Seeing Spectral Signatures

在农业食品生产中有许多应用，其中测量从场景反射的光谱而不是测量相机RGB将非常有用。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以及其他任务是有用的。不幸的是，光谱测量设备昂贵、使用麻烦，并且通常不适合在现场部署。相比之下，传统的RGB相机是廉价和可靠的，很容易在实验室外使用。该项目的核心目标是开发算法，用于将由传统相机记录的RGB信号映射到相应的光谱特征，用于解决分类问题，包括成熟与不成熟和植物疾病的诊断。该研究将基于校准，测量和推理。也就是说，我们将了解我们设备的物理特性，我们想要估计的世界光谱，然后我们将开发用于将RGB相机测量映射到光谱的算法。我们的工作还将考虑映射RGB+Near Infra Read相机系统，并开发用于预测扩展到光谱的NIR部分的光谱特征的算法。该研究将与Antobot密切合作开发该项目将重点关注强大算法的开发和现场部署的实用系统的工程。在所有阶段，该项目将与Antobot合作进行。光谱测量，计算机视觉和工程在Antobot的机器人系统的背景下相结合，使一个真正令人兴奋的合作，这将使学生具备独特的技能，桥接传统的研究主导的计算机/颜色科学和工程，并为未来的跨学科职业生涯在Agriforwards地区提供跳板。在整个项目过程中，学生将参加UEA的博士学院-包括其培训计划-以及Agriforwards CDT的队列发展活动。UEA+Antobot团队将定期开会，以审查进展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悠久的历史。这一业绩记录不仅有助于降低风险，而且我们将能够调用我们的网络，以帮助就不可预见的问题提供建议。

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.