Kernel smoothing M.C. Jones, M.P. Wand
Publisher: Chapman & Hall

So the automatic selection of the smoothing parameter doesn't work in this case. Figure 4 demonstrates the average preprocessed spectra of T. (and can over-smoothing be tuned/quantified?) I don't know if smoothing along one direction independently of the other makes sense, or if it's better to smooth in 2D. Now, it's time to find out how gaussian_kde actually selects the smoothing parameter or bandwith of the kernel. The kernel is sampled and normalized using the 2D Gaussian function . This is a companion to Matlab's Statistics toolbox ksdensity function and Philipp Berens' CircStat toolbox. This code takes image name and the kernel size as arguments and does Gaussian smoothing by creating a Gaussian kernel of specified size and apply the kernel to the image. The output will be a blurred image. In the method of "kernel smoothing density," i.e. Smoothing or blurring is a type of operation which attempts to eliminate the noise while preserving the structure of the image. This Demonstration shows the smoothing of an image using a 2D convolution with a Gaussian kernel. In general cases, when the smoothing factor tends to infinity, minimizing the smoothed error entropy will be approximately equivalent to minimizing error variance, regardless of the conditional PDF and the kernel. The primary way this is accomplished is through the use of neighborhood filters. Kernel smoothing density estimate for circular data (Scripts) 1.0, Downloads: 98, License: Shareware, By: Vlad Atanasiu, Size: 0.01.