论文标题
多重照明用于分类视觉上相似的对象
Multiplexed Illumination for Classifying Visually Similar Objects
论文作者
论文摘要
区分视觉上相似的物体，例如锻造/真实的账单和健康/不健康的植物，甚至超出了最复杂的分类器的功能。我们建议使用多重照明来扩展可以成功分类的对象范围。我们构建一个紧凑的RGB-IR光阶段，该阶段图像在不同的位置和颜色的不同组合下样本样本。然后，我们开发一种方法来选择照明模式并使用所得图像训练分类器。我们使用光阶段来建模并合成重新训练样本，并提出一种贪婪的模式选择方案，以利用这种训练能力进行模拟。然后，我们应用训练有素的模式来进行新对象的快速分类。我们证明了视觉上类似的人造和真实果实样品的方法，与固定持久方法以及更传统的代码选择方案相比，显示出明显的改进。这项工作允许快速分类以前无法区分的物体，并在伪造发现，农业和制造业中的质量控制以及皮肤病变分类中进行了潜在的应用。
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.
