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

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

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 Scale Invariant
 Rotation Invariant
 illumination Invariant
 Robust to occlusion
 Robust to clutter
 Robust Noise
 Cost of extraction

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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

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 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,

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10. Page 10
Sampling last image in the octave for the next octave

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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

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Steps:
1. Scale-Space Extrema Detection
2. Keypoint Localization
3. Orientation Assignment
4. Keypoint Descriptor

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 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.

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 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

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729 keypoint
from thresholding on the contrast
536 keypoint
from thresholding on the ratio

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Steps:
1. Scale-Space Extrema Detection
2. Keypoint Localization
3. Orientation Assignment
4. Keypoint Descriptor

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 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.

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As it can be seen that SIFT is robust to
image noises
78% repeatability
10% of image pixel noise

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Steps:
1. Scale-Space Extrema Detection
2. Keypoint Localization
3. Orientation Assignment
4. Keypoint Descriptor

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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.
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28. Questions are welcomed!?
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