Object class recognition in 3D image data

3D 图像数据中的对象类别识别

• 批准号: 249858-2011
• 负责人: Greenspan, Michael
• 金额: $ 2.33万
• 依托单位: Queen's University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2014
• 资助国家: 加拿大
• 起止时间: 2014-01-01 至 2015-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=547425
• 关键词: Object; class; recognition; 3D; image

Developing a solution to the problem of object recognition which automatically identifies and locates items within a scene would achieve one of the fundamental goals of computer vision, placing Canada at the forefront of revolutionary technology. Object recognition is a longstanding and difficult problem, and there has been much progress in recent years, which has led to fast and effective prototypes. At the same time, there is a pull toward the application of recognition methods in commercial systems, initially in fairly constrained domains such as optical character recognition, licence plate readers, gaming, face recognition and biometrics, and increasingly in more general scenarios. The objective of the proposed research is to develop effective object class recognition methods, in general scenes, using 3D range data. Effective object class recognition methods are the essential missing component to the advent of more autonomous intelligent systems. The coming advances to the field will elevate this technology to a high level of utility, enabling a new generation of intelligent robotic systems that can interact more flexibly in human environments. Society will benefit from manufacturing applications, such as flexible automation, but also from widespread deployment into everyday applications such as security and surveillance, and health-care and service robots.

开发一种自动识别和定位场景中物品的物体识别问题的解决方案，将实现计算机视觉的基本目标之一，使加拿大处于革命性技术的最前沿。物体识别是一个长期存在的难题，近年来已经取得了很大的进展，这导致了快速有效的原型。与此同时，商业系统中的识别方法的应用也受到了吸引，最初是在相当有限的领域，如光学字符识别，车牌阅读器，游戏，面部识别和生物识别，并且越来越多地在更一般的场景中应用。 所提出的研究的目的是开发有效的对象类识别方法，在一般场景中，使用3D范围数据。有效的对象类别识别方法是更自主的智能系统出现的重要组成部分。该领域即将取得的进展将把这项技术提升到高水平的实用性，使新一代智能机器人系统能够在人类环境中更灵活地进行交互。社会将受益于制造应用，如灵活的自动化，但也将受益于日常应用的广泛部署，如安全和监控，以及医疗保健和服务机器人。