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Lecture11

1. 1. Robert CollinsCSE486 Lecture 11: LoG and DoG Filters
2. 2. Robert CollinsCSE486 Today’s Topics Laplacian of Gaussian (LoG) Filter - useful for finding edges - also useful for finding blobs! approximation using Difference of Gaussian (DoG)
3. 3. Robert Collins Recall: First Derivative FiltersCSE486 • Sharp changes in gray level of the input image correspond to “peaks or valleys” of the first-derivative of the input signal. F(x) F ’(x) x (1D example)O.Camps, PSU
4. 4. Robert CollinsCSE486 Second-Derivative Filters • Peaks or valleys of the first-derivative of the input signal, correspond to “zero-crossings” of the second-derivative of the input signal. F(x) F ’(x) F’’(x) xO.Camps, PSU
5. 5. Robert CollinsCSE486 Numerical Derivatives See also T&V, Appendix A.2 Taylor Series expansion add Central difference approx 1 -2 1 to second derivative
6. 6. Robert CollinsCSE486 Example: Second Derivatives Ixx=d2I(x,y)/dx2 [ 1 -2 1 ] I(x,y) 2nd Partial deriv wrt x 1 -2 1 Iyy=d2I(x,y)/dy2 2nd Partial deriv wrt y
7. 7. Robert CollinsCSE486 Example: Second Derivatives Ixx Iyy benefit: you get clear localization of the edge, as opposed to the “smear” of high gradient magnitude values across an edge
8. 8. Robert CollinsCSE486 Compare: 1st vs 2nd DerivativesIxx IyyIx Iy
9. 9. Robert CollinsCSE486 Finding Zero-Crossings An alternative approx to finding edges as peaks in first deriv is to find zero-crossings in second deriv. In 1D, convolve with [1 -2 1] and look for pixels where response is (nearly) zero? Problem: when first deriv is zero, so is second. I.e. the filter [1 -2 1] also produces zero when convolved with regions of constant intensity. So, in 1D, convolve with [1 -2 1] and look for pixels where response is nearly zero AND magnitude of first derivative is “large enough”.
10. 10. Edge Detection SummaryRobert CollinsCSE486 1D 2D y step edge F(x) I(x) I(x,y) x x 2nd deriv 1st deriv dI(x) |∇I(x,y)| =(Ix 2(x,y) + Iy2(x,y))1/2 > Th > Th dx tan θ = Ix(x,y)/ Iy(x,y) d2I(x) =0 ∇2I(x,y) =Ix x (x,y) + Iyy (x,y)=0 dx2 Laplacian
11. 11. Robert CollinsCSE486 Finite Difference Laplacian Laplacian filter ∇2I(x,y)
12. 12. Robert CollinsCSE486 Example: Laplacian I(x,y) Ixx + Iyy
13. 13. Robert CollinsCSE486 Example: Laplacian Ixx Iyy Ixx+Iyy ∇2I(x,y)
14. 14. Robert CollinsCSE486 Notes about the Laplacian: • ∇2I(x,y) is a SCALAR – ↑ Can be found using a SINGLE mask – ↓ Orientation information is lost • ∇2I(x,y) is the sum of SECOND-order derivatives – But taking derivatives increases noise – Very noise sensitive! • It is always combined with a smoothing operation: I(x,y) O(x,y) Smooth LaplacianO.Camps, PSU
15. 15. Robert CollinsCSE486 LoG Filter • First smooth (Gaussian filter), • Then, find zero-crossings (Laplacian filter): – O(x,y) = ∇2(I(x,y) * G(x,y)) Laplacian of Laplacian of Gaussian (LoG) Gaussian-filtered image -filtered image Do you see the distinction?O.Camps, PSU
16. 16. Robert CollinsCSE486 1D Gaussian and Derivatives x2 − 2σ 2 g ( x) = e x2 x2 1 − 2 x − 2 g ( x) = − 2 2 xe 2σ =− 2 e 2σ 2σ σ 2 x2 x 1 − 2σ 2 g ( x ) = ( 4 − 2 )e σ3 σO.Camps, PSU
17. 17. Robert CollinsCSE486 Second Derivative of a Gaussian 2 x2 x 1 − 2 g ( x ) = ( 4 − 2 )e 3 2σ σ σ 2D analog LoG “Mexican Hat”O.Camps, PSU
18. 18. Robert CollinsCSE486 Effect of LoG Operator Original LoG-filtered Band-Pass Filter (suppresses both high and low frequencies) Why? Easier to explain in a moment.
