I am research about correlation kernel and I have some questions that need your help. Let see the pp. 3302-3303 in the http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=6517250&tag=1 The special kernel is defined that
where $\gamma$ is a normalizing weight obtained by
and $\omega$ is Gaussian kernel
I implement it with support from Mr. Rayryeng. This is comparison of gaussian filter (right side)and correlation filter(left side). The gaussian looks like the low pass filter, whereas correlation kernel is as high pass filter. Could you help me explain the purpose of correlation kernel comparison with gaussian kernel? Which is beneifit? What kind of image to apply correlation kernel. Thank you
Consider image segmentation task, what do you think which is best for below image
If you observe that $\frac1{1+t}\approx1-t$ for $t$ not too large, you see that this filter subtracts a Gauss-low-passed image from the original, and is indeed a high-pass.
The purpose is probably to reduce the slope (slow illumination changes) in the uniform areas while preserving edge information, to facilitate segmentation.