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DETECTING LEVELLING RODS
USING SIFT FEATURE MATCHING
GROUP 1
MSc Course 2006-08
25TH
June 2007
Sajid Pareeth
Sonam Tashi
G...
Objective
Introduction
SIFT Algorithm
SIFT-Keypoints
Extraction
Keypoints
Matching
Work Flow
RANSAC
Advantages/
Limitation...
Summary of Steps
Images
SIFT Key Feature Extraction
Keypoint Matching
Removing Outliers - RANSAC
Transformation
Introducti...
Background - SIFT
 Published by David Lowe et al. 1999-2004
 Algorithm to extract features that are invariant to
rotatio...
A keypoint descriptor is created by first computing the
gradient magnitude and orientation at each image sample
point in a...
Scale Invariant Detection
 Consider regions (e.g. circles) of different sizes around
a point
 Regions of corresponding s...
 The problem: how do we choose
corresponding circles independently in each
image?
Scale Invariant Detection
SIFT Algorith...
 Solution:
 Design a function on the region (circle), which is
“scale invariant” (the same for corresponding
regions, ev...
 Common approach
scale = 1/2
f
region size
Image 1 f
region size
Image 2
Take a local maximum of this function
Observatio...
scale
x
y
← DoG →
←DoG→
SIFT(Lowe)
Maxima and minima of the difference-of-Gaussian
images detected by comparing a pixel (m...
Gaussian pyramids
Scale
First Octave
Scale
Next
Octave
Difference of Gaussians
Difference of Gaussian(DoG)
SIFT-Keypoints
...
Keypoint Descriptors
Feature Vectors
 Thresholded image gradients sampled over:
 16x16 array of locations in scale space...
Criteria for Selection
 Prominent
 Distinguishable-from neighborhood
 Invariant
 Stable to disturbances
 Rare (except...
SIFT keypoints
 Detected Keypoints in reference and candidate image
 Keypoints in both images will be matched
Candidate ...
Keypoint Matching
 Fundamental aspect in computer vision.
 Based on Euclidean distance of the feature vectors.
 Nearest...
90% of the false matches are
removed.
False matches due to ambiguos
features or features arise from
background clutter.
Re...
RANSAC: Algorithm
RANdom SAmple Consensus
 Estimate parameters of a mathematical model from a set of
observed data which ...
Ransac
RANSAC: Result
SIFT Algorithm
SIFT-Keypoints
Extraction
Work Flow
RANSAC
Advantages/
Limitations
Demo
Transformatio...
Make the shortest image the
same height as the other image.
Append Images
Transformation
tform = cp2tform(M1,M2,’affine')
...
2
3
Rod vs. OpenShrubs
1
Matches: 9
2
Rod: 626 keypoints found
Image: 28958 keypoints found
Matches: 16
…Good Matches
SIFT...
2
Rod Vs Human Occlusion??
1
Rod: 626 keypoints found
Image: 2347 keypoints found
Matches: 155
Rod: 626 keypoints found
Im...
Limitations
1
Rod: 626 keypoints found
Image: 17927 keypoints found
Matches: 16
Matches: 4
2
Rod: 626 keypoints found
Imag...
Advantages
 Locality: features are local, so robust to occlusion and clutter
(no prior segmentation)
 Distinctiveness: i...
Problems/Enhancement
 Only invariant to affine transformations to a certain degree
 Best performance on highly textured ...
Demo
Demo
SIFT Algorithm
SIFT-Keypoints
Extraction
Keypoints
Matching
Work Flow
RANSAC
Advantages/
Limitations
Transformat...
