Convolution.

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Presentation transcript:

Convolution

Convolution Properties Commutative: f*g = g*f Associative: (f*g)*h = f*(g*h) Homogeneous: f*(g)=  f*g Additive (Distributive): f*(g+h)= f*g+f*h Shift-Invariant f*g(x-x0,y-yo)= (f*g) (x-x0,y-yo)

The Convolution Theorem and similarly:

Examples What is the Fourier Transform of ? *

Image Domain Frequency Domain

The Sampling Theorem Nyquist frequency, Aliasing, etc… (on the board)

Multi-Resolution Image Representation Gaussian pyramids Laplacian Pyramids Wavelet Pyramids

Image Pyramid Low resolution High resolution

Fast Pattern Matching Also good for: - motion analysis search search search search Also good for: - motion analysis - image compression - other applications

The Gaussian Pyramid Low resolution down-sample blur down-sample blur High resolution

- = - = - = The Laplacian Pyramid Gaussian Pyramid Laplacian Pyramid expand - = expand - = expand - =

Laplacian ~ Difference of Gaussians - = DOG = Difference of Gaussians More details on Gaussian and Laplacian pyramids can be found in the paper by Burt and Adelson (link will appear on the website).

Computerized Tomography f(x,y) u v F(u,v)

Computerized Tomography Original (simulated) 2D image 8 projections- Frequency Domain 120 projections- Frequency Domain Reconstruction from 8 projections Reconstruction from 120 projections