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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- 1. DAVIDG. LOWE 2004 Presentation by Hadi Sinaee Sharif
University ofTechnology MachineVision Course, Spring 2014
Instructor: Dr. M.Jamzad
- 2. Background SIFT(Scale Invariant FeatureTransform) Steps
Recognition Example Conclusion Page 2
- 3. Page 3 Object Detection 3D reconstruction
MotionTracking
- 4. Page 4 Scale Invariant Rotation Invariant illumination
Invariant Robust to occlusion Robust to clutter Robust Noise Cost
of extraction
- 5. Page 5
- 6. Background SIFT(Scale Invariant FeatureTransform) Steps
Recognition Example Conclusion Page 6
- 7. Page 7 Steps: 1. Scale-Space Extrema Detection 2. Keypoint
Localization 3. Orientation Assignment 4. Keypoint Descriptor
- 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. Page 9
- 10. Page 10 Sampling last image in the octave for the next
octave
- 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. Page 12
- 13. Page 13 Steps: 1. Scale-Space Extrema Detection 2. Keypoint
Localization 3. Orientation Assignment 4. Keypoint Descriptor
- 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. 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. Page 16
- 17. Page 17 729 keypoint from thresholding on the contrast 536
keypoint from thresholding on the ratio
- 18. Page 18 Steps: 1. Scale-Space Extrema Detection 2. Keypoint
Localization 3. Orientation Assignment 4. Keypoint Descriptor
- 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. Page 20 As it can be seen that SIFT is robust to image
noises 78% repeatability 10% of image pixel noise
- 21. Page 21 Steps: 1. Scale-Space Extrema Detection 2. Keypoint
Localization 3. Orientation Assignment 4. Keypoint Descriptor
- 22. Page 22
- 23. Page 23
- 24. Page 24 50% >
- 25. Background SIFT(Scale Invariant FeatureTransform) Steps
Recognition Example Conclusion Page 25
- 26. Page 26
- 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. Questions are welcomed!? Page 28
- 29. Page 29