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首页> 外文会议>Pacific-Rim conference on multimedia >Color and Active Infrared Vision: Estimate Infrared Vision of Printed Color Using Bayesian Classifier and K-Nearest Neighbor Regression
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Color and Active Infrared Vision: Estimate Infrared Vision of Printed Color Using Bayesian Classifier and K-Nearest Neighbor Regression

机译：颜色和活性红外视野：使用贝叶斯分类器和K-最近邻回归估算印刷颜色的红外视野

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摘要

Speaking of active infrared vision, its inability to see physical colors has long been considered as one major drawback or something everybody has paid no attention to until very recently. Looking at this color blindness from other perspective, we propose an idea of a novel medium whose visibilities in both visible and active infrared light spec-trums can be controlled, enabling vision-based techniques to transform everyday printed media into smart, eco-friendly and sustainable monitor-like interactive displays. To begin with, this paper observes the most important key success procedure regarding the idea-estimating how physical colors should look like when being seen by an active infrared camera. Two alternative methods are proposed and evaluated here. The first one uses Bayesian classifier to find some color-attribute combinations that can precisely classify our sample data. The second alternative relies on simple weighted average and k-nearest neighbor regression in two color models-RGB and CIE L*a*b*. Suggesting by experimental results, the second method is more practical and consistent at different distances. Besides, it shows likelihoods of the model created in this work being able to estimate infrared vision of colors printed on different material.
机译：谈到积极的红外视野，无法看到物理色彩长期被认为是一个主要的缺点或每个人都没有注意到最近没有注意。从其他角度看这种颜色的失明，我们提出了一种新颖的媒介，可以控制可见和活跃的红外光谱法的可见性，从而实现基于视觉的技术，将每天印刷媒体转换为智能，环保和可持续的监视器类似的交互式显示。首先，本文观察了关于思想估计的最重要的关键成功程序，当由有源红外相机看到的时，物理颜色应该如何看起来像。这里提出并评估了两种替代方法。第一个使用贝叶斯分类器找到一些可以精确分类我们的样本数据的颜色属性组合。第二种替代方案依赖于两种颜色模型中的简单加权平均值和k最近邻回归-RGB和CIE L * A * B *。通过实验结果表明，第二种方法更实用，在不同的距离下保持一致。此外，它显示了在这项工作中创建的模型的可能性，能够估计印刷在不同材料上的颜色的红外视野。

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