Direct methods are widely used for alignment of models to images, due to their accuracy, since they minimize errors in the domain of measurement noise. They have leveraged least squares minimizations, for simple, efficient, variational optimization, since the seminal 1981 work of Lucas & Kanade, and normalized cross correlation (NCC), for robustness to intensity variations, since at least 1972. Despite the complementary benefits of these two well known methods, they have not been effectively combined to address local variations in intensity. Many ad-hoc NCC frameworks, sub-optimal least squares methods and image transformation approaches have thus been proposed instead, each with their own limitations. This work shows that a least squares optimization of NCC without approximation is not only possible, but straightforward and efficient. A robust, locally normalized formulation is introduced to mitigate local intensity variations and partial occlusions. Finally, sparse features with oriented patches are proposed for further efficiency. The resulting framework is simple to implement, computationally efficient and robust to local intensity variations. It is evaluated on the image alignment problem, showing improvements in both convergence rate and computation time over existing lighting invariant methods.

翻译：由于其准确性,模型与图像的匹配广泛使用直接方法,因为这些方法可以最大限度地减少测量噪音领域的误差;自卢卡斯和卡纳德1981年的开创性工作以来,它们为简单、高效、可变优化而利用最小正方最小的最小优化;自1972年以来,为了稳健地适应强度变化,采用了标准化的交叉关系(NCC);尽管这两种众所周知的方法具有互补效益,但它们并没有有效地结合到解决当地强度变化方面的地方差异;因此,提出了许多特别的NCC框架、最优化的最小正方和图像转换方法,每个都有各自的局限性;这项工作表明,不近似的NCC最不优化的正方最小的正方,不仅是可能的,而且是直接和高效的;采用了一种稳健的当地标准化的配方公式,以缓解当地强度变化和部分隔离;最后,提出了具有定向补丁点的零散特征,以提高效率;因此,框架易于实施、计算有效和稳健地适应当地强度变化;对图像调整问题进行了评估,显示在现有易变的照明方法上,趋近率和计算时间的改进。