Applied and Computational Mathematics Program, Princeton University, Princeton, NJ 08544-1000, USA.
IEEE Trans Image Process. 2012 Sep;21(9):3915-23. doi: 10.1109/TIP.2012.2198222. Epub 2012 May 8.
It is well-known that box filters can be efficiently computed using pre-integration and local finite-differences. By generalizing this idea and by combining it with a nonstandard variant of the central limit theorem, we had earlier proposed a constant-time or O(1) algorithm that allowed one to perform space-variant filtering using Gaussian-like kernels. The algorithm was based on the observation that both isotropic and anisotropic Gaussians could be approximated using certain bivariate splines called box splines. The attractive feature of the algorithm was that it allowed one to continuously control the shape and size (covariance) of the filter, and that it had a fixed computational cost per pixel, irrespective of the size of the filter. The algorithm, however, offered a limited control on the covariance and accuracy of the Gaussian approximation. In this paper, we propose some improvements of our previous algorithm.
众所周知,可以使用预积分和局部有限差分有效地计算盒式滤波器。通过推广这个想法,并将其与中心极限定理的非标准变体相结合,我们之前提出了一种恒定时间或 O(1)算法,该算法允许使用类似高斯的核进行空间变化滤波。该算法基于这样的观察结果,即各向同性和各向异性高斯都可以使用某些称为盒样条的双变量样条来近似。该算法的一个吸引人的特点是,它允许连续控制滤波器的形状和大小(协方差),并且它每像素的计算成本固定,与滤波器的大小无关。然而,该算法对高斯逼近的协方差和精度的控制有限。在本文中,我们提出了对我们之前算法的一些改进。