Seeing Spectral Signatures

在农业食品生产中有许多应用，测量场景反射的光的光谱比测量相机的RGB非常有用。事实上，光谱特征对于识别植物物种、监测生长和确定疾病的存在等任务是有用的。不幸的是，光谱测量设备价格昂贵，使用起来很麻烦，而且通常不适合在现场部署。相比之下，传统的RGB相机价格便宜，可靠性高，易于在实验室外使用。该项目的核心目标是开发算法，将传统相机记录的RGB信号映射到相应的光谱特征，从而有助于解决包括成熟与不成熟以及植物病害诊断在内的分类问题。研究将建立在校准、测量和推断的基础上。也就是说，我们将了解我们设备的物理特性，我们希望估计世界上的光谱，然后我们将开发将RGB相机测量结果映射到光谱的算法。我们的工作还将考虑绘制RGB+近红外阅读相机系统的地图，并开发预测光谱特征的算法，以扩展到光谱的近红外部分。这项研究将在Antobot的密切合作下进行，该项目将双重专注于开发健壮的算法和在外地部署的实用系统的工程。在所有阶段，该项目都将与Antobot合作进行。在Antobot机器人系统的背景下，光谱测量、计算机视觉和工程的结合形成了一种真正令人兴奋的合作，将使学生拥有一种独特的技能集，将传统研究引领的计算机/颜色科学与工程联系起来，并为未来在Agriforward领域的跨学科职业生涯提供跳板。在整个项目中，学生将参加UEA的博士学院--包括其培训计划--以及Agriforward CDT的队列开发活动。UEA+Antobot团队将定期会面，审查进展情况并制定近期和中期计划(从而提供识别和管理风险的机会)。UEA团队在帮助工业合作伙伴更好地使用他们的视觉系统方面有着长期的记录。这一记录不仅有助于降低风险，而且我们将能够呼吁我们的网络帮助提供关于不可预见的问题的建议。

There are many application in agri-food production where it would be very useful to measure the spectrum of light reflected from a scene rather than measuring a camera RGB. Indeed spectral signatures are useful for identifying plant species, monitoring growth and to determine the presence of disease, among other tasks . Unfortunately, spectral measurement devices are expensive, cumbersome to use, and often unsuitable for deployment in the field. In contrast conventional RGB cameras are cheap and reliable and are easy to use outside the lab.The core aim of this project is to develop algorithms for mapping the RGB signal recorded by a conventional camera to corresponding spectral signatures that are useful for solving classification problems including ripe vs non-ripe and the diagnosis of plant diseases. The research will be based on calibration, measurement and inference . That is, we will understand the physical characteristics of our device, the spectra in the world we would like to estimate and then we will develop algorithms for mapping the RGB camera measurements to spectra. Our work will also consider mapping RGB+Near Infra Read camera systems and develop algorithms for predicting spectral signature that extend to the NIR part of the spectrum. The research will be developed in close collaboration Antobot The project will dually focus on the development of robust algorithms and the engineering of practical systems that are deployed in the field. At all stages the project will take place in collaboration with Antobot. The combintion of spectral measurement, computer vision and engineering in the context of Antobot's robotic system makes for a truly exciting collaboration that will equip the student with a unique skillset bridging traditional research led computer /color science and engineering and provide springboard for a future inter-disciplinary career in the Agriforwards area. Throughout the project the student will participate in UEA's doctoral College - including its training programme - and the cohort development activities of the Agriforwards CDT.The UEA+Antobot team will meet regularly to review progress and plan for the near and medium term (thus providing an opportunity to identify and then manage risk). The UEA team have a long track record in helping industrial partners to better use their vision systems. Not only does this track record serve to mitigate risk but we will be able to call on our network to help advise on unforeseen problems.