# Difference of Gaussian Filtering

Hi

I have a query in DoG, is the sigma variance applies for both x,y? Then Gaussian blur will be passed same value for x and y ?

Thanks DS

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Difference of Gaussian Filtering

Hi

I have a query in DoG, is the sigma variance applies for both x,y? Then Gaussian blur will be passed same value for x and y ?

Thanks DS

add a comment

3

Daniil, DoG is one of the methods to detect edges in image without calculating gradients. Original image smoothed twice with Gaussian kernels with different sigmas. DoG is the difference of those two images. See wikipedia for details.

DS, Gaussian kernel is blurs the image equally in all directions (in theory at least). What do you mean by passing same values to x and y?

I think DoG is used to much more than detecting edges, it is actually an approximation of the Laplace of the image at a given scale... So, it would be more correct to say that is in fact a way of detecting corners or blobs. I don't know if you guys agree on this.

I used DoG mainly for edge detection, this is why I used it as example. And I didn't really meant to make full explanation. This is why I gave link to wiki.

3

Hi,

I don't know if you are talking about some specific function in OpenCV, can you give us more details about it? If you are referring to the buildDoGPyramid function (that actually computes a Difference of Gaussians), you don't need to pass any sigma to the function. Take a look to this source file: modules/nonfree/src/sift.cpp.

If I'm not mistaken, the whole theory of scale space is to filter with a Gaussian Filter that has the same standard deviation in the x and y coordinates. Otherwise the procedure will remove more details in one direction than another, which it does not make too much sense.

Asked: **
2012-07-26 17:33:47 -0600
**

Seen: **7,645 times**

Last updated: **Jul 27 '12**

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