Seeking a better view: Guiding cameras for optimal imaging via reinforcement learning

寻求更好的视野：通过强化学习引导相机实现最佳成像

• 批准号: 2750751
• 金额: --
• 依托单位: University of Nottingham
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2750751
• 关键词: Seeking; better; view; Guiding; cameras

In Biosciences, image capture is often used in microscopy, lab and field environments as the first step in obtaining reliable scientific measurements of plants. Good image capture is fundamental to many of these experiments, by measuring plants you can determine which are healthier, more robust, or producing more food. However, you can't measure what you can't see, and, capturing a better image could be crucial in measuring the subtle differences which indicate a higher yielding crop, or a resistance to pests.This project will explore reinforcement and machine learning approaches to train robotic systems to capture better images with no human interaction. It will evaluate varied reward systems to train AI that can provide guidance on which view is likely to be best given what has been seen so far. Working across disciplines, the PhD will training automated image capture systems on a variety of plant subjects ranging in size and shape, with a view to improving the performance on a range of tasks including 3D reconstruction, feature detection and counting. The robotics will include linear 1D and 2D actuators, as well as a 6-dof robotic manipulator.

在生物科学中，图像捕获通常用于显微镜、实验室和野外环境，作为获得可靠的植物科学测量的第一步。良好的图像捕捉是许多这些实验的基础，通过测量植物，您可以确定哪些更健康，更健壮，或生产更多的食物。然而，你无法测量你看不到的东西，捕捉更好的图像对于测量指示更高产量的作物或抗虫害的细微差异至关重要。该项目将探索强化和机器学习方法，以训练机器人系统在没有人类交互的情况下捕捉更好的图像。它将评估各种奖励系统来训练人工智能，这些系统可以提供指导，说明到目前为止所看到的最佳视图。跨学科工作，博士将培训自动图像捕捉系统对各种植物主题的大小和形状，以期提高性能的一系列任务，包括三维重建，特征检测和计数。机器人将包括线性1D和2D致动器，以及6-dof机器人操纵器。