gaussian

I find on the OpenCV documentation for cvSmooth that sigma can be calculated from the kernel size as follows: sigma = 0.3(n/2 - 1) + 0.8 I would like to know the theoretical background of this equation. Thank you. Using such a value for sigma, the ratio between the value at the centre of the kernel and on the edge of the kernel, found for y=0 and x=n/2-1 , is: g_edge / g_center = exp(-(x²+y²)/(2σ²)) = exp(-(n/2-1)²/(2*(0.3(n/2-1)+0.8)²)) The limit of this value as n increases is: exp(-1/(2*0.3²)) = 0.00386592 Note that 1/256 is 0.00390625 . Images are often encoded in 256-value ranges. The

I am considering using this method to interpolate some 3D points I have. As an input I have atmospheric concentrations of a gas at various elevations over an area. The data I have appears as values every few feet of vertical elevation for several tens of feet, but horizontally separated by many hundreds of feet (so 'columns' of tightly packed values). The assumption is that values vary in the vertical direction significantly more than in the horizontal direction at any given point in time. I want to perform 3D kriging with that assumption accounted for (as a parameter I can adjust or that is

I am creating gaussian pyramid in MATLAB 2010b. I want to show images like same patterned mentioned here . I tried to use imresize , truesize but getting all images in same size. Could anybody please help me on this issue? You can use "imshow with True Size for multiple images" FEX file to answer your question... EDIT : The code below will produce the subplot at the bottom right part of the figure: clear imagesCellArray mand = imread('mandelbrot_set.jpg'); % read image dim = 3; [imagesCellArray{1:dim,1:dim}] = deal(mand); % create smaller images by imresize for iRow = 1:dim for iCol = 1:dim

I need to generate a Gaussian random sample of n numbers, with mean 0 and variance 1, using the randn function. In general, how would I generate a Gaussian random sample X of n numbers, with mean mu and variance v , using the randn function? nbonneel A standard normal distribution already has mean 0 and variance 1. If you want to change the mean, just "translate" the distribution, i.e., add your mean value to each generated number. Similarly, if you want to change the variance, just "scale" the distribution, i.e., multiply all your numbers by sqrt(v) . For example, v = 1.5; % variance sigma =