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Overview

Overview. Introduction to local features Harris interest points + SSD, ZNCC, SIFT Scale & affine invariant interest point detectors Evaluation and comparison of different detectors Region descriptors and their performance . Scale invariance - motivation.

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Overview

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  1. Overview • Introduction to local features • Harris interest points + SSD, ZNCC, SIFT • Scale & affine invariant interest point detectors • Evaluation and comparison of different detectors • Region descriptors and their performance

  2. Scale invariance - motivation • Description regions have to be adapted to scale changes • Interest points have to be repeatable for scale changes

  3. Harris detector + scale changes Repeatability rate

  4. Scale adaptation Scale change between two images Scale adapted derivative calculation

  5. Scale adaptation Scale change between two images Scale adapted derivative calculation

  6. Scale adaptation where are the derivatives with Gaussian convolution

  7. Scale adaptation where are the derivatives with Gaussian convolution Scale adapted auto-correlation matrix

  8. Harris detector – adaptation to scale

  9. Multi-scale matching algorithm

  10. Multi-scale matching algorithm 8 matches

  11. Multi-scale matching algorithm Robust estimation of a global affine transformation 3 matches

  12. Multi-scale matching algorithm 3 matches 4 matches

  13. Multi-scale matching algorithm 3 matches 4 matches highest number of matches correct scale 16 matches

  14. Matching results Scale change of 5.7

  15. Matching results 100% correct matches (13 matches)

  16. Scale selection • For a point compute a value (gradient, Laplacian etc.) at several scales • Normalization of the values with the scale factor • Select scale at the maximum → characteristic scale • Exp. results show that the Laplacian gives best results e.g. Laplacian scale

  17. Scale selection • Scale invariance of the characteristic scale s norm. Lap. scale

  18. Scale selection • Scale invariance of the characteristic scale s norm. Lap. norm. Lap. scale scale • Relation between characteristic scales

  19. Harris-Laplace Laplacian Scale-invariant detectors • Harris-Laplace (Mikolajczyk & Schmid’01) • Laplacian detector (Lindeberg’98) • Difference of Gaussian (Lowe’99)

  20. Harris-Laplace invariant points + associated regions [Mikolajczyk & Schmid’01] multi-scale Harris points selection of points at maximum of Laplacian

  21. Matching results 213 / 190 detected interest points

  22. Matching results 58 points are initially matched

  23. Matching results 32 points are matched after verification – all correct

  24. Matching results all matches are correct (33)

  25. Laplacian of Gaussian (LOG)

  26. LOG detector Detection of maxima and minima of Laplacian in scale space

  27. Difference of Gaussian (DOG) • Difference of Gaussian approximates the Laplacian

  28. DOG detector • Fast computation, scale space processed one octave at a time

  29. Local features - overview • Scale invariant interest points • Affine invariant interest points • Evaluation of interest points • Descriptors and their evaluation

  30. Affine invariant regions - Motivation • Scale invariance is not sufficient for large baseline changes detected scale invariant region projected regions, viewpoint changes can locally be approximated by an affine transformation

  31. Affine invariant regions - Motivation

  32. Affine invariant regions - Example

  33. Harris/Hessian/Laplacian-Affine • Initialize with scale-invariant Harris/Hessian/Laplacian points • Estimation of the affine neighbourhood with the second moment matrix [Lindeberg’94] • Apply affine neighbourhood estimation to the scale-invariant interest points [Mikolajczyk & Schmid’02, Schaffalitzky & Zisserman’02] • Excellent results in a recent comparison

  34. Affine invariant regions • Based on the second moment matrix (Lindeberg’94) • Normalization with eigenvalues/eigenvectors

  35. Affine invariant regions Isotropic neighborhoods related by image rotation

  36. Affine invariant regions - Estimation • Iterative estimation – initial points

  37. Affine invariant regions - Estimation • Iterative estimation – iteration #1

  38. Affine invariant regions - Estimation • Iterative estimation – iteration #2

  39. Affine invariant regions - Estimation • Iterative estimation – iteration #3, #4

  40. Harris-Affine Harris-Laplace Harris-Affine versus Harris-Laplace

  41. Harris/Hessian-Affine Harris-Affine Hessian-Affine

  42. Harris-Affine

  43. Hessian-Affine

  44. Matches 22 correct matches

  45. Matches 33 correct matches

  46. Maximally stable extremal regions (MSER) [Matas’02] • Extremal regions: connected components in a thresholded image (all pixels above/below a threshold) • Maximally stable: minimal change of the component (area) for a change of the threshold, i.e. region remains stable for a change of threshold • Excellent results in a recent comparison

  47. Maximally stable extremal regions (MSER) Examples of thresholded images high threshold low threshold

  48. MSER

  49. Overview • Introduction to local features • Harris interest points + SSD, ZNCC, SIFT • Scale & affine invariant interest point detectors • Evaluation and comparison of different detectors • Region descriptors and their performance

  50. Evaluation of interest points • Quantitative evaluation of interest point/region detectors • points / regions at the same relative location and area • Repeatability rate : percentage of corresponding points • Two points/regions are corresponding if • location error small • area intersection large • [K. Mikolajczyk, T. Tuytelaars, C. Schmid, A. Zisserman, J. Matas, F. Schaffalitzky, T. Kadir & L. Van Gool ’05]

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