How to perform image registration with an alpha channel
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1
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I have two images and would like to perform feature detection on both and match these features. My problem is that the second image is a section of the first image with missing pixels. These missing pixels cause a strong discontinuity in the pixel intensity causing the feature detectors to place all features at this boundary as such:
Because of this the feature matching program fails since (i think) the descriptor of these features contain the missing pixel intensities which don't exist in the original image. As such i would like the feature detector to exclude these features and instead search within the 'valid' pixel regions. Does anyone have an idea ?
Else how, maybe using pattern matching on the pixel intensity could be a strong alternative but i can't find an efficient implementation for this (especially considering that the two images may be rotated with respect to one another).
[EDIT] Here are the two images:
matlab image-processing pattern-matching feature-detection image-registration
asked yesterday
Guillaume
155
|
show 1 more comment
up vote
1
down vote
favorite
I have two images and would like to perform feature detection on both and match these features. My problem is that the second image is a section of the first image with missing pixels. These missing pixels cause a strong discontinuity in the pixel intensity causing the feature detectors to place all features at this boundary as such:
Because of this the feature matching program fails since (i think) the descriptor of these features contain the missing pixel intensities which don't exist in the original image. As such i would like the feature detector to exclude these features and instead search within the 'valid' pixel regions. Does anyone have an idea ?
Else how, maybe using pattern matching on the pixel intensity could be a strong alternative but i can't find an efficient implementation for this (especially considering that the two images may be rotated with respect to one another).
[EDIT] Here are the two images:
matlab image-processing pattern-matching feature-detection image-registration
asked yesterday
Guillaume
155
2
Do you have the 2 starting images as well please?
– Mark Setchell
yesterday
I just added them in an edit.
– Guillaume
yesterday
You can't fool me that easily! That's one image with no transparent pixels...
– Mark Setchell
yesterday
I tried... here are the actual images
– Guillaume
yesterday
What do you mean by "feature matching program". Maybe you can customize it to reject the features on the boundary and not the weaker ones in the valid area.
– Knipser
yesterday
|
show 1 more comment
up vote
1
down vote
favorite
up vote
1
down vote
favorite
I have two images and would like to perform feature detection on both and match these features. My problem is that the second image is a section of the first image with missing pixels. These missing pixels cause a strong discontinuity in the pixel intensity causing the feature detectors to place all features at this boundary as such:
Because of this the feature matching program fails since (i think) the descriptor of these features contain the missing pixel intensities which don't exist in the original image. As such i would like the feature detector to exclude these features and instead search within the 'valid' pixel regions. Does anyone have an idea ?
Else how, maybe using pattern matching on the pixel intensity could be a strong alternative but i can't find an efficient implementation for this (especially considering that the two images may be rotated with respect to one another).
[EDIT] Here are the two images:
matlab image-processing pattern-matching feature-detection image-registration
asked yesterday
Guillaume
155
I have two images and would like to perform feature detection on both and match these features. My problem is that the second image is a section of the first image with missing pixels. These missing pixels cause a strong discontinuity in the pixel intensity causing the feature detectors to place all features at this boundary as such:
Because of this the feature matching program fails since (i think) the descriptor of these features contain the missing pixel intensities which don't exist in the original image. As such i would like the feature detector to exclude these features and instead search within the 'valid' pixel regions. Does anyone have an idea ?
Else how, maybe using pattern matching on the pixel intensity could be a strong alternative but i can't find an efficient implementation for this (especially considering that the two images may be rotated with respect to one another).
[EDIT] Here are the two images:
matlab image-processing pattern-matching feature-detection image-registration
matlab image-processing pattern-matching feature-detection image-registration
asked yesterday
Guillaume
155
asked yesterday
Guillaume
155
edited yesterday
asked yesterday
Guillaume
155
asked yesterday
Guillaume
155
asked yesterday
Guillaume
155
155
2
Do you have the 2 starting images as well please?
– Mark Setchell
yesterday
I just added them in an edit.
– Guillaume
yesterday
You can't fool me that easily! That's one image with no transparent pixels...
– Mark Setchell
yesterday
I tried... here are the actual images
– Guillaume
yesterday
What do you mean by "feature matching program". Maybe you can customize it to reject the features on the boundary and not the weaker ones in the valid area.
– Knipser
yesterday
|
show 1 more comment
2
Do you have the 2 starting images as well please?
– Mark Setchell
yesterday
I just added them in an edit.
– Guillaume
yesterday
You can't fool me that easily! That's one image with no transparent pixels...
– Mark Setchell
yesterday
I tried... here are the actual images
– Guillaume
yesterday
What do you mean by "feature matching program". Maybe you can customize it to reject the features on the boundary and not the weaker ones in the valid area.
– Knipser
yesterday
2
2
Do you have the 2 starting images as well please?
– Mark Setchell
yesterday
Do you have the 2 starting images as well please?
– Mark Setchell
yesterday
I just added them in an edit.
