Deep Learning-based Computer Vision Methods

基于深度学习的计算机视觉方法

• 批准号: 1940016
• 金额: --
• 依托单位: University of Oxford
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2017
• 资助国家: 英国
• 起止时间: 2017 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-1940016
• 关键词: Deep; Learning; based; Computer; Vision

Visual understanding of 3D from images and videoThis project falls within the EPSRC Information and Communication Technologies research area.The objective of this research is to be able to match, describe and categorise image and video content. The aim here is to achieve human like performance and beyond, for example in recognising configurations of parts and spatial layout, counting and delineating objects, or recognising human actions and inter-actions in videos, significantly superseding the current limitations of computer vision systems, and enabling new and far reaching applications. This research will build on top of the success of discriminatively trained recognition systems to recognise visual content in images. The research will involve developing combined generative and discriminative models that can infer the attributes and shape of novel visual material. The new algorithms will learn automatically, building on recent breakthroughs in deep machine learning. They will be capable of weakly-supervised learning, for example from imagines and videos downloaded from the internet, and require very little human supervision.There are applications of this work to image categorisation and search in large image and video datasets. Our vision is that anything visual should be searchable for, in the manner of a Google search of the web: by specifying a query, and having results returned immediately, irrespective of the size of the data. Such enabling capabilities will have widespread application both for general image/video search - consider how Google's web search has opened up new areas - and also for designing customised solutions for searching. Another application is the automatic counting of large numbers of objects in imagines. This is used in a variety of domains, from counting cells in medical images to counting cars in traffic or satellite data.The ubiquity of digital imagine means that every UK citizen may potentially benefit from the Programme research in different ways. One example is an enhanced iplayer that can search for where particular characters appear in a programme, or intelligently fast forward to the next 'hugging' sequence. A second is wider deployment of lower cost imaging solutions in healthcare delivery. A third, also motivated by healthcare, is through the employment of new machine learning methods for validating targets for drug discovery based on microscopy images.

本项目福尔斯属于EPSRC信息和通信技术研究领域。本研究的目标是能够对图像和视频内容进行匹配、描述和分类。其目标是实现类似人类的性能，例如识别部件和空间布局的配置，计数和描绘对象，或识别视频中的人类动作和交互，大大取代计算机视觉系统的当前限制，并实现新的和深远的应用。这项研究将建立在区分训练识别系统的成功之上，以识别图像中的视觉内容。该研究将涉及开发组合生成和判别模型，可以推断新的视觉材料的属性和形状。新算法将自动学习，建立在深度机器学习的最新突破基础上。它们将能够进行弱监督学习，例如从互联网上下载的图像和视频，并且需要很少的人工监督。这项工作可以应用于大型图像和视频数据集的图像分类和搜索。我们的愿景是，任何可视化的东西都应该是可搜索的，就像谷歌搜索网络一样：通过指定一个查询，并立即返回结果，而不管数据的大小。这种使能能力将广泛应用于一般的图像/视频搜索--考虑一下谷歌的网络搜索是如何开辟新领域的--以及设计定制的搜索解决方案。另一个应用是自动计数大量的对象在想象中。这被用于各种领域，从计算医学图像中的细胞到计算交通或卫星数据中的汽车。数字想象的无处不在意味着每个英国公民都可能以不同的方式从该计划的研究中受益。一个例子是一个增强的iplayer，它可以搜索特定角色在节目中出现的位置，或者智能地快进到下一个“拥抱”序列。第二个是在医疗保健服务中更广泛地部署低成本成像解决方案。第三个也是出于医疗保健的动机，是通过采用新的机器学习方法来验证基于显微图像的药物发现目标。