Multi-Image Matching

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Published on October 3, 2013

Author: slksaad

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Multi-Image Matching using Multi-Scale Oriented Patches

Internal Internal Multi-Image Matching using Multi-Scale Oriented Patches Matthew, Richard, Simon. (2005) Saad Khalaf Alqurashi

Internal Internal 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

Internal Internal 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.

Internal Internal Direct feature-based. Two main field in Image matching

Internal Internal Simpler than SIFT (Scale-invariant feature transform). Designed Specially for image matching. Why we use Multi-Scale Oriented Patches?

Internal Internal 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

Internal Internal Invariant features 2

Internal Internal 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

Internal Internal Harris corner detector We should easily recognize the point by looking through a small window Shifting 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.

Internal Internal Flat region no change in all directions Edge: no change along the edge direction corner: significant change in all directions Harris Corner Detector

Internal Internal Harris corner detector  Use a Gaussian function

Internal Internal Harris Detector: Workflow

Internal Internal Harris Detector: Workflow Compute corner response R

Internal Internal Harris Detector: Workflow Find points with large corner response:

Internal Internal Harris Detector: Workflow Take only the points of local maxima of R

Internal Internal Harris Detector: Workflow

Internal Internal 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.

Internal Internal 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.

Internal Internal Adaptive Non-Maximal Suppression

Internal Internal Figure 3. Repeatability of interest points, orientation and matching for multi-scale oriented patches at the finest pyramid level.

Internal Internal Figure 4. Descriptors are formed using an 8×8 sampling of bias/gain normalised intensity values, with a sample spacing of 5 pixels relative to the detection scale. This low frequency sampling gives the features some robustness to interest point location error, and is achieved by sampling at a higher pyramid level than the detection scale.

Internal Internal Feature Matching

Internal Internal Panoramic Image Stitching The researchers have been successfully tested their multi-image matching scheme on a panoramic images

Internal Internal

Internal Internal Conclusion presented a new type of invariant feature, which they call it Multi-Scale Oriented Patches. introduced two innovations in multi image matching.

Internal Internal References http://learnonline.canberra.edu.au/pluginfile.php/611932/mod_label/i ntro/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/i ntro/8890_CVIA_PG_WIT_2012_Lecture_6.pdf http://www.csie.ntu.edu.tw/~cyy/courses/vfx/08spring/lectures/hand outs/lec06_feature2_4up.pdf http://research.microsoft.com/pubs/70120/tr-2004-133.pdf

Internal Internal Thank you for listening Any questions?

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