Feature based image registration
First Claim
Patent Images
1. A method, comprising:
- applying a first grid to a blurred first image;
computing a determinant of a Hessian matrix at predetermined points of the first grid on the blurred first image;
determining low resolution feature points in the blurred first image based on the first grid;
applying a second grid to the blurred first image, the second grid being finer than the first grid;
computing a determinant of a Hessian matrix at predetermined points of the second grid on the blurred first image;
determining high resolution feature points in the blurred first image based on the second grid;
creating a first set of blurred first image key points;
extracting a first feature descriptor for each of the blurred first image key points;
applying the first grid to a blurred second image;
computing a determinant of a Hessian matrix at predetermined points of the first grid on the blurred second image;
determining low resolution feature points in the blurred second image based on the first grid;
applying the second grid to the blurred second image;
computing a determinant of a Hessian matrix at predetermined points of the second grid on the blurred second image;
determining high resolution feature points in the blurred second image based on the second grid;
creating a second set of blurred second image key pointsextracting a second feature descriptor for each of the blurred second image key points;
selecting blurred first image key points and blurred second image key points for matching based on a measure of closeness of the first feature descriptors and the second feature descriptors; and
mapping the first image into the second image based on matching pairs of blurred first image key points and blurred second image key points.
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Abstract
Example embodiments disclosed herein relate to feature based image registration. Feature based image registration determines correspondence between image features such as points, lines, and contours to align or register a reference or first image and a target or second image. The examples disclosed herein may be used in mobile devices such as cell phones, personal digital assistants, personal computers, cameras, and video recorders.
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Citations
25 Claims
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1. A method, comprising:
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applying a first grid to a blurred first image; computing a determinant of a Hessian matrix at predetermined points of the first grid on the blurred first image; determining low resolution feature points in the blurred first image based on the first grid; applying a second grid to the blurred first image, the second grid being finer than the first grid; computing a determinant of a Hessian matrix at predetermined points of the second grid on the blurred first image; determining high resolution feature points in the blurred first image based on the second grid; creating a first set of blurred first image key points; extracting a first feature descriptor for each of the blurred first image key points; applying the first grid to a blurred second image; computing a determinant of a Hessian matrix at predetermined points of the first grid on the blurred second image; determining low resolution feature points in the blurred second image based on the first grid; applying the second grid to the blurred second image; computing a determinant of a Hessian matrix at predetermined points of the second grid on the blurred second image; determining high resolution feature points in the blurred second image based on the second grid; creating a second set of blurred second image key points extracting a second feature descriptor for each of the blurred second image key points; selecting blurred first image key points and blurred second image key points for matching based on a measure of closeness of the first feature descriptors and the second feature descriptors; and mapping the first image into the second image based on matching pairs of blurred first image key points and blurred second image key points. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)
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15. A non-transitory computer-readable storage medium storing instructions, when executed by a processor, cause the processor to:
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apply a first grid to a blurred first image; compute a determinant of a Hessian matrix at predetermined points of the first grid on the blurred first image; determine low resolution feature points in the blurred first image based on the first grid; apply a second grid to at least portions of the blurred first image, the second grid being finer than the first grid; compute a determinant of a Hessian matrix at predetermined points of the second grid on the blurred first image; determine high resolution feature points in the blurred first image based on the second grid; create a first set of blurred first image key points; extract a first feature descriptor for each of the blurred first image key points; apply the first grid to a blurred second image; compute a determinant of a Hessian matrix at predetermined points of the first grid on the blurred second image; determine low resolution feature points in the blurred second image based on the first grid; apply the second grid to at least portions of the blurred second image; compute a determinant of a Hessian matrix at predetermined points of the second grid on the blurred second image; determine high resolution feature points in the blurred second image based on the second grid; create a second set of blurred second image key points extract a second feature descriptor for each of the blurred second image key points; select blurred first image key points and blurred second image key points for matching based on a measure of closeness of the first feature descriptors and the second feature descriptors; and map the first image into the second image based on matching pairs of blurred first image key points and blurred second image key points. - View Dependent Claims (16, 17, 18, 19, 20, 21, 22, 23, 24, 25)
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Specification