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Feature Matching using SIFT algorithm

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Feature Matching using SIFT algorithm; co-authored presentation on Photogrammetry studio by Sajid Pareeth, Gabriel Vincent Sanya, Sonam Tashi and Michael Mutale

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Feature Matching using SIFT algorithm

  1. 1. DETECTING LEVELLING RODS USING SIFT FEATURE MATCHING GROUP 1 MSc Course 2006-08 25TH June 2007 Sajid Pareeth Sonam Tashi Gabriel Vincent Sanya Michael Mutale PHOTOGRAMMETRY STUDIO
  2. 2. Objective Introduction SIFT Algorithm SIFT-Keypoints Extraction Keypoints Matching Work Flow RANSAC Advantages/ Limitations Demo  To develop a MATLAB procedure for the detection of levelling rods.  To carry out matching based on Algorithms proposed by David Lowe  Object recognition using invariant features Transformation Examples
  3. 3. Summary of Steps Images SIFT Key Feature Extraction Keypoint Matching Removing Outliers - RANSAC Transformation Introduction SIFT Algorithm SIFT-Keypoints Extraction Keypoints Matching Work Flow RANSAC Advantages/ Limitations Demo Transformation Examples
  4. 4. Background - SIFT  Published by David Lowe et al. 1999-2004  Algorithm to extract features that are invariant to rotation, scaling and  partially invariant to changes in illumination and camera viewpoint  Resulting features are highly distinctive Consists of (Major stages of computation):  Scale-space extrema detection-the Gaussian Widow  Keypoint localization- 4x4 samples per window in 8 directions  Orientation assignment  Keypoint descriptor-feature vector is modified to reduce the effects of illumination change. The vector is normalized to unit length. large gradient magnitudes by thresholding the values in the unit feature vector to each be no larger than 0.2, and then renormalizing to unit length. SIFT Algorithm SIFT-Keypoints Extraction Keypoints Matching Work Flow RANSAC Advantages/ Limitations Demo Transformation Examples Introduction
  5. 5. A keypoint descriptor is created by first computing the gradient magnitude and orientation at each image sample point in a region around the keypoint location, These are weighted by a Gaussian window, indicated by the overlaid circle. These samples are then accumulated into orientation histograms summarizing the contents over 4x4 subregions, The length of each arrow corresponding to the sum of the gradient magnitudes near that direction within the region.
  6. 6. Scale Invariant Detection  Consider regions (e.g. circles) of different sizes around a point  Regions of corresponding sizes will look the same in both images SIFT-Keypoints Extraction Keypoints Matching Work Flow RANSAC Advantages/ Limitations Demo Transformation Examples SIFT Algorithm Introduction
  7. 7.  The problem: how do we choose corresponding circles independently in each image? Scale Invariant Detection SIFT Algorithm SIFT-Keypoints Extraction Keypoints Matching Work Flow RANSAC Advantages/ Limitations Demo Transformation Examples SIFT Algorithm Introduction
  8. 8.  Solution:  Design a function on the region (circle), which is “scale invariant” (the same for corresponding regions, even if they are at different scales) Example: average intensity. For corresponding regions (even of different sizes) it will be the same.  For a point in one image, we can consider it as a function of region size (circle radius) scale = 1/2 f region size Image 1 f region size Image 2 Scale Invariant Detection SIFT-Keypoints Extraction Keypoints Matching Work Flow RANSAC Advantages/ Limitations Demo Transformation Examples SIFT Algorithm Introduction
  9. 9.  Common approach scale = 1/2 f region size Image 1 f region size Image 2 Take a local maximum of this function Observation: region size, for which the maximum is achieved, should be invariant to image scale. s1 s2 Important: this scale invariant region size is found in each image independently! Scale Invariant Detection SIFT-Keypoints Extraction Keypoints Matching Work Flow RANSAC Advantages/ Limitations Demo Transformation Examples SIFT Algorithm Introduction
  10. 10. scale x y ← DoG → ←DoG→ SIFT(Lowe) Maxima and minima of the difference-of-Gaussian images detected by comparing a pixel (marked with X) to its 26 neighbors in 3x3 regions at the current and adjacent scales (marked with circles). SIFT-Keypoints Extraction Keypoints Matching Work Flow RANSAC Advantages/ Limitations Demo Transformation Examples SIFT Algorithm Introduction
  11. 11. Gaussian pyramids Scale First Octave Scale Next Octave Difference of Gaussians Difference of Gaussian(DoG) SIFT-Keypoints Extraction Keypoints Matching Work Flow RANSAC Advantages/ Limitations Demo Transformation Examples SIFT Algorithm Introduction
