I\'m writing a gaussian filter, and my goal is to match the gaussian blur filter in photoshop as closely as possible. This is my first image processing endeavor. Some problems/questions I have are...
So far I\'ve implemented a gaussian blur filter entirely in the space domain, making use of the separability of the gaussian, that is, applying a 1D gaussian kernel along the rows and then along the c
I\'ve searched a lot over the internet to find a way to generate random numbers on my CUDA device, within a kernel. The numbers must come from a gaussian distribution.
I am trying to do some image processing and I would like to apply the LoG kernel. I know the formula, which is :
I want to generate开发者_如何学JAVA some random integers in Java, but this according to some distribution laws.
im trying to implement a gaussian blur with the use of FFT and could find her开发者_开发技巧e the following recipe.
I do not und开发者_运维问答erstand what a convolution kernel is and how I would apply a convolution matrix topixels in an image (I am talking about doing a Gaussian Blur operation on an image).
When I add Gaussian noise to an array sho开发者_高级运维uldnt the histogram be Gaussian? Although the noise is random, the distribution should be gaussian right? That is not what I get.
I\'m using R. I have 25 variables over 15 time points, with 3 or more replicates per variable per time point. I\'ve melted this into a data.frame, which I can plot happily using (amongst other things)
This is a formula for LoG filtering: (source: ed.ac.uk) Also in applications with LoG filtering I see that function is called with only one parameter:
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