Simão Josemar, Jörg Andreas Schneebeli Hans, Vassallo Raquel Frizera
J Opt Soc Am A Opt Image Sci Vis. 2015 Nov 1;32(11):2033-43. doi: 10.1364/JOSAA.32.002033.
Color constancy is the ability to perceive the color of a surface as invariant even under changing illumination. In outdoor applications, such as mobile robot navigation or surveillance, the lack of this ability harms the segmentation, tracking, and object recognition tasks. The main approaches for color constancy are generally targeted to static images and intend to estimate the scene illuminant color from the images. We present an iterative color constancy method with temporal filtering applied to image sequences in which reference colors are estimated from previous corrected images. Furthermore, two strategies to sample colors from the images are tested. The proposed method has been tested using image sequences with no relative movement between the scene and the camera. It also has been compared with known color constancy algorithms such as gray-world, max-RGB, and gray-edge. In most cases, the iterative color constancy method achieved better results than the other approaches.
颜色恒常性是指即使在光照变化的情况下，也能够将表面颜色感知为不变的能力。在户外应用中，如移动机器人导航或监控，缺乏这种能力会损害分割、跟踪和目标识别任务。颜色恒常性的主要方法通常针对静态图像，旨在从图像中估计场景光源颜色。我们提出了一种迭代颜色恒常性方法，该方法将时间滤波应用于图像序列，其中参考颜色是从先前校正的图像中估计出来的。此外，还测试了两种从图像中采样颜色的策略。所提出的方法已经使用场景和相机之间没有相对运动的图像序列进行了测试。它还与已知的颜色恒常性算法（如灰色世界、最大RGB和灰色边缘）进行了比较。在大多数情况下，迭代颜色恒常性方法比其他方法取得了更好的结果。
