1. Distinctive Image Features from Scale-Invariant Keypoints

2. Note
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이 점을 감안해서 혹시 그림이나 사진 등을 참고할 생각이시라면 인터넷을 통해 해당하는 paper, ppt, web-page 등을 찾으셔서 레퍼런스로 활용해주시기 바랍니다.
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3. Contents Introduction SIFT Overview Test and Result Conclusion
Scale-space extrema detection
Keypoint localization
Orientation assignment
Keypoint descriptor
Test and Result
Conclusion

4. Introduction
Ideal Interest Points / Regions
<Image Features>

5. Introduction Harris Corner Detector Rotation invariance
Partial invariance to affine change
<Feature Detection>

6. Introduction Harris Corner Detector Non-invariance to scale change
edge
corner

7. Introduction SIFT Application The Scale Invariant Feature Transform
Choosing features that are invariant to image scaling and rotation
Partially invariant to changes in illumination and 3D camera viewpoint
Well localized in both spatial and frequency domain
- Resistant to noise, clutter, and occlusion
Features are highly distinctive, matched with high probability against large number of features
See……
Application
Object recognition
Automatic mosaic
Tracking
Robot localization
3D scene modeling
Panoramas

8. Introduction
<Object Recognition>

9. Introduction
<Automatic Mosaic>

10. SIFT overview Detector Scale-space extrema detection
Keypoint localization and filtering
Orientation assignment
Keypoint descriptor
Descriptor

11. SIFT overview Detector Scale-space extrema detection
Keypoint localization and filtering
Orientation assignment
Keypoint descriptor
Descriptor

12. 1. Scale-space extrema detection
A “good” function for scale detection has one stable sharp peak
Good ! f
region size
bad
bad
L or DOG(Difference of Gaussians) kernel is a matching filter

13. 1. Scale-space extrema detection
DOG(Difference of Gaussians)
Construct scale-space
Take differences
Downsample
Convolve with
Gaussian

14. 1. Scale-space extrema detection
For example
Construct scale-space

15. 1. Scale-space extrema detection
For example
Take differences

16. 1. Scale-space extrema detection
Compare a pixel with its 26 neighbors in 3*3 regions at the current and adjacent scales

17. 1. Scale-space extrema detection
For example
Scale-space extrema

18. SIFT overview Detector Scale-space extrema detection
Keypoint localization and filtering
Orientation assignment
Keypoint descriptor
Descriptor

19. 2. Keypoint localization and filtering
Reject points with bad contrast
DOG smaller than 0.03 (image values in [0, 1])

20. 2. Keypoint localization and filtering
Reject points with strong edge response in one direction only
To check if ratio of principal curvature is below some threshold, r

21. SIFT overview Detector Scale-space extrema detection
Keypoint localization and filtering
Orientation assignment
Keypoint descriptor
Descriptor

22. 3. Orientation assignment
Descriptor computed relative to keypoint’s orientation achieves rotation invariance
Let, for a keypointm L is the image with the closest scale
Compute the orientation histogram
- within a region around the keypoint (16 Ⅹ 16)
Compute gradient magnitude and orientation using finite differences

23. 3. Orientation assignment

24. 3. Orientation assignment

25. 3. Orientation assignment

26. 3. Orientation assignment

27. 3. Orientation assignment

28. SIFT overview Detector Scale-space extrema detection
Keypoint localization and filtering
Orientation assignment
Keypoint descriptor
Descriptor

29. Keypoint descriptor The computation of the keypoint descriptor
A set of keypoints are obtained from each reference image
Each such keypoint has a graphical descriptor – which is a 128 component vector (4Ⅹ4Ⅹ8) ← keypoint descriptor’s complexity

30. Keypoint descriptor
Storing

32. Keypoint descriptor Matching Acceptance of a match
Test image gives a new set of (keypoint, vector) pair
Find the nearest (top 2) descriptors in database
Acceptance of a match
Storage using k-d trees - Use the Best-Bin-First(BBF) algorithm
Ratio of distance to first nearest descriptor to that of second < threshold (0.8)

33. Test and Result <Recognition under occlusion>
<3D object recognition>

34. Test and Result
<Recognition under differing illumination>

35. Test and Result
<Location recognition>

36. Test and Result
SIFT didn’t work
Large illumination change

37. Test and Result
SIFT didn’t work
Non-rigid deformation

38. Conclusion SIFT SIFT extensions
A novel method for detecting interest points
Invariant to
- image scaling
- translation
- rotation
Robust matching across substantial range of
- distortion
- change in 3D view point
- addition of noise
- change in illumination
SIFT extensions
PCA-SIFT
SURF
Approx SIFT
GPU implementation