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

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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

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,

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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

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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

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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

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25. Background SIFT(Scale Invariant FeatureTransform) Steps Recognition Example Conclusion Page 25

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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

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