摘要
提出一种针对复杂标识的分类学习算法,并将其应用于移动增强现实系统中,实现了基于自然特征的跟踪定位系统.在场景特征点识别分类基础上,采用关键帧匹配算法实现无标识跟踪定位.针对含有对称结构的场景提出一种误匹配特征的回收机制.实验结果表明,该算法可解决由于场景对称结构导致的错误特征匹配,从而大幅提高特征的正确匹配率.
This paper presents a supervised machine learning method to detect and track complex man-made logos in real-time.The key-frame based registration method is applied to estimating the camera pose and the randomized tree method is used to matching key-points which are extracted from the input image and from key-frames.In order to overcome the problem of false feature matching caused by the repetitive texture in the real environment,a false feature matching recovery mechanism is also proposed to effectively improve the feature matching performance.The presented algorithm has been applied to the mobile augmented reality system.
出处
《北京理工大学学报》
EI
CAS
CSCD
北大核心
2012年第2期189-193,共5页
Transactions of Beijing Institute of Technology
基金
国家自然科学基金资助项目(60309070)
国家"八六三"计划项目(2009AA01Z309)
光电成像技术与系统教育部重点实验室(北京理工大学)开放基金项目(2010OEIOF03)
教育部长江学者和创新团队发展计划项目(IRT0606)
关键词
特征识别
增强现实
跟踪注册
重复纹理
feature recognition
augmented reality
registration
repetitive texture
作者简介
刘伟（1981-），男，博士生，Email：bitlw@bit．edu．cn；
王涌天（1957-），男，教授，博士生导师，E-mail：wyt@bit．edu．cn．
