Distinguishing visually similar objects like forged/authentic bills and healthy/unhealthy plants is beyond the capabilities of even the most sophisticated classifiers. We propose the use of multiplexed illumination to extend the range of objects that can be successfully classified. We construct a compact RGB-IR light stage that images samples under different combinations of illuminant position and colour. We then develop a methodology for selecting illumination patterns and training a classifier using the resulting imagery. We use the light stage to model and synthetically relight training samples, and propose a greedy pattern selection scheme that exploits this ability to train in simulation. We then apply the trained patterns to carry out fast classification of new objects. We demonstrate the approach on visually similar artificial and real fruit samples, showing a marked improvement compared with fixed-illuminant approaches as well as a more conventional code selection scheme. This work allows fast classification of previously indistinguishable objects, with potential applications in forgery detection, quality control in agriculture and manufacturing, and skin lesion classification.

中文翻译：

---

用于分类视觉相似对象的多路复用照明

即使是最复杂的分类器也无法区分视觉上相似的物体，例如伪造/真品钞票和健康/不健康的植物。我们建议使用多路复用照明来扩展可以成功分类的对象范围。我们构建了一个紧凑的 RGB-IR 光级，在不同的光源位置和颜色组合下对样本进行成像。然后，我们开发了一种方法，用于选择照明模式并使用生成的图像训练分类器。我们使用光照阶段对训练样本进行建模和综合重新光照，并提出了一种利用这种能力在模拟中进行训练的贪婪模式选择方案。然后我们应用经过训练的模式来对新对象进行快速分类。我们在视觉上相似的人造和真实水果样本上演示了该方法，与固定光源方法以及更传统的代码选择方案相比，显示出显着的改进。这项工作允许对以前无法区分的物体进行快速分类，在伪造检测、农业和制造业的质量控制以及皮肤病变分类方面具有潜在应用。