Cardei Vlad C, Funt Brian, Barnard Kobus
NextEngine Incorporated, 401 Wilshire Boulevard, Ninth Floor, Santa Monica, California 90401, USA.
J Opt Soc Am A Opt Image Sci Vis. 2002 Dec;19(12):2374-86. doi: 10.1364/josaa.19.002374.
A neural network can learn color constancy, defined here as the ability to estimate the chromaticity of a scene's overall illumination. We describe a multilayer neural network that is able to recover the illumination chromaticity given only an image of the scene. The network is previously trained by being presented with a set of images of scenes and the chromaticities of the corresponding scene illuminants. Experiments with real images show that the network performs better than previous color constancy methods. In particular, the performance is better for images with a relatively small number of distinct colors. The method has application to machine vision problems such as object recognition, where illumination-independent color descriptors are required, and in digital photography, where uncontrolled scene illumination can create an unwanted color cast in a photograph.
神经网络可以学习颜色恒常性，这里将其定义为估计场景整体光照色度的能力。我们描述了一种多层神经网络，它仅根据场景图像就能恢复光照色度。该网络预先通过一组场景图像及其相应场景光源的色度进行训练。对真实图像的实验表明，该网络的性能优于以前的颜色恒常性方法。特别是，对于具有相对较少不同颜色的图像，性能更好。该方法可应用于机器视觉问题，如需要光照无关颜色描述符的目标识别，以及数字摄影中，不受控制的场景光照会在照片中产生不必要的色偏。
