Deep Learning-based Computer Vision Methods

从图像和视频中视觉理解3D这个项目属于EPSRC信息和通信技术研究领域。这项研究的目标是能够匹配、描述和分类图像和视频内容。这里的目标是实现与人类相似的性能和更高的性能，例如在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相互作用方面，显著取代目前计算机视觉系统的限制，并实现新的和深远的应用。这项研究将建立在区别训练识别系统成功识别图像中的视觉内容的基础上。这项研究将涉及开发组合的生成和区分模型，可以推断新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最近的突破的基础上。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。这项工作已应用于在大型图像和视频数据集中进行图像分类和搜索。我们的愿景是，任何可视化的东西都应该可以搜索，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，而不考虑数据的大小。这种支持能力将广泛应用于一般的图像/视频搜索--想想谷歌的网络搜索如何开辟了新的领域--也用于设计定制的搜索解决方案。另一个应用是对想象中的大量物体进行自动计数。这项技术应用于许多领域，从医学图像中的细胞计数到交通或卫星数据中的汽车计数。数字图像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。一个例子是增强型iPlayer，它可以搜索节目中特定角色出现的位置，或者智能地快进到下一个拥抱序列。第二是在医疗保健服务中更广泛地部署成本较低的成像解决方案。第三个也是受医疗保健的推动，是通过使用新的机器学习方法，基于显微镜图像来验证药物发现的目标。

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.