Published on October 3, 2013
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 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?
Multi-Image Matching using Multi-Scale Oriented Patches June 1, 2005 Download PDF BibTex Authors Simon Winder Rick Szeliski ...
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Matching is achieved using a fast nearest neighbour algorithm that indexes features based on their low frequency Haar wavelet coefficients.