The problem of removing illuminant variations to preserve the colours of objects ( colour constancy ) has already been solved by the human brain using mechanisms that rely largely on centre-surround computations of local contrast. In this paper we adopt some of these biological solutions described by long known physiological findings into a simple, fully automatic, functional model (termed Adaptive Surround Modulation or ASM). In ASM, the size of a visual neuron's receptive field (RF) as well as the relationship with its surround varies according to the local contrast within the stimulus, which in turn determines the nature of the centre-surround normalisation of cortical neurons higher up in the processing chain. We modelled colour constancy by means of two overlapping asymmetric Gaussian kernels whose sizes are adapted based on the contrast of the surround pixels, resembling the change of RF size. We simulated the contrast-dependent surround modulation by weighting the contribution of each Gaussian according to the centre-surround contrast. In the end, we obtained an estimation of the illuminant from the set of the most activated RFs’ outputs. Our results on three single-illuminant and one multi-illuminant benchmark datasets show that ASM is highly competitive against the state-of-the-art and it even outperforms learning-based algorithms in one case. Moreover, the robustness of our model is more tangible if we consider that our results were obtained using the same parameters for all datasets, that is, mimicking how the human visual system operates. These results suggest a dynamical adaptation mechanisms contribute to achieving higher accuracy in computational colour constancy.

中文翻译：

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超越经典感受野的颜色恒常性


人脑已经使用主要依赖于局部对比度的中心-环绕计算的机制解决了消除光源变化以保留物体颜色（颜色恒定性）的问题。在本文中，我们将长期已知的生理学发现所描述的一些生物解决方案采用到一个简单的全自动功能模型中（称为自适应环绕调制或 ASM）。在 ASM 中，视觉神经元感受野 (RF) 的大小以及与其周围环境的关系根据刺激内的局部对比度而变化，这反过来又决定了高层皮层神经元的中心-周围标准化的性质。加工链。我们通过两个重叠的不对称高斯核对颜色恒常性进行建模，其大小根据周围像素的对比度进行调整，类似于 RF 大小的变化。我们通过根据中心-环绕对比度对每个高斯的贡献进行加权来模拟依赖于对比度的环绕调制。最后，我们从一组最活跃的 RF 输出中获得了对光源的估计。我们在三个单光源和一个多光源基准数据集上的结果表明，ASM 与最先进的算法相比具有很强的竞争力，甚至在一种情况下优于基于学习的算法。此外，如果我们考虑到我们的结果是使用所有数据集的相同参数获得的，即模仿人类视觉系统的运行方式，那么我们模型的稳健性就更加明显。这些结果表明动态适应机制有助于在计算颜色恒常性方面实现更高的准确性。