• Share
  • Email
  • Embed
  • Like
  • Save
  • Private Content
Distinctive image features from scale invariant keypoint
 

Distinctive image features from scale invariant keypoint

on

  • 33 views

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

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

Statistics

Views

Total Views
33
Views on SlideShare
33
Embed Views
0

Actions

Likes
0
Downloads
0
Comments
1

0 Embeds 0

No embeds

Accessibility

Upload Details

Uploaded via as Microsoft PowerPoint

Usage Rights

© All Rights Reserved

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel

11 of 1 previous next

  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Processing…
Post Comment
Edit your comment

    Distinctive image features from scale invariant keypoint Distinctive image features from scale invariant keypoint Presentation Transcript

    • DAVIDG. LOWE 2004 Presentation by Hadi Sinaee Sharif University ofTechnology MachineVision Course, Spring 2014 Instructor: Dr. M.Jamzad
    • • 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 Noise  Cost of extraction
    • 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 of a same object.  To efficiently detect stable keypoint locations in scale space, Lowe(1999) use DoG of two nearby scales,
    • 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 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
    • 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 localized along an edge  If the extremum is greater than 0.5 it means the extremum is closer to another sample point.
    • 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
    • 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 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.
    • 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, affine transformation.  They are robust to clutter backgrounds. Page 27
    • Questions are welcomed!? Page 28
    • Page 29