19. 19. Robert CollinsCSE486 Zero-Crossings as an Edge Detector Raw zero-crossings (no contrast thresholding) LoG sigma = 2, zero-crossing
20. 20. Robert CollinsCSE486 Zero-Crossings as an Edge Detector Raw zero-crossings (no contrast thresholding) LoG sigma = 4, zero-crossing
21. 21. Robert CollinsCSE486 Zero-Crossings as an Edge Detector Raw zero-crossings (no contrast thresholding) LoG sigma = 8, zero-crossing
22. 22. Robert CollinsCSE486 Note: Closed Contours You may have noticed that zero-crossings form closed contours. It is easy to see why… Think of equal-elevation contours on a topo map. Each is a closed contour. Zero-crossings are contours at elevation = 0 . remember that in our case, the height map is of a LoG filtered image - a surface with both positive and negative “elevations”
23. 23. Robert CollinsCSE486 Other uses of LoG: Blob Detection Lindeberg: ``Feature detection with automatic scale selection. International Journal of Computer Vision, vol 30, number 2, pp. 77-- 116, 1998.
24. 24. Robert CollinsCSE486 Pause to Think for a Moment: How can an edge finder also be used to find blobs in an image?
25. 25. Robert CollinsCSE486 Example: LoG Extrema LoG maximasigma = 2 minima
26. 26. Robert CollinsCSE486 LoG Extrema, Detail maxima LoG sigma = 2
27. 27. Robert CollinsCSE486 LoG Blob Finding LoG filter extrema locates “blobs” maxima = dark blobs on light background minima = light blobs on dark background Scale of blob (size ; radius in pixels) is determined by the sigma parameter of the LoG filter. LoG sigma = 2 LoG sigma = 10
28. 28. Robert CollinsCSE486 Observe and Generalize convolve result with LoG maxima
29. 29. Robert CollinsCSE486 Observe and Generalize LoG looks a bit like an eye. and it responds maximally in the eye region!
30. 30. Robert CollinsCSE486 Observe and Generalize LoG Derivative of Gaussian Looks like dark blob Looks like vertical and on light background horizontal step edges Recall: Convolution (and cross correlation) with a filter can be viewed as comparing a little “picture” of what you want to find against all local regions in the mage.
31. 31. Robert CollinsCSE486 Observe and Generalize Key idea: Cross correlation with a filter can be viewed as comparing a little “picture” of what you want to find against all local regions in the image. Maximum response: Maximum response: dark blob on light background vertical edge; lighter on left Minimum response: Minimum response: light blob on dark background vertical edge; lighter on right
32. 32. Robert CollinsCSE486 Efficient Implementation Approximating LoG with DoG LoG can be approximate by a Difference of two Gaussians (DoG) at different scales 1D exampleM.Hebert, CMU
33. 33. Robert CollinsCSE486 Efficient Implementation LoG can be approximate by a Difference of two Gaussians (DoG) at different scales. Separability of and cascadability of Gaussians applies to the DoG, so we can achieve efficient implementation of the LoG operator. DoG approx also explains bandpass filtering of LoG (think about it. Hint: Gaussian is a low-pass filter)
34. 34. Robert CollinsCSE486 Back to Blob Detection Lindeberg: blobs are detected as local extrema in space and scale, within the LoG (or DoG) scale-space volume.
35. 35. Robert CollinsCSE486 Other uses of LoG: Blob Detection Gesture recognition for the ultimate couch potato
36. 36. Robert CollinsCSE486 Other uses for LOG: Image Coding • Coarse layer of the Gaussian pyramid predicts the appearance of the next finer layer. • The prediction is not exact, but means that it is not necessary to store all of the next fine scale layer. • Laplacian pyramid stores the difference.
37. 37. Robert CollinsCSE486 Other uses for LOG: Image Coding 256x256 128x128 64x64 32x32 takes less bits to store compressed versions of these than to compress the original full-res 256x256 128x128 64x64 greyscale image The Laplacian Pyramid as a Compact Image Code Burt, P., and Adelson, E. H., IEEE Transactions on Communication, COM-31:532-540 (1983).
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