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基于形状上下文和方向梯度直方图特征的异源图像配准
Alternative TitleMulti-sensor Image Registration Based on Shape Context and Histograms of Oriented Gradient Feature
黄微1,2,3; 任卫红1,2,3; 朱琳琳4; 田建东1,2
Department机器人学研究室
Source Publication信息与控制
ISSN1002-0411
2019
Volume48Issue:2Pages:149-155
Indexed ByCSCD
CSCD IDCSCD:6496352
Contribution Rank1
Funding Organization国家自然科学基金资助项目(91648118,61503256)
Keyword图像配准 前景检测 形状上下文 方向梯度直方图(HOG)特征
Abstract针对单模态图像包含的信息存在局限性的问题,提出了一种基于形状上下文和HOG(histogram of oriented gradient)特征的红外和可见光图像配准方法.在混合高斯模型前景检测的基础上,通过提出的形状上下文和HOG特征结合的方法实现轮廓特征匹配,再利用TPS(thin plate spline)转换模型将匹配延伸到整个形状,并使用正则化和缩放特性迭代重组对应关系及估计转换降低估计误差.最后,采用RANSAC(random sample consensus)算法去除错误匹配点.与已有的形状上下文方法相比,此方法结合了边缘和轮廓特征信息,降低了误差,鲁棒性更好.
Other AbstractWe propose a registration method for infrared and visible images based on shape context and histogram of oriented gradient (HOG) feature to overcome the limitations of single mode image information. On the basis of foreground detection by Gaussian mixture model, we realize the contour feature matching by the proposed shape context and HOG feature. Matching is extended to the whole shape through the TPS transformation model. Then we use the regularization and scaling characteristics to reorganize the corresponding relation and estimate the transformation in order to reduce the estimation error. Finally, the RANSAC algorithm is used to remove the error matching points. Compared with existing shape context method, this method combines edge and contour feature information with lower error and better robustness.
Language中文
Citation statistics
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/24107
Collection机器人学研究室
Corresponding Author田建东
Affiliation1.中国科学院沈阳自动化研究所机器人学国家重点实验室
2.中国科学院机器人与智能制造创新研究院
3.中国科学院大学
4.沈阳航空航天大学
Recommended Citation
GB/T 7714
黄微,任卫红,朱琳琳,等. 基于形状上下文和方向梯度直方图特征的异源图像配准[J]. 信息与控制,2019,48(2):149-155.
APA 黄微,任卫红,朱琳琳,&田建东.(2019).基于形状上下文和方向梯度直方图特征的异源图像配准.信息与控制,48(2),149-155.
MLA 黄微,et al."基于形状上下文和方向梯度直方图特征的异源图像配准".信息与控制 48.2(2019):149-155.
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