CAREER: Integrated system for object and scene recognition

职业：物体和场景识别集成系统

• 批准号: 0747120
• 负责人: Antonio Torralba
• 金额: $ 50万
• 依托单位: Massachusetts Institute of Technology
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-04-01 至 2013-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0747120&HistoricalAwards=false
• 关键词: CAREER; Integrated; system; object; scene

AbstractTitle: Integrated system for object and scene recognitionPI: Antonio TorralbaInstitution: MITIn traditional computer vision, scene and object recognition are two related visual tasks generally studied separately. By devising systems that solve these tasks in an integrated fashion it is possible to build more efficient and robust recognition systems. At the lowest level, significant computational savings can be achieved if different categories share a common set of features. More importantly, jointly trained recognition systems can use similarities between object categories to their advantage by learning features which lead to better generalization. In complex natural scenes, object recognition systems can be further improved by using contextual knowledge both about the objects likely to be found in a given scene, and also the spatial relationships between those objects. Object detection and recognition is generally posed as a matching problem between the object representation and the image features while rejecting the background features using an outlier process. The PI will formulate object detection as a problem of aligning elements of the entire scene. The background, instead of being treated as a set of outliers will be used to guide the detection process.In developing integrated systems that try to recognize many objects, the lack of large annotated datasets becomes a major problem. The PI created and will extend two datasets; LabelMe and the 80 million tiny images datasets. LabelMe is an online annotation tool that allows sharing and labeling images for computer vision research. Both datasets offers an invaluable resource for research and teaching on computer vision and computer graphics. The datasets are also intended to foster creativity, as they allows students at all levels to explore well established algorithms as well as devise new applications in computer vision and computer graphics. The PI will also develop new image and video datasets by exploiting the millions of images available on the internet.The creation of robust systems for scene understanding will have a major impact on many fields by allowing the creation of smart devices able to interact and understand their environment, from aids to the visually-impaired, to autonomous vehicles, robotic assistants, or online tools for searching visual information.The PI will extend his teaching and research activities beyond the boundaries of the classroom and the laboratory by developing a substantial amount of online material.URL: http://people.csail.mit.edu/torralba/integratedSceneRecognition/

摘要标题：PI：Antonio Torralba Institution：MIT在传统的计算机视觉中，场景和物体识别是两个相互关联的视觉任务，通常被分开研究。通过设计以集成方式解决这些任务的系统，可以构建更有效和更强大的识别系统。在最低级别，如果不同的类别共享一组共同的特征，则可以实现显著的计算节省。更重要的是，联合训练的识别系统可以通过学习导致更好泛化的特征来利用对象类别之间的相似性。在复杂的自然场景中，可以通过使用关于在给定场景中可能发现的对象以及这些对象之间的空间关系的上下文知识来进一步改进对象识别系统。目标检测和识别通常被视为目标表示和图像特征之间的匹配问题，同时使用离群值处理来拒绝背景特征。PI将把目标检测公式化为整个场景的对齐元素的问题。背景，而不是被视为一组离群值将被用来指导检测过程。在开发集成系统，试图识别许多对象，缺乏大型注释数据集成为一个主要问题。PI创建并将扩展两个数据集：LabelMe和8000万个微小图像数据集。LabelMe是一个在线注释工具，允许共享和标记计算机视觉研究的图像。这两个数据集为计算机视觉和计算机图形学的研究和教学提供了宝贵的资源。这些数据集还旨在培养创造力，因为它们允许各级学生探索成熟的算法，并设计计算机视觉和计算机图形学的新应用。PI还将利用互联网上的数百万张图像开发新的图像和视频数据集。创建强大的场景理解系统将对许多领域产生重大影响，因为它允许创建能够交互和理解其环境的智能设备，从辅助设备到视力受损者，再到自动驾驶汽车，机器人助手，或在线工具搜索视觉信息。PI将通过开发大量在线材料，将其教学和研究活动扩展到课堂和实验室之外。URL：http://people.csail.mit.edu/torralba/integratedSceneRecognition/