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题名: 采用尺度不变特征和区域选择的图像配准方法
其他题名: Image registration method based on region selection and scale-invariant feature
作者: 李小昌 ; 朱丹
作者部门: 光电信息技术研究室
关键词: 特征点 ; SURF ; 区域选择 ; 图像熵 ; 图像配准
刊名: 红外与激光工程
ISSN号: 1007-2276
出版日期: 2012
卷号: 41, 期号:2, 页码:537-542
收录类别: EI ; CSCD
产权排序: 1
摘要: 提出了一种改进的尺度不变特征点的图像配准方法。该方法在SURF算法的基础上使用图像的熵来对匹配图像作特征检测区域选择,建立特征点筛选机制来从初步的特征检测中得到最显著的特征点,以控制特征点的数目来减少后继的计算量和算法性能。同时改进了SURF的特征描述方法的计算过程,提出了一种改进的特征描述方法。实验表明,该方法在提高算法性能的同时,明显改进了特征点的匹配率。
英文摘要: An improved scale invariant feature extraction method based on speed-up robust feature(SURF) algorithm was proposed.The SURF algorithm was a time-consuming computation,using fast-Hessian detector,which was a differential operator,to get the interest points.Because the statistical property of the image(like image entropy) could present the information abundance,the image could be divided into blocks,to estimate the interest point distribution using the image entropy.The block which had higher image entropy had more interest points.By selecting the image block with higher image entropy to extract the SURF interest point,the computation time can be reduced.Then the SURF feature point orientation computation method was and refined the feature descriptor was improved.The experimental results show that the algorithm is faster than SURF,and has better correct match rate. 更多
语种: 中文
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内容类型: 期刊论文
URI标识: http://ir.sia.cn/handle/173321/10041
Appears in Collections:光电信息技术研究室_期刊论文

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