## Find “interesting” pieces of the image - e.g. corners, salient regions
- Focus attention of algorithms
- Speed up computation
## Many possible uses in matching/recognition - Search
- Object recognition
- Image alignment & stitching
- Stereo
- Tracking
- …
## Interest points
## Local invariant photometric descriptors
## History - Matching ## 1. Matching based on correlation alone ## e.g. line segments ## Not very discriminating (why?) ## Solution : matching with interest points & correlation ## [ A robust technique for matching two uncalibrated images through the recovery of the unknown epipolar geometry, ## Z. Zhang, R. Deriche, O. Faugeras and Q. Luong, ## Artificial Intelligence 1995 ]
## Approach ## Extraction of interest points with the Harris detector ## Comparison of points with cross-correlation ## Verification with the fundamental matrix
## Harris detector
## Harris detector
## Harris detector
## Cross-correlation matching
## Global constraints
## Summary of the approach ## Very good results in the presence of occlusion and clutter - local information
- discriminant greyvalue information
- robust estimation of the global relation between images
- works well for
**limited view point changes** -
## Solution for more general view point changes - wide baseline matching (different viewpoint, scale and rotation)
- local
**invariant descriptors** based on greyvalue information
**Schmid & Mohr 1997**, Lowe 1999, Baumberg 2000, Tuytelaars & Van Gool 2000, Mikolajczyk & Schmid 2001, Brown & Lowe 2002, Matas et. al. 2002, Schaffalitzky & Zisserman 2002
## Approach ## 1) Extraction of interest points (characteristic locations) ## 2) Computation of local descriptors (**rotational invariants**) ## 3) Determining correspondences ## 4) Selection of similar images
## Harris detector
## Autocorrelation
## Background: Moravec Corner Detector
## Shortcomings of Moravec Operator ## Only tries 4 shifts. We’d like to consider “all” shifts. ## Uses a discrete rectangular window. We’d like to use a smooth circular (or later elliptical) window. ## Uses a simple min function. We’d like to characterize variation with respect to direction.
## Harris detector
## Harris detector
## Harris detector
## Harris detection ## Auto-correlation matrix - captures the structure of the local neighborhood
- measure based on
**eigenvalues** of M - 2 strong eigenvalues => interest point
- 1 strong eigenvalue => contour
- 0 eigenvalue => uniform region
## Interest point detection
## Some Details from the Harris Paper ## Corner strength R = Det(M) – k Tr(M)2 ## Let and be the two eigenvalues ## Tr(M) = + ## Det(M) = ## R is positive for corners, - for edges, and small for flat regions ## Select corner pixels that are 8-way local maxima
## Determining correspondences
## Distance Measures ## We can use the sum-square difference of the values of the pixels in a square neighborhood about the points being compared.
## Some Matching Results
## Summary of the approach ## Basic feature matching = **Harris Corners & Correlation** ## Very good results in the presence of occlusion and clutter - local information
- discriminant greyvalue information
- invariance to image rotation and illumination
## Not invariance to scale and affine changes ## Solution for more general view point changes
## Rotation/Scale Invariance
## Rotation/Scale Invariance
## Rotation/Scale Invariance
## Rotation/Scale Invariance
## Rotation/Scale Invariance
## Rotation/Scale Invariance
## Rotation/Scale Invariance
## Rotation/Scale Invariance
## Rotation/Scale Invariance
## Invariant Features ## Local image descriptors that are *invariant *(unchanged) under image transformations
## Canonical Frames
## Canonical Frames
## Multi-Scale Oriented Patches
## Multi-Scale Oriented Patches ## Sample scaled, oriented patch
## Multi-Scale Oriented Patches ## Sample scaled, oriented patch - 8x8 patch, sampled at 5 x scale
## Multi-Scale Oriented Patches ## Sample scaled, oriented patch - 8x8 patch, sampled at 5 x scale
## Bias/gain normalised
## Matching Interest Points: Summary ## Harris corners / correlation - Extract and match repeatable image features
- Robust to clutter and occlusion
- BUT not invariant to scale and rotation
## Multi-Scale Oriented Patches - Corners detected at multiple scales
- Descriptors oriented using local gradient
- Also, sample a blurred image patch
- Invariant to scale and rotation
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