Parametric image alignment using enhanced correlation coefficient maximization
Parametric image alignment using enhanced correlation coefficient maximization
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DOI：
10.1109/tpami.2008.113
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发表时间：
2008-10-01
影响因子：
23.6
通讯作者：
Psarakis, Emmanouil Z.
中科院分区：
文献类型：
--
作者：
Evangelidis, Georgios D.;Psarakis, Emmanouil Z.
在这项工作中，我们建议使用修改版本的相关系数作为图像对齐问题的性能标准。所提出的修正具有相对于光度畸变不变性的理想特性。由于所得到的相似性度量是翘曲参数的非线性函数，因此我们开发了两种迭代方案来实现其最大化，一种是基于正加法方法，另一种是基于逆组合方法。在迭代优化中，按照惯例，在每次迭代中，非线性目标函数用一个替代表达式来近似，该表达式对应的优化很简单。在我们的例子中，我们提出了一个有效的近似，导致一个低计算复杂性的封闭形式的解决方案（每次迭代），后一个性质在我们的反版本中特别强。通过仿真对所提出的方案进行了前向加性Lucas-Kanade和同时逆合成（SIC）算法的测试。在噪声和光度失真条件下，我们的正演版本实现了更精确的对准，并表现出更快的收敛速度，而我们的逆演版本具有与SIC算法相似的性能，但计算复杂度较低。
In this work, we propose the use of a modified version of the correlation coefficient as a performance criterion for the image alignment problem. The proposed modification has the desirable characteristic of being invariant with respect to photometric distortions. Since the resulting similarity measure is a nonlinear function of the warp parameters, we develop two iterative schemes for its maximization, one based on the forward additive approach and the second on the inverse compositional method. As is customary in iterative optimization, in each iteration, the nonlinear objective function is approximated by an alternative expression for which the corresponding optimization is simple. In our case, we propose an efficient approximation that leads to a closed-form solution (per iteration) which is of low computational complexity, the latter property being particularly strong in our inverse version. The proposed schemes are tested against the Forward Additive Lucas-Kanade and the Simultaneous Inverse Compositional (SIC) algorithm through simulations. Under noisy conditions and photometric distortions, our forward version achieves more accurate alignments and exhibits faster convergence, whereas our inverse version has similar performance as the SIC algorithm but at a lower computational complexity.