Efficient scale-space extraction and description of interest points
First Claim
Patent Images
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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Abstract
Method, system and computer program for efficiently extracting and describing scale-space interest points. It is designed towards low overall computational complexity. On one hand, the data acquired during extraction in the description phase is intensively re-used. On the other hand, an algorithmic optimization of the description that dramatically speeds up the process, is proposed.
First, the image is filtered with triangle kernel at different scales. The triangle filtered images are reused for extraction of the keypoints dominant orientation and the computation of the DAISY-like descriptor.
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Citations
20 Claims
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1. A method of keypoint scale-space extraction and description in an image, the method comprising the steps of:
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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. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9)
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10. A system comprising:
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a processor, the processor executing a program code adapted to perform 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 θ
xx is a second horizontal derivative of Gaussian over a filtered image response L(k, i, j) 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 parameters 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. - View Dependent Claims (11, 12, 13, 14, 15)
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16. A non-transitory computer readable medium storing a program code which, when executed by a processor, is adapted to 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, j) obtained in step a) at scale 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 parameters 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. - View Dependent Claims (17, 18, 19, 20)
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Specification