1. CSCE 643 Computer Vision: Extractions of Image Features Jinxiang Chai

2. Good Image Features What are we looking for? –Strong features –Invariant to changes (affine and perspective, occlusion, illumination, etc.)

3. Feature Extraction Why do we need to detect features? - Features correspond to important points in both the world and image spaces - Object detection/recognition - Solve the problem of correspondence Locate an object in multiple images (i.e. in video) Track the path of the object, infer 3D structures, object and camera movement

4. Outline Image Features - Corner detection - SIFT extraction

5. What are Corners? Point features

6. What are Corners? Point features Where two edges come together Where the image gradient has significant components in the x and y direction We will establish corners from the gradient rather than the edge images

7. Basic Ideas What are gradients along x and y directions?

8. Basic Ideas What are gradients along x and y directions?

9. Basic Ideas What are gradients along x and y directions? How to measure corners based on the gradient images?

10. Basic Ideas What are gradients along x and y directions? How to measure corners based on the gradient images? - two major axes in the local window!

11. How to Find Two Major Axes? Principal component analysis (PCA)Principal component analysis

12. How to Find Two Major Axes? Principal component analysis (PCA)Principal component analysis The length of two major axes is dependent on the ration of eigen values (λ1/λ2 ).

13. Corner Detection Algorithm 1. Compute the image gradients 2. Define a neighborhood size as an area of interest around each pixel 3x3 neighborhood

14. 3.For each image pixel (i,j), construct the following matrix from it and its neighborhood values e.g. Corner Detection Algorithm (cont’d) Similar to covariance matrix (I x,I y ) T !

15. Corner Detection Algorithm (cont’d) 4.For each matrix C (i,j), determine the 2 eigenvalues λ (i.j) = [λ 1, λ 2 ]. - This means dominant gradient direction aligns with x or y axis. - If either λ1 or λ2 is close to zero, then this is not a corner. Simple case:

16. Corner Detection Algorithm (cont’d) 4.For each matrix C (i,j), determine the 2 eigenvalues λ (i.j) = [λ 1, λ 2 ]. Simple case: Isolated pixelsInterior Region Edge Corner λ 1, λ 2 =0 Large λ 1 and small λ 2 Large λ 1 and large λ 2 small λ 1 and small λ 2

17. Corner Detection Algorithm (cont’d) 4.For each matrix C (i,j), determine the 2 eigenvalues λ (i.j) = [λ 1, λ 2 ]. - This is just a rotated version of the one on last slide - If either λ1 or λ2 is close to zero, then this is not a corner. - invariant to 2D rotation General case:

18. Eigen-values and Corner - λ1 is large - λ2 is large

19. Eigen-values and Corner - λ1 is large - λ2 is small

20. Eigen-values and Corner - λ1 is small - λ2 is small

21. Corner Detection Algorithm (cont’d) 4.For each matrix C (i,j), determine the 2 eigenvalues λ (i.j) = [λ 1, λ 2 ]. 5. If both λ 1 and λ 2 are big, we have a corner (Harris also checks the ratio of λs is not too high) ISSUE: The corners obtained will be a function of the threshold !

22. Image Gradients

23. Closeup of image orientation at each pixel

24. The Orientation Field Corners are detected where both λ1 and λ2 are big

25. The Orientation Field Corners are detected where both λ1 and λ2 are big

26. Corner Detection Sample Results Threshold=25,000Threshold=10,000 Threshold=5,000

27. Outline Image Features - Corner detection - SIFT extraction

28. Scale Invariant Feature Transform (SIFT) Choosing features that are invariant to image scaling and rotation Also, partially invariant to changes in illumination and 3D camera viewpoint

29. Motivation for SIFT Earlier Methods –Harris corner detector Sensitive to changes in image scale Finds locations in image with large gradients in two directions –No method was fully affine invariant Although the SIFT approach is not fully invariant it allows for considerable affine change SIFT also allows for changes in 3D viewpoint

30. Invariance Illumination Scale Rotation Affine

31. Readings Object recognition from local scale- invariant features [pdf link], ICCV 09pdf link David G. Lowe, "Distinctive image features from scale-invariant keypoints," International Journal of Computer Vision, 60, 2 (2004), pp. 91-110

32. SIFT Algorithm Overview 1.Scale-space extrema detection 2.Keypoint localization 3.Orientation Assignment 4.Generation of keypoint descriptors.

33. Scale Space Different scales are appropriate for describing different objects in the image, and we may not know the correct scale/size ahead of time.

34. Scale space (Cont.) Looking for features (locations) that are stable (invariant) across all possible scale changes –use a continuous function of scale (scale space) Which scale-space kernel will we use? –The Gaussian Function

35. -variable-scale Gaussian -input image Scale-Space of Image

36. -variable-scale Gaussian -input image To detect stable keypoint locations, find the scale-space extrema in difference-of- Gaussian function Scale-Space of Image

37. -variable-scale Gaussian -input image To detect stable keypoint locations, find the scale-space extrema in difference-of- Gaussian function Scale-Space of Image

38. -variable-scale Gaussian -input image To detect stable keypoint locations, find the scale-space extrema in difference-of- Gaussian function Scale-Space of Image Look familiar?

39. -variable-scale Gaussian -input image To detect stable keypoint locations, find the scale-space extrema in difference-of- Gaussian function Scale-Space of Image Look familiar? -bandpass filter!

