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Convolution and Edge Detection

Convolution and Edge Detection. 15-463: Computational Photography Alexei Efros, CMU, Fall 2005. Some slides from Steve Seitz. Fourier spectrum . Fun and games with spectra. Gaussian filtering. A Gaussian kernel gives less weight to pixels further from the center of the window

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Convolution and Edge Detection

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  1. Convolution and Edge Detection 15-463: Computational Photography Alexei Efros, CMU, Fall 2005 Some slides from Steve Seitz

  2. Fourier spectrum

  3. Fun and games with spectra

  4. Gaussian filtering • A Gaussian kernel gives less weight to pixels further from the center of the window • This kernel is an approximation of a Gaussian function:

  5. Mean vs. Gaussian filtering

  6. Convolution • Remember cross-correlation: • A convolution operation is a cross-correlation where the filter is flipped both horizontally and vertically before being applied to the image: • It is written: • Suppose H is a Gaussian or mean kernel. How does convolution differ from cross-correlation?

  7. The greatest thing since sliced (banana) bread! The Fourier transform of the convolution of two functions is the product of their Fourier transforms The inverse Fourier transform of the product of two Fourier transforms is the convolution of the two inverse Fourier transforms Convolution in spatial domain is equivalent to multiplication in frequency domain! The Convolution Theorem

  8. Fourier Transform pairs

  9. 2D convolution theorem example |F(sx,sy)| f(x,y) * h(x,y) |H(sx,sy)| g(x,y) |G(sx,sy)|

  10. Edges in images

  11. The gradient direction is given by: • how does this relate to the direction of the edge? • The edge strength is given by the gradient magnitude Image gradient • The gradient of an image: • The gradient points in the direction of most rapid change in intensity

  12. Effects of noise • Consider a single row or column of the image • Plotting intensity as a function of position gives a signal How to compute a derivative? • Where is the edge?

  13. Look for peaks in Solution: smooth first • Where is the edge?

  14. Derivative theorem of convolution • This saves us one operation:

  15. Laplacian of Gaussian • Consider Laplacian of Gaussian operator • Where is the edge? • Zero-crossings of bottom graph

  16. Laplacian of Gaussian • is the Laplacian operator: 2D edge detection filters Gaussian derivative of Gaussian

  17. MATLAB demo g = fspecial('gaussian',15,2); imagesc(g) surfl(g) gclown = conv2(clown,g,'same'); imagesc(conv2(clown,[-1 1],'same')); imagesc(conv2(gclown,[-1 1],'same')); dx = conv2(g,[-1 1],'same'); imagesc(conv2(clown,dx,'same'); lg = fspecial('log',15,2); lclown = conv2(clown,lg,'same'); imagesc(lclown) imagesc(clown + .2*lclown)

  18. What does blurring take away? original

  19. What does blurring take away? smoothed (5x5 Gaussian)

  20. Edge detection by subtraction Why does this work? smoothed – original

  21. Gaussian - image filter FFT Gaussian delta function Laplacian of Gaussian

  22. What is happening?

  23. - = + a = Unsharp Masking

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