Non-supervised chromatic illuminant: corrector for autonomous robots

D. Marini, A. Rizzi
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引用次数: 1

Abstract

One of the well-known problems in colour image interpretation is the colour-constancy problem. Autonomous robots that use colour information to select objects or landmarks can be deceived in presence of heavy coloured illuminants. Classic chromatic filtering presupposes detailed information about light source characteristics, but this is not always possible. The presence of emergency lights or different kinds of light sources can heavily influence object colour. Retinex theory, by Land and McCann (1971), can resolve these problems. This theory gives color perception on a color space based on three brightness computed as relative reflectance along multiple exploration paths of the perceived scene. This paper considers the application of this theory in order to allow automatic colour detection in autonomous robots. The algorithm has been tested on simple coloured scenes illuminated with different light sources. The results obtained are compared.
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非监督色光:自主机器人的校正器
彩色图像解译中一个众所周知的问题是色彩恒常性问题。使用颜色信息来选择物体或地标的自主机器人可能会在色彩浓重的光源面前被欺骗。经典的色度滤波以光源特性的详细信息为前提,但这并不总是可能的。应急灯或各种光源的存在会严重影响物体的颜色。Land和McCann(1971)的视网膜理论可以解决这些问题。该理论在色彩空间上给出了基于三个亮度的色彩感知,这些亮度是沿着被感知场景的多个探索路径计算的相对反射率。本文考虑了该理论的应用,以便在自主机器人中实现自动颜色检测。该算法已经在不同光源照明的简单彩色场景中进行了测试。并对所得结果进行了比较。
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