# CV_Filtering and Sampling

1.Correlation Filtering

Convolution

If H[-u,-v]=H[u,v], then correlation = convolution

2.

1)Gaussian kernel

Gaussian smoothing:

Variance determines extent of smoothing

set filter half-width to about 3*variance

In Matlab:

hsize=10;

sigma=5;

h=fspecial(‘gaussian’ hsize,sigma);

mesh(h);imagesc(h);

outim=imfilter(im,h);

imshow(outim);

2)Oriented Gaussian Filters

3）Difference of Gaussian

DOG

Laplacian of Gaussian can be approximated by the difference between two different Gaussians

3)Derivative of Gaussian

LOG Laplacian of Gaussian

3.Sampling

wagen whell effect

Nyquist theorem: In order to recover a certain frequency f, we need to sample with at least 2f.

Representation of scale

The Gaussian pyramid

blur+subsample

The Laplacian Pyramid

a band pass representation vice a low pass representation of Gaussian

4.Detectors:Harris

Bolbs and conners

auto-correlation matrix

f(x+triangelex)=f(x)+trianglex*f'(x)+ trianglex^2*f”(x2)

5.Gaboe Wavelet