1. Scale Invariant Feature Transform (SIFT)
JOJO

2. Outline Background Universal SIFT (2D) N-SIFT (3D) Conclusion
Keypoints Detection
Descriptor Construction
Match Method
N-SIFT (3D)
Conclusion

3. Outline Background Universal SIFT (2D) N-SIFT (3D) Conclusion
Keypoints Detection
Descriptor Construction
Match Method
N-SIFT (3D)
Conclusion

4. Background SIFT was proposed by David G. Lowe in 2004
Widely application in 2D image matching and recognition
Extended to N-dimensional SIFT by Warren Cheung in 2007 and applied in Medical image processing

5. Outline Background Universal SIFT (2D) N-SIFT (3D or more) Conclusion
Keypoints Detection
Descriptor Construction
Match Method
N-SIFT (3D or more)
Conclusion

6. Universal SIFT (2D)
Invariant to scale, rotation, illumination and adapt to complex geometry transform
Add a intermediate variable

7. Outline Background Universal SIFT (2D) N-SIFT (3D or more) Conclusion
Keypoints Detection
Descriptor Construction
Match Method
N-SIFT (3D or more)
Conclusion

8. 2D-SIFT (Keypoints Detection)
Scale space of image :
varies to get scale space
Keypoints are detected in multi-scale space

9. 2D-SIFT (Keypoints Detection)
Scale Space of An Image

10. 2D-SIFT (Keypoints Detection)
Linderberg found that normalized LoG scale space is invariant to scale.
DoG:
Detect keypoints in DoG space
Keypoints are detected in multi-scale space is relative to

11. 2D-SIFT (Keypoints Detection)
DoG Scale Space

12. 2D-SIFT (Keypoints Detection)
Build 3D scale space:
First octave
Down sample
Next octave
Keypoints are detected in multi-scale space

13. 2D-SIFT (Keypoints Detection)
Local extrema detection:
Search adjoining 26 points of one point in 3D DoG Space D(x,y,) . If the point is a local extrema, it’s a candidate keypoint.
Keypoints are detected in multi-scale space

14. 2D-SIFT (Keypoints Detection)
Accurate keypoint localization:
Change x
x is larger than 0.5
in any dimension Y
Keypoints are detected in multi-scale space N
Output x

15. 2D-SIFT (Keypoints Detection)
Eliminate unstationary keypoints:
Keypoints with low contrast
|D(x)|<0.03
Edge Keypoints
determinant

16. Extrema in DoG space: 832
Original image233*189
Extrema in DoG space:832
Discard low contrast:832->729
Discard edge points:729  536

17. Outline Background Universal SIFT (2D) N-SIFT (3D or more) Conclusion
Keypoints Detection
Descriptor Construction
Match Method
N-SIFT (3D or more)
Conclusion

18. 2D-SIFT (Descriptor Construction)
(1)Realize invariance to rotation:
Determine the main direction of a keypoint
Main direction correction
Keypoints are detected in multi-scale space

19. 2D-SIFT (Descriptor Construction)
Main direction of a keypoint
Keypoint
Divide the circle neighborhood
to several regions
Calculate the sum of gradient
magnitude of each region
Keypoints are detected in multi-scale space
parabola interpolation
Accurate localize
main direction

20. 2D-SIFT (Descriptor Construction)
Main direction correction
Main direction
Keypoints are detected in multi-scale space

21. 2D-SIFT (Descriptor Construction)
(2)Construct descriptor
Divide the 16*16 neighborhood to 4*4 blocks with 4*4 pixels
Main direction
Keypoints are detected in multi-scale space

22. 2D-SIFT (Descriptor Construction)
(2)Construct descriptor
Sum the gradient magnitude in 8 directions of each block
Keypoints are detected in multi-scale space

23. 2D-SIFT (Descriptor Construction)
(2)Construct descriptor
Sum the gradient magnitude in 8 directions of each block (8*4*4=128) and normalize the vector
主方向
Keypoints are detected in multi-scale space

24. 2D-SIFT Input image Detect extrema in DoG space
Accurate keypoints localization
Eliminate low contrast points
and edge points
Keypoints detection
Descriptor construction
Determine main direction
Main direction correction
Get descriptor
Keypoints are detected in multi-scale space
Feature vectors

25. Outline Background Universal SIFT (2D) N-SIFT (3D or more) Conclusion
Keypoints Detection
Descriptor Construction
Match Method
N-SIFT (3D or more)
Conclusion

26. 2D-SIFT (Match Method) SIFT extraction Feature set Image1 Image2
Corresponding
points set
NNDR: Nearest Neighbor
Distance Ratio
principle

27. Outline Background Universal SIFT (2D) N-SIFT (3D) Conclusion
Keypoints Detection
Descriptor Construction
Match Method
N-SIFT (3D)
Conclusion

28. N-SIFT (3D)

29. N-SIFT (3D)
Keypoints are detected in multi-scale space

30. N-SIFT (3D) 16*16*16  4*4*4 Histogram: 8*8 Vector: 8*8*4*4*4=16,384
Keypoints are detected in multi-scale space

31. N-SIFT (3D) Difference: No accurate keypoints localization
No main direction correction

32. Outline Background Universal SIFT (2D) N-SIFT (3D or more) Conclusion
Keypoints Detection
Descriptor Construction
Match Method
N-SIFT (3D or more)
Conclusion

33. Conclusion Invariant to scale
Keypoints was detected in DoG scale space
Invariant to rotation
Main direction correction
Invariant to illumination
Linear: normalize feature vector
Non-linear: elements above 0.2 in vector is cut to 0.2
Adaption to complex geometry transform
blocked histograms

34. Thank you!