一致分支点漂移算法

CJPD:Coherent Junction Point Drift for Junction Points Set

分支点是多传感器图像之间一种重要的关联特征,因此分支点匹配对多传感器图像配准有着十分重要的意义。基于CPD的基本思想提出了一致分支点漂移算法。针对分支点自身特点,提出了局部结构相容度的概念,用于度量和检验两个分支点的一致性程度;并将其作为匹配约束项嵌入到高斯混合模型分量的后验概率计算中,有效利用了分支点包含的分支边缘等结构信息,同时增强了算法对噪声和外点等干扰因素的抵抗能力,提高了分支点匹配的收敛速度。实验结果表明,提出的一致分支点漂移算法比CPD算法能够更快收敛到最优参数集上,同时得到的分支点配准精度更高。

Junction is an important associate feature among the multi-sensor images and junction point set matching plays a key role of multi-sensor images registration. In this paper, coherent junction point drift for affine transformation （CJPD） was proposed. According to the inherent characteristic of junction,we defined the local structural consistency, which is used to measure the similarity between two junctions. What＇s more,we introduced local structural consistency of junctions as a constraint of the posterior probabilities of GMM components. The added structural information improves the robustness of CJPD for noise and outliers and speeds up its convergence. We tested the CJPD algorithm for affine transformation in the presence of noise and outliers, where CJPD shows more accurate results and outperforms current state-of-the-art methods than CPD.