摘要
为提高图像拼接时的配准速度和精度,针对鲁棒性模型估计问题,提出一种基于行列式点过程的改进RANSAC算法(Random Sample Consensus).该方法利用行列式点过程抽样法的全局负相关特性对匹配的特征点进行建模,实现抽样点的均匀化和分散化,剔除一些错误匹配点.用行列式点过程抽取的点集作为RANSAC算法的输入来求取变换矩阵.实验结果表明:该算法相对于传统的RANSAC算法,能够保持较高的精度和鲁棒性,减少传统RANSAC算法迭代次数,显著提升图像自动拼接的计算效率.
To improve the speed and precision of registration in image stitching, this study proposes a modified RANSAC algorithm based on Determinantal Point Processes（DPP）, aiming to tackle the issue of robustness model estimation. This method utilizes global negative correlation of the DPP sampling to model matching feature points, eliminates those incorrect matching points, and therefore realizes the homogenization and decentralization of the sampling. The point set extracted in DPP is used as the input of RANSAC to elicit transformation matrix. Experimental results show that compared with traditional RANSAC algorithm, this algorithm ensures higher accuracy and robustness, which greatly enhances the efficiency of automatic image stitching.
作者
汪旌
张赟
陈爽
WANG Jing;ZHANG Yun;CHEN Shuang(Institute of Graphic and Image, School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018 China;Institute of Zhejiang Radio and Television Technology, Zhejiang University of Media and Communications, Hangzhou 310018 China;Institute of Information Science and Engineering, Shenyang University of Technology, Shenyang 110023, China)
出处
《计算机系统应用》
2018年第5期112-118,共7页
Computer Systems & Applications
基金
国家自然科学基金(61602402)
浙江省公益技术研究项目(2016C31085)
关键词
RANSAC算法
行列式点过程
配准
图像拼接
特征点匹配
概率分布
RANSAC algorithm
Determinantal Point Process （DPP）
registration
image stitching
lbature points matching
probability distributions
