周诗豪,张云.对数极坐标特征指导的迭代就近点法[J].计算机科学,2018,45(1):297-306
对数极坐标特征指导的迭代就近点法
Log-polar Feature Guided Iterative Closest Point Algorithm
投稿时间:2016-12-26  修订日期:2017-03-31
DOI:10.11896/j.issn.1002-137X.2018.01.052
中文关键词:  图像匹配,尺度不变特征点,对数极坐标空间,立体视觉
英文关键词:Image registration,Scale-invariant feature point,Log-polar space,Stereo vision
基金项目:本文受国家自然科学基金项目(61262043),云南省科技计划项目(2011FZ029)资助
作者单位E-mail
周诗豪 昆明理工大学信息工程与自动化学院 昆明650500昆明理工大学云南省计算机技术应用重点实验室 昆明650500 772881831@qq.com 
张云 昆明理工大学信息工程与自动化学院 昆明650500昆明理工大学云南省计算机技术应用重点实验室 昆明650500  
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中文摘要:
      在多模态图像,有光照、取向及尺度或纹理变化的图像,以及宽基线图像之间通常存在大的形貌畸变。然而,当前主流推广的双自举迭代就近点法(GDB-ICP)在配准这类图对时存在困难。主要原因是,该方法利用提取的尺度不变泡点(SIFT keypoint)来引导迭代就近点法(ICP),但在大畸变图像上提取的SIFT点是不可靠的。为此,提出了一种用对数极坐标特征点(LPF)来引导迭代就近点的图像配准新方法(LPF-ICP)。实验结果表明,LPF-ICP方法成功地从Rensselaer数据组中的所有22对挑战性图对提取了可靠的LPF种子,并顺利实现了全图配准,而GDB-ICP方法则只完成了其中的19对,从而证实了LPF-ICP方法的有效性。
英文摘要:
      Images with lighting variations,rotation/optical zoom,physical changes of scene or at widely different viewpoints,can substantially change their appearance and shape when they are acquired using different modalities.Even with the state-of-the-art technology,e.g.,the generalized dual-bootstrap iterative closest point (GDB-ICP) method,it is still difficult to register those challenging images.The reason is that the GDB-ICP method uses the scale-invariant blub points (or SIFT keypoints) to drive the iterative closest point method (ICP).However,the SIFT keypoints cannot be reliably extracted from images with large appearance changes.To handle this issue,this paper proposed a novel log-polar feature guided iterative closest point (LPF-ICP) algorithm for image registration.The experimental evaluation illustrates that the LPF-ICP method can reliably extract the log-polar feature points and successfully register all the 22 ima-ge pairs contained in the Rensselaer dataset,while the GDB-ICP method only succeeds in 19 of them,thus verifying the effectiveness of the proposed method.
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