Interest Operators Find “interesting” pieces of the image



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tarix17.11.2018
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Interest Operators

  • 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

  • 2. Matching based on geometric primitives

  • 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


Invariant Features

  • 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



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



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

    • I’ = (I – )/


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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