Howtosignalprocessing
- Is it possible to approximate the LoG filter by a difference of Gaussian DoG explain?
- How does difference of Gaussians work?
- What is the difference between DoG and LoG?
- What is the difference between Gaussian filter and reference?
Is it possible to approximate the LoG filter by a difference of Gaussian DoG explain?
It is possible to approximate the LoG filter with a filter that is just the difference of two differently sized Gaussians. Such a filter is known as a DoG filter (short for `Difference of Gaussians').
How does difference of Gaussians work?
A well known method of edge detection is the Difference of Gaussians (DoG). The method consists of subtracting two Gaussians, where a kernel has a standard deviation smaller than the previous one. The convolution between the subtraction of kernels and the input image results in the edge detection of this image.
What is the difference between DoG and LoG?
As I understand it currently: DoG is an approximation of LoG. Both are used in blob detection, and both perform essentially as band-pass filters. Convolution with a Mexican Hat/Ricker wavelet seems to achieve very much the same effect.
What is the difference between Gaussian filter and reference?
Difference of Gaussians (DoG) is calculated as the difference between two smoothed versions of an image obtained by applying two Gaussian kernels of different standard deviations (sigma) on that image.
