A generalized model is developed to quantitatively describe the smoothing effects from different polishing tools used for optical surfaces. The smoothing effect naturally corrects mid-to-high spatial frequency errors that have features small compared to the size of the polishing lap. The original parametric smoothing model provided a convenient way to compare smoothing efficiency of different p...

Commercially available intensity-modulated radiation therapy (IMRT) inverse treatment planning systems (ITPS) typically include a smoothing function which allows the user to vary the complexity of delivered beam fluence patterns. This study evaluated the behavior of three ITPSs when varying smoothing parameters. We evaluated four cases treated with IMRT in our clinic: sinonasal carcinoma (SNC),...

Smoothing techniques are of major importance for the generation of surface meshes. For this reason a considerable amount of research has been spent on developing a large variety of sophisticated smoothing approaches. However, these methods either require direct access to the analytic surface description or are restricted to flat meshes in two dimensions. In particular, if no analytic surface da...

‎in this paper‎, ‎we present a novel approach for image selective smoothing by the evolution of two paired nonlinear‎ ‎partial differential equations‎. ‎the distribution coefficient in de-noising equation controls the speed of distribution‎, ‎and is‎ ‎determined by the edge-strength function‎. ‎in the previous works‎, ‎the edge-strength function depends on isotropic‎ ‎smoothing of the image‎, ‎...

In certain contexts, maximum entropy (ME) modeling can be viewed as maximum likelihood training for exponential models, and like other maximum likelihood methods is prone to over tting of training data. Several smoothing methods for maximum entropy models have been proposed to address this problem, but previous results do not make it clear how these smoothing methods compare with smoothing meth...

In this paper, we propose and validate a computationally efficient non-iterative domain decomposition procedure for calculating bivariate cubic L1 smoothing splines. This domain decomposition procedure involves calculating local L1 smoothing splines individually on overlapping “extended subdomains” that cover the global domain and then creating the global L1 smoothing spline by patching togethe...

In certain contexts, maximum entropy (ME) modeling can be viewed as maximum likelihood (ML) training for exponential models, and like other ML methods is prone to overfitting of training data. Several smoothing methods for ME models have been proposed to address this problem, but previous results do not make it clear how these smoothing methods compare with smoothing methods for other types of ...