摘要
针对错误匹配点干扰条件下的多单应矩阵估计问题,提出了一种对错误匹配点鲁棒的多单应矩阵估计初始化方法.该方法基于特征点对的代数误差和结构相似性约束条件,将错误匹配点剔除策略有机地融合到单应矩阵估计的过程中,在不增加计算复杂度的前提下,能够有效地剔除错误匹配点并估计出多单应矩阵的初值.结合AML-COV(approximate maximum likelihood with homography covariance)后端优化算法,本文通过仿真数据实验和真实图像实验从客观性能指标和主观视觉效果方面对算法的性能进行了验证分析.实验结果表明,本文提出的多单应矩阵估计方法能够精确、高效、鲁棒地估计出多单应矩阵的值,较好地解决了错误匹配点干扰条件下的多单应矩阵估计问题.
For the multiple homographies estimation problem in the case of outliers, an initialization method of the multiple homographies estimation robust to outliers is proposed. In this method, the outlier rejection is integrated into the multiple homographies estimation based on the algebraic error and the structure similarity constraint of the key-point correspondences.As a result, the outliers can be removed effectively and the initialization value of multiple homographies can be estimated with a negligible computational overhead. Combining the AML-COV(approximate maximum likelihood with homography covariance) algorithm, several experiments based on simulation data and real images demonstrate the performance of the proposed method in subjective visual quality and objective measurement quality. The experimental results show that the proposed method can achieve accurate, efficient, and robust multiple homographies estimation and performs a good solution to the multiple homographies estimation problem in the case of outliers.
出处
《机器人》
EI
CSCD
北大核心
2017年第5期608-619,共12页
Robot
基金
国家自然科学基金(61203189)
陕西省自然科学基金(2015JQ6226)
人工智能四川省重点实验室开放基金(2016RYJ02)
关键词
多单应矩阵估计
近似极大似然
结构相似性约束
错误匹配点剔除
鲁棒性
multiple homographies estimation
approximate maximum likelihood
structure similarity constraint
outlier rejection
robustness