40. Difference of Gaussian 1.A = Convolve image with vertical and horizontal 1D Gaussians, σ=sqrt(2) 2.B = Convolve A with vertical and horizontal 1D Gaussians, σ=sqrt(2) 3.DOG (Difference of Gaussian) = A – B 4.So how to deal with different scales?

41. Difference of Gaussian 1.A = Convolve image with vertical and horizontal 1D Gaussians, σ=sqrt(2) 2.B = Convolve A with vertical and horizontal 1D Gaussians, σ=sqrt(2) 3.DOG (Difference of Gaussian) = A – B 4.Downsample B with bilinear interpolation with pixel spacing of 1.5 (linear combination of 4 adjacent pixels)

42. A1 B1 Difference of Gaussian Pyramid Input Image Blur Downsample B2 B3 A2 A3 A3-B3 A2-B2 A1-B1 DOG2 DOG1 DOG3 Blur

43. Other issues Initial smoothing ignores highest spatial frequencies of images

44. Other issues Initial smoothing ignores highest spatial frequencies of images - expand the input image by a factor of 2, using bilinear interpolation, prior to building the pyramid

45. Other issues Initial smoothing ignores highest spatial frequencies of images - expand the input image by a factor of 2, using bilinear interpolation, prior to building the pyramid How to do downsampling with bilinear interpolations?

46. Bilinear Filter Weighted sum of four neighboring pixels x y u v

47. Bilinear Filter Sampling at S(x,y): (i+1,j) (i,j) (i,j+1) (i+1,j+1) S(x,y) = a*b*S(i,j) + a*(1-b)*S(i+1,j) + (1-a)*b*S(i,j+1) + (1-a)*(1-b)*S(i+1,j+1) u v y x

48. Bilinear Filter Sampling at S(x,y): (i+1,j) (i,j) (i,j+1) (i+1,j+1) S(x,y) = a*b*S(i,j) + a*(1-b)*S(i+1,j) + (1-a)*b*S(i,j+1) + (1-a)*(1-b)*S(i+1,j+1) S i = S(i,j) + a*(S(i,j+1)-S(i)) S j = S(i+1,j) + a*(S(i+1,j+1)-S(i+1,j)) S(x,y) = S i +b*(S j -S i) To optimize the above, do the following u v y x

49. Bilinear Filter (i+1,j) (i,j) (i,j+1) (i+1,j+1) y x

50. Pyramid Example A1 B1DOG1 DOG3 A2 A3 B3 B2

51. Feature Detection Find maxima and minima of scale space For each point on a DOG level: –Compare to 8 neighbors at same level –If max/min, identify corresponding point at pyramid level below –Determine if the corresponding point is max/min of its 8 neighbors –If so, repeat at pyramid level above Repeat for each DOG level Those that remain are key points

52. Identifying Max/Min DOG L-1 DOG L DOG L+1

53. Refining Key List: Illumination For all levels, use the “A” smoothed image to compute –Gradient Magnitude Threshold gradient magnitudes: –Remove all key points with M ij less than 0.1 times the max gradient value Motivation: Low contrast is generally less reliable than high for feature points

54. SIFT Feature Orientation? We now obtain the location and scale of SIFT features How can we obtain the orientation of features?

55. Assigning Canonical Orientation For each remaining key point: –Choose surrounding N x N window at DOG level it was detected DOG image

56. Assigning Canonical Orientation For all levels, use the “A” smoothed image to compute –Gradient Orientation + Gaussian Smoothed Image Gradient OrientationGradient Magnitude

57. Assigning Canonical Orientation Gradient magnitude weighted by 2D gaussian Gradient Magnitude2D GaussianWeighted Magnitude * =

58. Assigning Canonical Orientation Accumulate in histogram based on orientation Histogram has 36 bins with 10° increments Weighted Magnitude Gradient Orientation Sum of Weighted Magnitudes

59. Assigning Canonical Orientation Identify peak and assign orientation and sum of magnitude to key point Weighted Magnitude Gradient Orientation Sum of Weighted Magnitudes Peak *

60. Local Image Description SIFT keys each assigned: –Location –Scale (analogous to level it was detected) –Orientation (assigned in previous canonical orientation steps) Now: Describe local image region invariant to the above transformations

61. SIFT key example

62. Local Image Description For each key point: Identify 8x8 neighborhood (from DOG level it was detected) Align orientation to x- axis

63. Local Image Description 3.Calculate gradient magnitude and orientation map 4.Weight by Gaussian

64. Local Image Description 5.Calculate histogram of each 4x4 region. 8 bins for gradient orientation. Tally weighted gradient magnitude.

65. Local Image Description 6.This histogram array is the image descriptor. (Example here is vector, length 8*4=32. Best suggestion: 128 vector for 16x16 neighborhood)

66. Applications: Image Matching Find all key points identified in source and target image –Each key point will have 2d location, scale and orientation, as well as invariant descriptor vector For each key point in source image, search corresponding SIFT features in target image. Find the transformation between two images using epipolar geometry constraints or affine transformation.

67. Image matching via SIFT featrues Feature detection

68. Image matching via SIFT featrues Image matching via nearest neighbor search - if the ratio of closest distance to 2nd closest distance greater than 0.8 then reject as a false match. Remove outliers using epipolar line constraints.

69. Image matching via SIFT featrues

70. Summary SIFT features are reasonably invariant to rotation, scaling, and illumination changes. We can use them for image matching and object recognition among other things. Efficient on-line matching and recognition can be performed in real time