关键词: 正则项, 凹函数, 空间变换, 点对应关系, 特征点匹配, 匈牙利算法

Abstract: For the existing point matching algorithms adopting the l1 norm regularization terms, the corresponding l1 norm optimization problems are equivalent to linear programs. But the constraints do not satisfy the total unimodularity property, which causes the point correspondence solutions to be non-integers and post-processing is needed to convert the solutions to integers. Such processing brings error and complicates the algorithms. To resolve the above problem, based on the latest result with the robust point matching algorithm, a new regularization term was proposed. The new regularization term is concave and it can be proved that the objective function has integral optimal solutions. Therefore, no post-processing is needed and it is simpler to implement. The experimental results show that, compared with the algorithms adopting the l1 norm regularization terms, the proposed algorithm is more robust to various types of disturbances, particularly outliers, while its error is only half of the compared algorithms.

Key words: regularization term, concave function, spatial transformation, point correspondence, feature point matching, Hungarian algorithm