– Guillaume
yesterday
I just added them in an edit.
– Guillaume
yesterday
You can't fool me that easily! That's one image with no transparent pixels...
– Mark Setchell
yesterday
You can't fool me that easily! That's one image with no transparent pixels...
– Mark Setchell
yesterday
I tried... here are the actual images
– Guillaume
yesterday
I tried... here are the actual images
– Guillaume
yesterday
What do you mean by "feature matching program". Maybe you can customize it to reject the features on the boundary and not the weaker ones in the valid area.
– Knipser
yesterday
What do you mean by "feature matching program". Maybe you can customize it to reject the features on the boundary and not the weaker ones in the valid area.
– Knipser
yesterday
|
show 1 more comment
1 Answer
1
active
oldest
votes
up vote
1
down vote
If you slide the "holey" image over the solid one, and difference them, they will be aligned when you have the maximum number of black pixels. Watch for the magenta diagonal to disappear.
answered 18 hours ago
Mark Setchell
83.9k569169
Thank you for your response. I like the idea, sounds very practical in this scenario as the reference image is of the same height which is usually not the case... Additionally I would need my algorithm to be invariant to rotation. I will try to implement your idea on a non-ideal case (where the height and relative rotation are different) but i fear the efficiency and computation time will take a hit.
– Guillaume
14 hours ago
add a comment |
1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
up vote
1
down vote
If you slide the "holey" image over the solid one, and difference them, they will be aligned when you have the maximum number of black pixels. Watch for the magenta diagonal to disappear.
answered 18 hours ago
Mark Setchell
83.9k569169
Thank you for your response. I like the idea, sounds very practical in this scenario as the reference image is of the same height which is usually not the case... Additionally I would need my algorithm to be invariant to rotation. I will try to implement your idea on a non-ideal case (where the height and relative rotation are different) but i fear the efficiency and computation time will take a hit.
– Guillaume
14 hours ago
add a comment |
up vote
1
down vote
If you slide the "holey" image over the solid one, and difference them, they will be aligned when you have the maximum number of black pixels. Watch for the magenta diagonal to disappear.
answered 18 hours ago
Mark Setchell
83.9k569169
Thank you for your response. I like the idea, sounds very practical in this scenario as the reference image is of the same height which is usually not the case... Additionally I would need my algorithm to be invariant to rotation. I will try to implement your idea on a non-ideal case (where the height and relative rotation are different) but i fear the efficiency and computation time will take a hit.
– Guillaume
14 hours ago
add a comment |
up vote
1
down vote
up vote
1
down vote
If you slide the "holey" image over the solid one, and difference them, they will be aligned when you have the maximum number of black pixels. Watch for the magenta diagonal to disappear.
answered 18 hours ago
Mark Setchell
83.9k569169
If you slide the "holey" image over the solid one, and difference them, they will be aligned when you have the maximum number of black pixels. Watch for the magenta diagonal to disappear.
answered 18 hours ago
Mark Setchell
83.9k569169
answered 18 hours ago
Mark Setchell
83.9k569169
answered 18 hours ago
Mark Setchell
83.9k569169
answered 18 hours ago
Mark Setchell
83.9k569169
83.9k569169
Thank you for your response. I like the idea, sounds very practical in this scenario as the reference image is of the same height which is usually not the case... Additionally I would need my algorithm to be invariant to rotation. I will try to implement your idea on a non-ideal case (where the height and relative rotation are different) but i fear the efficiency and computation time will take a hit.
– Guillaume
14 hours ago
add a comment |
Thank you for your response. I like the idea, sounds very practical in this scenario as the reference image is of the same height which is usually not the case... Additionally I would need my algorithm to be invariant to rotation. I will try to implement your idea on a non-ideal case (where the height and relative rotation are different) but i fear the efficiency and computation time will take a hit.
– Guillaume
14 hours ago
Thank you for your response. I like the idea, sounds very practical in this scenario as the reference image is of the same height which is usually not the case... Additionally I would need my algorithm to be invariant to rotation. I will try to implement your idea on a non-ideal case (where the height and relative rotation are different) but i fear the efficiency and computation time will take a hit.
– Guillaume
14 hours ago
Thank you for your response. I like the idea, sounds very practical in this scenario as the reference image is of the same height which is usually not the case... Additionally I would need my algorithm to be invariant to rotation. I will try to implement your idea on a non-ideal case (where the height and relative rotation are different) but i fear the efficiency and computation time will take a hit.
– Guillaume
14 hours ago
add a comment |
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2
Do you have the 2 starting images as well please?
– Mark Setchell
yesterday
I just added them in an edit.
– Guillaume
yesterday
You can't fool me that easily! That's one image with no transparent pixels...
– Mark Setchell
yesterday
I tried... here are the actual images
– Guillaume
yesterday
What do you mean by "feature matching program". Maybe you can customize it to reject the features on the boundary and not the weaker ones in the valid area.
– Knipser
yesterday