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A novel approach to color normalization using neural network

机译:一种使用神经网络进行色彩归一化的新方法

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摘要

Color is a powerful descriptor that often simplifies object extraction and identification, and many computer vision systems use color to aid object recognition. However, image colors strongly depend on lighting geometry (direction and intensity of light source) and illuminant color (spectral power distribution). Either small variation in the intensity or the change of scene illumination can dramatically make object color changed. To overcome the lighting dependency problem, a color constancy or normalization algorithm should be used for preprocessing. This paper presents a novel approach to performing color normalization. A nonlinear mapping function is estimated using a neural network. Once the mapping function is found accurately, an image under unknown illumination may be transformed to the image under the predetermined illumination, which will be useful for color image processing. Three groups of experiments were conducted. In our experiments, images are processed by various neural networks and the performance is boosted by using a committee machine, and then the mapping errors are estimated and the results are compared with those of other algorithms. The experimental results demonstrate that the performance of the proposed method is superior to that of other color normalization algorithms.
机译:颜色是一种强大的描述符,通常可以简化对象的提取和识别,许多计算机视觉系统都使用颜色来辅助对象识别。但是,图像颜色在很大程度上取决于照明的几何形状(光源的方向和强度)和光源的颜色(光谱功率分布)。强度的微小变化或场景照度的变化都会极大地改变物体的颜色。为了克服照明依赖性问题,应将颜色恒定性或归一化算法用于预处理。本文提出了一种执行颜色归一化的新颖方法。使用神经网络估计非线性映射函数。一旦准确找到映射函数,就可以将未知照明下的图像转换为预定照明下的图像,这对于彩色图像处理很有用。进行了三组实验。在我们的实验中,图像通过各种神经网络进行处理,并通过使用委员会机器来提高性能,然后估计映射误差,并将结果与​​其他算法进行比较。实验结果表明,该方法的性能优于其他颜色归一化算法。

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