1. Templates, Image Pyramids, and Filter Banks
09/19/11
Computer Vision
James Hays, Brown
Slides: Hoiem and others

2. Review Match the spatial domain image to the Fourier magnitude image 21 3 4 5 B
1 - D
2 – B
3 – A
4 - E
5 - C C E A D
Slide: Hoiem

3. Reminder
Project 1 due in one week

4. Today’s class Template matching Image Pyramids
Filter banks and texture
Denoising, Compression

5. Template matching Goal: find in image
Main challenge: What is a good similarity or distance measure between two patches?
Correlation
Zero-mean correlation
Sum Square Difference
Normalized Cross Correlation
Slide: Hoiem

6. Matching with filters Goal: find in image
Method 0: filter the image with eye patch
f = image
g = filter
Problem: response is stronger for higher intensity
What went wrong?
Input
Filtered Image
Slide: Hoiem

7. Matching with filters Goal: find in image
Method 1: filter the image with zero-mean eye
mean of f
True detections
Likes bright pixels where filters are above average, dark pixels where filters are below average. Problems: response is sensitive to gain/contrast, pixels in filter that are near the mean have little effect, does not require pixel values in image to be near or proportional to values in filter.
False detections
Input
Filtered Image (scaled)
Thresholded Image
Slide: Hoiem

8. Matching with filters Goal: find in image Method 2: SSD
True detections
Problem: SSD sensitive to average intensity
Input
1- sqrt(SSD)
Thresholded Image
Slide: Hoiem

9. Matching with filters
Can SSD be implemented with linear filters?

10. What’s the potential downside of SSD?
Matching with filters
What’s the potential downside of SSD?
Goal: find in image
Method 2: SSD
Problem: SSD sensitive to average intensity
Input
1- sqrt(SSD)
Slide: Hoiem

11. Matching with filters Goal: find in image
Method 3: Normalized cross-correlation
mean template
mean image patch
Invariant to mean and scale of intensity
Matlab: normxcorr2(template, im)
Slide: Hoiem

12. Matching with filters Goal: find in image
Method 3: Normalized cross-correlation
True detections
Input
Thresholded Image
Normalized X-Correlation
Slide: Hoiem

13. Matching with filters Goal: find in image
Method 3: Normalized cross-correlation
True detections
Input
Thresholded Image
Normalized X-Correlation
Slide: Hoiem

14. Q: What is the best method to use?
A: Depends
SSD: faster, sensitive to overall intensity
Normalized cross-correlation: slower, invariant to local average intensity and contrast

15. Q: What if we want to find larger or smaller eyes?
A: Image Pyramid

16. Low-Pass Filtered Image
Review of Sampling
Gaussian
Filter
Sample
Image
Low-Pass Filtered Image
Low-Res Image
Slide: Hoiem

17. Gaussian pyramid
Source: Forsyth

18. Template Matching with Image Pyramids
Input: Image, Template
Match template at current scale
Downsample image
Repeat 1-2 until image is very small
Take responses above some threshold, perhaps with non-maxima suppression
Slide: Hoiem

19. Coarse-to-fine Image Registration
Compute Gaussian pyramid
Align with coarse pyramid
Successively align with finer pyramids
Search smaller range
Why is this faster?
Are we guaranteed to get the same result?
Slide: Hoiem

20. Laplacian filter unit impulse Gaussian Laplacian of Gaussian
Source: Lazebnik

21. 2D edge detection filters
Laplacian of Gaussian
Gaussian
derivative of Gaussian
How many 2nd derivative filters are there? There are four 2nd partial derivative filters.
In practice, it’s handy to define a single 2nd derivative filter—the Laplacian
is the Laplacian operator:

22. Laplacian pyramid
Source: Forsyth

23. Computing Gaussian/Laplacian Pyramid
Can we reconstruct the original from the laplacian pyramid?

24. Perception
Why do we get different, distance-dependent interpretations of hybrid images? ?
Slide: Hoiem

25. Hybrid Image
Slide: Hoiem

26. Hybrid Image in Laplacian Pyramid
High frequency  Low frequency
Slide: Hoiem

27. Question
Can you align the images using the FFT?

28. Image representation
Pixels: great for spatial resolution, poor access to frequency
Fourier transform: great for frequency, not for spatial info
Pyramids/filter banks: balance between spatial and frequency information
Slide: Hoiem

29. Major uses of image pyramids
Compression
Object detection
Scale search
Features
Detecting stable interest points
Registration
Course-to-fine
Slide: Hoiem

30. Denoising
Gaussian Filter
Additive Gaussian Noise
Slide: Hoiem

31. Reducing Gaussian noise
Smoothing with larger standard deviations suppresses noise, but also blurs the image
Source: S. Lazebnik

32. Reducing salt-and-pepper noise by Gaussian smoothing
3x3
5x5
7x7
What’s wrong with these results?

33. Alternative idea: Median filtering
A median filter operates over a window by selecting the median intensity in the window
Better at salt’n’pepper noise
Not convolution: try a region with 1’s and a 2, and then 1’s and a 3
Is median filtering linear?
Source: K. Grauman

34. Median filter
What advantage does median filtering have over Gaussian filtering?
Robustness to outliers
Source: K. Grauman

35. Salt-and-pepper noise
Median filter
Salt-and-pepper noise
Median filtered
MATLAB: medfilt2(image, [h w])
Source: M. Hebert

36. Median vs. Gaussian filtering
3x3
5x5
7x7
Gaussian
Median

37. Other non-linear filters
Weighted median (pixels further from center count less)
Clipped mean (average, ignoring few brightest and darkest pixels)
Bilateral filtering (weight by spatial distance and intensity difference)
Bilateral filtering
Image:

38. Review of last three days

39. Review: Image filtering1 90 90
ones, divide by 9
Credit: S. Seitz

40. Image filtering 1 90 90 10
ones, divide by 9
Credit: S. Seitz

41. Image filtering 1 90 90 10 20
ones, divide by 9
Credit: S. Seitz

42. Filtering in spatial domain-1 1 -2 2 * =

43. Filtering in frequency domain
FFT
FFT =
Inverse FFT
Slide: Hoiem

44. Review of Last 3 Days Filtering in spatial domain
Slide filter over image and take dot product at each position
Remember linearity (for linear filters)
Examples
1D: [-1 0 1], [ ]
1D: [ ], [ ]
2D: [1 0 0 ; ; 0 0 1]/4

45. Review of Last 3 Days Filtering in frequency domain
Can be faster than filtering in spatial domain (for large filters)
Can help understand effect of filter
Algorithm:
Convert image and filter to fft (fft2 in matlab)
Pointwise-multiply ffts
Convert result to spatial domain with ifft2

46. Review of Last 3 Days Linear filters for basic processing
Edge filter (high-pass)
Gaussian filter (low-pass)
[-1 1]
Gaussian
FFT of Gradient Filter
FFT of Gaussian
Slide: Hoiem

47. Review of Last 3 Days
Derivative of Gaussian
Slide: Hoiem

48. Review of Last 3 Days Applications of filters
Template matching (SSD or Normxcorr2)
SSD can be done with linear filters, is sensitive to overall intensity
Gaussian pyramid
Coarse-to-fine search, multi-scale detection
Laplacian pyramid
More compact image representation
Can be used for compositing in graphics
Downsampling
Need to sufficiently low-pass before downsampling

49. Next Lectures
Machine Learning Crash Course