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Efficient scale-space extraction and description of interest points

  • US 8,798,377 B2
  • Filed: 09/01/2010
  • Issued: 08/05/2014
  • Est. Priority Date: 02/08/2010
  • Status: Active Grant
First Claim
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1. A method of keypoint scale-space extraction and description in an image, the method comprising the steps of:

  • a) filtering the image with triangle kernel filters at different scales;

    b) computing an approximation of a determinant of Hessian at each scale, the approximation at each scale k being calculated as |∂

    kxx(i, j)·



    kyy(i, j)−



    kxy(i, j)2| where ∂

    xx is a second horizontal derivative of Gaussian over a filtered image response L(k, i, i) obtained in step a) at scale k at point (i, j), ∂

    yy is a second vertical derivative of Gaussian over the filtered image response L(k, i, j), and ∂

    xy is the cross derivative of Gaussian over the filtered image response L(k, i, j), using a first design parameter d1 for computing a second horizontal and vertical derivatives of Gaussian, ∂

    xx and ∂

    yy, and a second design parameter d2 for computing a cross derivative of Gaussian ∂

    xy, being both the first design parameter d1 and the second design parameter d2 proportional to a deviation σ

    of a second derivative of Gaussian kernel;

    c) searching for extremum values both within a single scale and along the scale space of the approximation of the determinant of Hessian obtained in step b) and calculating the keypoints from these extrema values;

    d) for each keypoint, localized at an extremum value, detecting the dominant orientations from gradient information calculated using the filtered image response obtained in step a); and

    e) calculating for each dominant orientation a keypoint descriptor.

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