0
DAVIDG. LOWE
2004
Presentation by Hadi Sinaee
Sharif University ofTechnology
MachineVision Course, Spring 2014
Instructor:...
• Background
• SIFT(Scale Invariant FeatureTransform) Steps
• Recognition Example
• Conclusion
Page 2
Page 3
 Object Detection 3D reconstruction MotionTracking
Page 4
 Scale Invariant
 Rotation Invariant
 illumination Invariant
 Robust to occlusion
 Robust to clutter
 Robust ...
Page 5
• Background
• SIFT(Scale Invariant FeatureTransform) Steps
• Recognition Example
• Conclusion
Page 6
Page 7
Steps:
1. Scale-Space Extrema Detection
2. Keypoint Localization
3. Orientation Assignment
4. Keypoint Descriptor
Page 8
 Searching over all scales in order to identify the Location and Scales that can
be assigned under differing views...
Page 9
Page 10
Sampling last image in the octave for the next octave
Page 11
Finding the minimum or maximum sample point
among its 26 neighbors
The extrema may be close to each other and it c...
Page 12
Page 13
Steps:
1. Scale-Space Extrema Detection
2. Keypoint Localization
3. Orientation Assignment
4. Keypoint Descriptor
Page 14
 Once keypoint candidates has been found, we want to reduce the response to the low
contrast points, or poorly lo...
Page 15
 The value of the extremum is useful to reject the unstable extrema with low contrast.
Original Image Keypoints f...
Page 16
Page 17
729 keypoint
from thresholding on the contrast
536 keypoint
from thresholding on the ratio
Page 18
Steps:
1. Scale-Space Extrema Detection
2. Keypoint Localization
3. Orientation Assignment
4. Keypoint Descriptor
Page 19
 Peaks in histogram shows dominant directions in the spatial domain.
 Highest peak and any one in the 80% of it ...
Page 20
As it can be seen that SIFT is robust to
image noises
78% repeatability
10% of image pixel noise
Page 21
Steps:
1. Scale-Space Extrema Detection
2. Keypoint Localization
3. Orientation Assignment
4. Keypoint Descriptor
Page 22
Page 23
Page 24
50% >
• Background
• SIFT(Scale Invariant FeatureTransform) Steps
• Recognition Example
• Conclusion
Page 25
Page 26
 SIFT keypoints are useful due to their
distinctiveness for object detection.
 They are invariants to scale, orientation...
Questions are welcomed!?
Page 28
Page 29
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Distinctive image features from scale invariant keypoint

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This my presentation about SIFT features at Sharif University of technology, Tehran, Iran. This presented in Machine Vision Course offered by Dr. M.Jamzad.

The presentation contains animations and it can not play properly! Please send e-mail to get the original one: sinaee@ce.sharif.ir

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Transcript of "Distinctive image features from scale invariant keypoint"

  1. 1. DAVIDG. LOWE 2004 Presentation by Hadi Sinaee Sharif University ofTechnology MachineVision Course, Spring 2014 Instructor: Dr. M.Jamzad
  2. 2. • Background • SIFT(Scale Invariant FeatureTransform) Steps • Recognition Example • Conclusion Page 2
  3. 3. Page 3  Object Detection 3D reconstruction MotionTracking
  4. 4. Page 4  Scale Invariant  Rotation Invariant  illumination Invariant  Robust to occlusion  Robust to clutter  Robust Noise  Cost of extraction
  5. 5. Page 5
  6. 6. • Background • SIFT(Scale Invariant FeatureTransform) Steps • Recognition Example • Conclusion Page 6
  7. 7. Page 7 Steps: 1. Scale-Space Extrema Detection 2. Keypoint Localization 3. Orientation Assignment 4. Keypoint Descriptor
  8. 8. Page 8  Searching over all scales in order to identify the Location and Scales that can be assigned under differing views of a same object.  To efficiently detect stable keypoint locations in scale space, Lowe(1999) use DoG of two nearby scales,
  9. 9. Page 9
  10. 10. Page 10 Sampling last image in the octave for the next octave
  11. 11. Page 11 Finding the minimum or maximum sample point among its 26 neighbors The extrema may be close to each other and it cause to be quite unstable to small perturbations of image This problem arises from the frequency of samples being used for detection of extrema. Unfortunately, there is no minimum spacing of samples to detect all extrema
  12. 12. Page 12
  13. 13. Page 13 Steps: 1. Scale-Space Extrema Detection 2. Keypoint Localization 3. Orientation Assignment 4. Keypoint Descriptor
  14. 14. Page 14  Once keypoint candidates has been found, we want to reduce the response to the low contrast points, or poorly localized along an edge  If the extremum is greater than 0.5 it means the extremum is closer to another sample point.
  15. 15. Page 15  The value of the extremum is useful to reject the unstable extrema with low contrast. Original Image Keypoints from extremas of DoG, 832Keypoints 729, after threshold on the minimum contrast
  16. 16. Page 16
  17. 17. Page 17 729 keypoint from thresholding on the contrast 536 keypoint from thresholding on the ratio
  18. 18. Page 18 Steps: 1. Scale-Space Extrema Detection 2. Keypoint Localization 3. Orientation Assignment 4. Keypoint Descriptor
  19. 19. Page 19  Peaks in histogram shows dominant directions in the spatial domain.  Highest peak and any one in the 80% of it are used to create a keypoint orientation.  For those who have the multiple peak of the same magnitude, there will be multiple keypoint at a same point and location but different orientation.
  20. 20. Page 20 As it can be seen that SIFT is robust to image noises 78% repeatability 10% of image pixel noise
  21. 21. Page 21 Steps: 1. Scale-Space Extrema Detection 2. Keypoint Localization 3. Orientation Assignment 4. Keypoint Descriptor
  22. 22. Page 22
  23. 23. Page 23
  24. 24. Page 24 50% >
  25. 25. • Background • SIFT(Scale Invariant FeatureTransform) Steps • Recognition Example • Conclusion Page 25
  26. 26. Page 26
  27. 27.  SIFT keypoints are useful due to their distinctiveness for object detection.  They are invariants to scale, orientation, affine transformation.  They are robust to clutter backgrounds. Page 27
  28. 28. Questions are welcomed!? Page 28
  29. 29. Page 29
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