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Tracking Categorical Surface Colour Across IUuminant Changes In Natural Scenes

机译:跟踪自然场景中主要变化的分类表面颜色

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How well can categorical colour perception be maintained in natural environments with varying illuminants? To address this question, a colour-naming experiment was performed with colour-monitor images of natural scenes simulated under two different daylights of correlated colour temperature 6500 K and 25000 K. Images were obtained from a set of hyperspectral data to enable the accurate control of illuminant and reflectance spectra. Each scene contained a spherical test surface whose digitally manipulated spectral reflectance coincided with that of a sample drawn randomly from approximately 430 Munsell reflectances grouped into eight colour categories, namely, red, green, blue, yellow, pink, purple, brown, and orange. Observers had to name the colour of the test surface in each image, presented for 1 s, by pressing one of nine computer keys corresponding to the eight categorical colour names and a neutral category. Focal colours were estimated from the peaks of the smoothed distributions of observers' naming responses in the C1E 1976 (u', v') chromaticity diagram. The effect of the illuminant change was quantified by a focal colour constancy index, with values 0 and 1 corresponding to no constancy and perfect constancy. Average levels of focal colour constancy were close to those from traditional measures of colour constancy, but with variation across categories and surface lightness. For blue and purple surfaces levels approached 0.9. For many surface colours, colour naming seems to be robust under illuminant changes and may help to anchor non-categorical judgements of arbitrary surface colours in natural scenes.
机译:在自然环境中使用不同的光源,分类色彩的感知能力如何保持良好?为了解决这个问题,我们进行了色彩命名实验,对自然场景的彩色监视器图像进行了模拟,该图像在两种相关色温6500 K和25000 K的不同日光下进行了模拟。图像是从一组高光谱数据中获得的,以能够精确控制光源和反射光谱。每个场景都包含一个球形的测试表面,其数字控制的光谱反射率与从大约430个孟塞尔反射率中随机抽取的样本的光谱值重合,后者被分为八种颜色类别,即红色,绿色,蓝色,黄色,粉红色,紫色,棕色和橙色。观察者必须通过按下与八个分类颜色名称和一个中性类别相对应的九个计算机键之一来命名每个图像中测试表面的颜色,持续1 s。根据C1E 1976(u',v')色度图中观察者命名响应的平滑分布的峰值来估计焦点颜色。光源变化的影响通过焦点颜色恒定指数进行量化,值0和1对应于无恒定和完美恒定。焦点颜色恒定性的平均水平与传统颜色恒定性的测量值接近,但类别和表面亮度之间存在差异。对于蓝色和紫色表面,水平接近0.9。对于许多表面颜色,颜色命名在光源变化下似乎很可靠,并且可能有助于锚定自然场景中任意表面颜色的非分类判断。

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