Is it good in general to subtract background from a sequence of images for learning?
$begingroup$
Context:
I have a sequence of satellite images, indexed by time, so basically it's a video. Images were taken on top of a mountain, to capture a main cause that affects solar rays. (GHI in other words).
I thought, subtracting background (most shared features) will only keep clouds, which is the real variable.
Original Images
Masked Images
This would probably help the network, along side original images.
Technically, I used OpenCV following this method
The generation (or lets say image data augmentation) in this case, didn't give higher score, but barely equal or a little lower.
The reason could be, that the codec I used generated images of lower quality. But also, the first images in the sequence, logically were not augmented well, as the algorithm knows the background after some iterations.
Is it good practice to omit background ?
Waiting for some hints, I am actively trying to improve data augmentation, and re-test again, doing my exercise!
cnn data-augmentation image-preprocessing
asked yesterday
bacloud14bacloud14
579
$endgroup$
add a comment |
$begingroup$
Context:
I have a sequence of satellite images, indexed by time, so basically it's a video. Images were taken on top of a mountain, to capture a main cause that affects solar rays. (GHI in other words).
I thought, subtracting background (most shared features) will only keep clouds, which is the real variable.
Original Images
Masked Images
This would probably help the network, along side original images.
Technically, I used OpenCV following this method
The generation (or lets say image data augmentation) in this case, didn't give higher score, but barely equal or a little lower.
The reason could be, that the codec I used generated images of lower quality. But also, the first images in the sequence, logically were not augmented well, as the algorithm knows the background after some iterations.
Is it good practice to omit background ?
Waiting for some hints, I am actively trying to improve data augmentation, and re-test again, doing my exercise!
cnn data-augmentation image-preprocessing
asked yesterday
bacloud14bacloud14
579
$endgroup$
add a comment |
$begingroup$
Context:
I have a sequence of satellite images, indexed by time, so basically it's a video. Images were taken on top of a mountain, to capture a main cause that affects solar rays. (GHI in other words).
I thought, subtracting background (most shared features) will only keep clouds, which is the real variable.
Original Images
Masked Images
This would probably help the network, along side original images.
Technically, I used OpenCV following this method
The generation (or lets say image data augmentation) in this case, didn't give higher score, but barely equal or a little lower.
The reason could be, that the codec I used generated images of lower quality. But also, the first images in the sequence, logically were not augmented well, as the algorithm knows the background after some iterations.
Is it good practice to omit background ?
Waiting for some hints, I am actively trying to improve data augmentation, and re-test again, doing my exercise!
cnn data-augmentation image-preprocessing
asked yesterday
bacloud14bacloud14
579
$endgroup$
Context:
I have a sequence of satellite images, indexed by time, so basically it's a video. Images were taken on top of a mountain, to capture a main cause that affects solar rays. (GHI in other words).
I thought, subtracting background (most shared features) will only keep clouds, which is the real variable.
Original Images
Masked Images
This would probably help the network, along side original images.
Technically, I used OpenCV following this method
The generation (or lets say image data augmentation) in this case, didn't give higher score, but barely equal or a little lower.
The reason could be, that the codec I used generated images of lower quality. But also, the first images in the sequence, logically were not augmented well, as the algorithm knows the background after some iterations.
Is it good practice to omit background ?
Waiting for some hints, I am actively trying to improve data augmentation, and re-test again, doing my exercise!
cnn data-augmentation image-preprocessing
cnn data-augmentation image-preprocessing
asked yesterday
bacloud14bacloud14
579
asked yesterday
bacloud14bacloud14
579
edited yesterday
bacloud14
asked yesterday
bacloud14bacloud14
579
asked yesterday
bacloud14bacloud14
579
asked yesterday
bacloud14bacloud14
579
579
add a comment |
add a comment |
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