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Distinctive Image Features from Scale-Invariant Keypoints.

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Presentation on theme: "Distinctive Image Features from Scale-Invariant Keypoints."— Presentation transcript:

1 Distinctive Image Features from Scale-Invariant Keypoints

2 Note ppt Lowe google feature paper, ppt.. paper, ppt, web-page. ( )

3 Contents Introduction SIFT Overview Scale-space extrema detection Keypoint localization Orientation assignment Keypoint descriptor Test and Result Conclusion

4 Introduction Ideal Interest Points / Regions

5 Introduction Harris Corner Detector Rotation invariance Partial invariance to affine change

6 Introduction Harris Corner Detector Non-invariance to scale change edgecorner

7 Introduction SIFT The Scale Invariant Feature Transform Choosing features that are invariant to image scaling and rotation Partially invariant to changes in illumination and 3D camera viewpoint Well localized in both spatial and frequency domain - Resistant to noise, clutter, and occlusion Features are highly distinctive, matched with high probability against large number of features See…… Application Object recognition Automatic mosaic Tracking Robot localization 3D scene modeling Panoramas

8 Introduction

9 Introduction

10 SIFT overview 1. Scale-space extrema detection 2. Keypoint localization and filtering 3. Orientation assignment 4. Keypoint descriptor Detector Descriptor

11 SIFT overview 1. Scale-space extrema detection 2. Keypoint localization and filtering 3. Orientation assignment 4. Keypoint descriptor Detector Descriptor

12 1. Scale-space extrema detection A good function for scale detection has one stable sharp peak f region size bad Good ! L or DOG(Difference of Gaussians) kernel is a matching filter

13 1. Scale-space extrema detection DOG(Difference of Gaussians) Construct scale-space Take differences Convolve with Gaussian Downsample

14 1. Scale-space extrema detection For example Construct scale-space

15 1. Scale-space extrema detection For example Take differences

16 1. Scale-space extrema detection Scale-space extrema Compare a pixel with its 26 neighbors in 3*3 regions at the current and adjacent scales

17 1. Scale-space extrema detection For example Scale-space extrema

18 SIFT overview 1. Scale-space extrema detection 2. Keypoint localization and filtering 3. Orientation assignment 4. Keypoint descriptor Detector Descriptor

19 2. Keypoint localization and filtering Reject points with bad contrast DOG smaller than 0.03 (image values in [0, 1])

20 2. Keypoint localization and filtering Reject points with strong edge response in one direction only To check if ratio of principal curvature is below some threshold, r

21 SIFT overview 1. Scale-space extrema detection 2. Keypoint localization and filtering 3. Orientation assignment 4. Keypoint descriptor Detector Descriptor

22 3. Orientation assignment Descriptor computed relative to keypoints orientation achieves rotation invariance Let, for a keypointm L is the image with the closest scale Compute the orientation histogram - within a region around the keypoint (16 16) Compute gradient magnitude and orientation using finite differences

23 3. Orientation assignment

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28 SIFT overview 1. Scale-space extrema detection 2. Keypoint localization and filtering 3. Orientation assignment 4. Keypoint descriptor Detector Descriptor

29 Keypoint descriptor The computation of the keypoint descriptor A set of keypoints are obtained from each reference image Each such keypoint has a graphical descriptor – which is a 128 component vector (4 4 8) keypoint descriptors complexity

30 Keypoint descriptor Storing

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32 Keypoint descriptor Matching Test image gives a new set of (keypoint, vector) pair Find the nearest (top 2) descriptors in database Acceptance of a match Storage using k-d trees - Use the Best-Bin-First(BBF) algorithm Ratio of distance to first nearest descriptor to that of second < threshold (0.8)

33 Test and Result

34 Test and Result

35 Test and Result

36 Test and Result SIFT didnt work Large illumination change

37 Test and Result SIFT didnt work Non-rigid deformation

38 Conclusion SIFT A novel method for detecting interest points Invariant to - image scaling - translation - rotation Robust matching across substantial range of - distortion - change in 3D view point - addition of noise - change in illumination SIFT extensions PCA-SIFT SURF Approx SIFT GPU implementation


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