polarity. The absence of a contrast polarity-invariant representation of color alone suggests that it is redundant with other representations.

中文翻译：

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V1 复杂细胞和人工神经网络中的颜色和亮度处理

自然和人工视觉系统的对象识别受益于对象边界的识别。检测对象边界的一个有用提示是亮度和颜色边缘的叠加。为了深入了解这种提示对于对象识别的适用性，我们检查了经过训练以识别自然图像中的对象的卷积神经网络模型。我们特别关注第二个卷积层中的单元，其激活对于正弦光栅的空间相位是不变的。其中一些单元针对颜色和亮度的非线性组合进行了调整，这与对象边界检测中的作用大致一致。其他人仅针对亮度进行调整，但很少有人仅针对颜色进行调整。文献综述表明 V1 复合体细胞具有相似的调谐分布。我们推测，这种灵敏度模式可能通过减轻照明对亮度对比度极性的影响，为物体识别提供有效的基础。仅缺乏颜色的对比极性不变表示表明它与其他表示是多余的。