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81.3% Affine invariant feature detector and image correlation Martin Bujňák © 2004 Martin Bujňák,

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Presentation on theme: "81.3% Affine invariant feature detector and image correlation Martin Bujňák © 2004 Martin Bujňák,"— Presentation transcript:

1 81.3% Affine invariant feature detector and image correlation Martin Bujňák © 2004 Martin Bujňák,,

2 Previous work… (simple and fast) Moravec detector –using small window –determining the average changes of image intensity –big-change implies feature © 2004 Martin Bujňák,,

3 Previous work… (simple and fast) Harris, Stephens –analytically described and enhanced Moravec detector –correlation with neighbors replaced with simple calculations - fast © 2004 Martin Bujňák,,

4 Works fine but,… good results, but –region is described by one point –additional information about local shape can be obtained by principal component analysis would help, but only for small very changes even though usable but –many feature points must be detected and robust brute force algorithm for image correspondence must be used (RANSAC…) © 2004 Martin Bujňák,,

5 Idea Let the feature point be rotation, scale and translation invariant (or other similarity transformations)… –not the miracle, but we get more stable features –let the feature by intersection of "bigger or stronger" edge (figure 1) figure 1. Features in edge intersection. © 2004 Martin Bujňák,,

6 Idea Build graph –feature is graph node –two features are connected if there exists direct (or long A-B are connected) edge between them. Edges are not oriented. A B X1 X2 X3 © 2004 Martin Bujňák,,

7 Idea Feature information –neighborhood with other vertices – ordered by angle –distance ration to neighbor for each connection (marked red) A n1n2 n3 φ2φ2 φ1φ1 φ3φ3 φ4φ4 Φ = 0 © 2004 Martin Bujňák,,

8 Advanced – pie – slice correlation get elliptical cut bounded by two edges and elliptical arc (and call it pie slice) A n1 n3 φ2φ2 φ1φ1 φ3φ3 φ4φ4 Φ = 0 n3-n1 pie slice Feature © 2004 Martin Bujňák,,

9 Advanced – pie – slice correlation Correlation process –two features are at the first rotated to best fit incidence information –correlated each pie-slice separately (using standard similarity or dissimilarity measures) © 2004 Martin Bujňák,,

10 Advanced – pie – slice correlation Results –more stable for affine and also for projective transformation –good “corners” will remain –feature don’t need to well correlate in all slices connectivity information helps graph topology speeds up RANSAC elimination pie-slice correlation give better matching probability than standard one. © 2004 Martin Bujňák,,

11 H.P. Moravec, Visual mapping by a robot rover, Proc. of the 6th International Joint Conference on Artificial Intelligence, pp.598-600, 1979 C. Harris - M. Stephens, A combined corner and edge detector, Fourth Alvey Vision Conference, pp.147-151, 1988 R. Deriche - G. Giraudon, "A computational approach for corner and vertex detection", International Journal of Computer Vision, 1(2):167-187, 1993 S.M. Smith - J.M. Brady. "SUSAN - a new approach to low level image processing". Int. Journal of Computer Vision, Vol.23, Nr.1, pp.45-78, 1997 K. Mikolajczyk - S.M. Smith. "Scale & affine invariant interest point detectors". INRIA Rhne-Alpes GRAVIR-CNRS, 655 av. de l’Europe, 38330 Montbonnot, France T. Kadir - A. Zisserman - M.Brady. "An affinne invariant salient region detector". Department of Engineering Science, University of Oxford, UK. M. Pollefeys - L. Van Gool - M. Vergauwen - F. Verbiest - K. Cornelis - J. Tops - R. Koch, Visual modeling with a hand-held camera, International Journal of Computer Vision 59(3), 207-232, 2004 References © 2004 Martin Bujňák,,

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