choosing SIFT keypoints that will be least affected when image undergoes affine transformation
Consider the following situation.
I have a 512x512 grey scale image of a fingerprint with 3k SIFT keypoints.
When I rotate this about its centre in 2D plane then I get a bigger square image.
Assume white background in large rotated image for newly created areas.
If I do brute force matching by selecting a keypoint in original image and comparing (L2 distance) it with all keypoints in rotated image then some keypoints (call them 'good') in original image undergo very less change (approximately 50%) .
My purpose is to create a small database for image using 'good' keypoints mentioned above.
How can I identify 'good' keypoints ?
Currently I am choosing keypoints with high response .
Is that a right approach ?
Thanks.
sift keypoint
asked Nov 24 '18 at 10:52
user1371666user1371666
7612
add a comment |
Consider the following situation.
I have a 512x512 grey scale image of a fingerprint with 3k SIFT keypoints.
When I rotate this about its centre in 2D plane then I get a bigger square image.
Assume white background in large rotated image for newly created areas.
If I do brute force matching by selecting a keypoint in original image and comparing (L2 distance) it with all keypoints in rotated image then some keypoints (call them 'good') in original image undergo very less change (approximately 50%) .
My purpose is to create a small database for image using 'good' keypoints mentioned above.
How can I identify 'good' keypoints ?
Currently I am choosing keypoints with high response .
Is that a right approach ?
Thanks.
sift keypoint
asked Nov 24 '18 at 10:52
user1371666user1371666
7612
add a comment |
Consider the following situation.
I have a 512x512 grey scale image of a fingerprint with 3k SIFT keypoints.
When I rotate this about its centre in 2D plane then I get a bigger square image.
Assume white background in large rotated image for newly created areas.
If I do brute force matching by selecting a keypoint in original image and comparing (L2 distance) it with all keypoints in rotated image then some keypoints (call them 'good') in original image undergo very less change (approximately 50%) .
My purpose is to create a small database for image using 'good' keypoints mentioned above.
How can I identify 'good' keypoints ?
Currently I am choosing keypoints with high response .
Is that a right approach ?
Thanks.
sift keypoint
asked Nov 24 '18 at 10:52
user1371666user1371666
7612
Consider the following situation.
I have a 512x512 grey scale image of a fingerprint with 3k SIFT keypoints.
When I rotate this about its centre in 2D plane then I get a bigger square image.
Assume white background in large rotated image for newly created areas.
If I do brute force matching by selecting a keypoint in original image and comparing (L2 distance) it with all keypoints in rotated image then some keypoints (call them 'good') in original image undergo very less change (approximately 50%) .
My purpose is to create a small database for image using 'good' keypoints mentioned above.
How can I identify 'good' keypoints ?
Currently I am choosing keypoints with high response .
Is that a right approach ?
Thanks.
sift keypoint
sift keypoint
asked Nov 24 '18 at 10:52
user1371666user1371666
7612
asked Nov 24 '18 at 10:52
user1371666user1371666
7612
asked Nov 24 '18 at 10:52
user1371666user1371666
7612
asked Nov 24 '18 at 10:52
user1371666user1371666
7612
asked Nov 24 '18 at 10:52
user1371666user1371666
7612
7612
add a comment |
add a comment |
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