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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?