multi-image matching
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Multi-Image Matching using Multi-Scale Oriented PatchesTRANSCRIPT
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Multi-Image Matching using Multi-Scale Oriented Patches
Matthew, Richard, Simon. (2005)
Saad Khalaf Alqurashi
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Overview Introduction Image matching Why use Multi-Scale Oriented Patches? invariant features Advantages of invariant local features Harris corner detector Interest Point Detectors Adaptive Non-Maximal Suppression Feature Matching Panoramic Image Stitching Conclusion
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Introduction:
The article is about describe multi-view matching framework based on a new type of invariant feature.
This feature which will uses is Harris
corners in discrete scale-space and oriented using a blurred local gradient.
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Direct
feature-based.
Two main field in Image matching
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Simpler than SIFT(Scale-invariant feature transform).
Designed Specially for image matching.
Why we use Multi-Scale Oriented Patches?
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invariant features
These approaches are invariant features, which use large amounts of local image data around salient features to form invariant descriptors for indexing and matching
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Invariant features 2
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Advantages of invariant local features
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
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Harris corner detector
We should easily recognize the point by looking through a small windowShifting a window in any direction should give a large Change in intensity
Reference : C. Harris and M. Stephens, “A combined corner and edge detector”, Proceedings of the 4th AlveyVision Conference, 1988, pp. 147--151.
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Flat regionno change in all directions Edge:
no change along
the edge direction
corner:significant
change in all directions
Harris Corner Detector
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Harris corner detector
Use a Gaussian function
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Harris Detector: Workflow
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Harris Detector: Workflow
Compute corner response R
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Harris Detector: WorkflowFind points with large corner response:
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Harris Detector: Workflow
Take only the points of local maxima of R
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Harris Detector: Workflow
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Interest Point Detectors
use multi-scale Harris corners For each input image I(x, y) we form a
Gaussian image pyramid Pl(x, y) using a subsampling
rate s = 2 and pyramid smoothing width p = 1.0 Interest points are extracted from each level of
the pyramid.
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Figure 1. Multi-scale Oriented Patches (MOPS) extracted at five pyramid levels from one of the Matier images. The
boxes show the feature orientation and the region from which the descriptor vector is sampled.
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Adaptive Non-Maximal Suppression
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Figure 3. Repeatability of interest points, orientationand matching for multi-scale oriented patches at thefinest pyramid level.
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Figure 4. Descriptors are formed using an 8×8 samplingof bias/gain normalised intensity values, with a
sample spacing of 5 pixels relative to the detectionscale. This low frequency sampling gives the features
some robustness to interest point location error, and isachieved by sampling at a higher pyramid level than
the detection scale.
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Feature Matching
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Panoramic Image Stitching
The researchers have been successfully tested their multi-imagematching scheme on a panoramic images
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Conclusion
presented a new type of invariant feature, which they call it Multi-Scale Oriented Patches.
introduced two innovations in multi image matching.
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References
http://learnonline.canberra.edu.au/pluginfile.php/611932/mod_label/intro/Brown_cvpr05_multi_image_matching.pdf
http://mesh.brown.edu/engn1610/szeliski/04-FeatureDetectionAndMatching.pdf
http://learnonline.canberra.edu.au/pluginfile.php/611936/mod_label/intro/8890_CVIA_PG_WIT_2012_Lecture_6.pdf
http://www.csie.ntu.edu.tw/~cyy/courses/vfx/08spring/lectures/handouts/lec06_feature2_4up.pdf
http://research.microsoft.com/pubs/70120/tr-2004-133.pdf
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Thank you for listening Any questions?