  12. 12. Keypoint Descriptors Feature Vectors  Thresholded image gradients sampled over:  16x16 array of locations in scale space  Histogram of 4x4 samples per window in 8 directions  Gaussian weighting around center  8 orientations x 4 x 4 histogram array = 128 dimensional feature vector  4x4 Gradient window SIFT Algorithm Keypoints Matching Work Flow RANSAC Advantages/ Limitations Demo Transformation Examples SIFT-Keypoints Extraction Introduction Gaussian Gradient Window
  13. 13. Criteria for Selection  Prominent  Distinguishable-from neighborhood  Invariant  Stable to disturbances  Rare (exceptional) - from other selected points  Meaningful - with respect to image interpretation SIFT Algorithm Keypoints Matching Work Flow RANSAC Advantages/ Limitations Demo Transformation Examples SIFT-Keypoints Extraction Introduction
  14. 14. SIFT keypoints  Detected Keypoints in reference and candidate image  Keypoints in both images will be matched Candidate image with keypointsReference image with keypoints SIFT Algorithm Keypoints Matching Work Flow RANSAC Advantages/ Limitations Demo Transformation Examples SIFT-Keypoints Extraction Introduction
  15. 15. Keypoint Matching  Fundamental aspect in computer vision.  Based on Euclidean distance of the feature vectors.  Nearest neighbor algorithms.  Product between descriptors is calculated.  Inverse cosine of the products gives the Euclidean distance  Matches with Ratio of vector angles from the nearest to second nearest neighbor less than distRatio value are selected. SIFT Algorithm Keypoints Matching Work Flow RANSAC Advantages/ Limitations Demo Transformation Examples SIFT-Keypoints Extraction Introduction
  16. 16. 90% of the false matches are removed. False matches due to ambiguos features or features arise from background clutter. Reliable object recognition with few best Matches Remove outliers Matched points SIFT Algorithm SIFT-Keypoints Extraction Work Flow RANSAC Advantages/ Limitations Demo Transformation Examples Keypoints Matching Introduction
  17. 17. RANSAC: Algorithm RANdom SAmple Consensus  Estimate parameters of a mathematical model from a set of observed data which contains outliers.  A model is fitted using hypothetical inliers.  If other data fits to the model, added to the inliers.  Reestimated the model with the new set of inliers.  We used ransac-fit-homography which Robustly fits a homography to a set of matched points. SIFT Algorithm SIFT-Keypoints Extraction Work Flow Advantages/ Limitations Demo Transformation Examples Keypoints Matching RANSAC Introduction
  18. 18. Ransac RANSAC: Result SIFT Algorithm SIFT-Keypoints Extraction Work Flow RANSAC Advantages/ Limitations Demo Transformation Examples Keypoints Matching Introduction
  19. 19. Make the shortest image the same height as the other image. Append Images Transformation tform = cp2tform(M1,M2,’affine') SIFT Algorithm SIFT-Keypoints Extraction Keypoints Matching Work Flow Advantages/ Limitations Demo Transformation Examples RANSAC Introduction TStructure
  20. 20. 2 3 Rod vs. OpenShrubs 1 Matches: 9 2 Rod: 626 keypoints found Image: 28958 keypoints found Matches: 16 …Good Matches SIFT Algorithm SIFT-Keypoints Extraction Keypoints Matching Work Flow RANSAC Advantages/ Limitations Demo Examples Transformation Introduction
  21. 21. 2 Rod Vs Human Occlusion?? 1 Rod: 626 keypoints found Image: 2347 keypoints found Matches: 155 Rod: 626 keypoints found Image: 2347 keypoints found Matches: 144 2 3 …Good Matches SIFT Algorithm SIFT-Keypoints Extraction Keypoints Matching Work Flow RANSAC Advantages/ Limitations Demo Transformation Examples Introduction
  22. 22. Limitations 1 Rod: 626 keypoints found Image: 17927 keypoints found Matches: 16 Matches: 4 2 Rod: 626 keypoints found Image: 30505 keypoints found 3 SIFT Algorithm SIFT-Keypoints Extraction Keypoints Matching Work Flow RANSAC Advantages/ Limitations Demo Transformation Examples Introduction
  23. 23. Advantages  Locality: features are local, so robust to occlusion and clutter (no prior segmentation)  Distinctiveness: individual features can be matched to a large database of objects  Quantity: many features can be generated for even small objects  Efficiency: close to real-time performance  Extensibility: can easily be extended to wide range of differing feature types, with each adding robustness SIFT Algorithm SIFT-Keypoints Extraction Keypoints Matching Work Flow RANSAC Advantages/ Limitations Demo Transformation Examples Introduction
  24. 24. Problems/Enhancement  Only invariant to affine transformations to a certain degree  Best performance on highly textured images  Use Principal components analysis PCA instead of Gaussian weighting for gradients Demo SIFT Algorithm SIFT-Keypoints Extraction Keypoints Matching Work Flow RANSAC Advantages/ Limitations Transformation Examples Introduction
  25. 25. Demo Demo SIFT Algorithm SIFT-Keypoints Extraction Keypoints Matching Work Flow RANSAC Advantages/ Limitations Transformation Examples Introduction THANK YOU!!

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