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 | 1 | initial version |
wait:
"The images are all presented" -- that's plural ? different images ? are you trying to do some arbitrary "object detection" like this ? then you're probably on the wrong bus (it won't ever work ! feature matching is for finding parts of a known scene only)
| | 2 | No.2 Revision |
wait:
"The images are all presented" -- that's plural ? different images ?
are you trying to do some arbitrary "object detection" like this ?
then you're probably on the wrong bus (it won't ever work ! feature matching is for finding parts of a known scene only)
| | 3 | No.3 Revision |
wait:
"The images are all presented" -- that's plural ? different images ?
are you trying to do some arbitrary "object detection" detection" like this ?
then you're probably on the wrong bus (it won't ever work ! feature matching is for finding parts of a known scene only)
| | 4 | No.4 Revision |
wait:
"The images are all presented" "out of a local database of 200 pictures" -- that's plural ? different images ?
are you trying to do some arbitrary "object detection" like this ?
then you're most probably on the wrong bus bus.
(it won't ever work ! feature matching is for finding parts of a known scene only)only, it can never tell you, if it's NOT there, no matter, which features you choose, it's a constraint of the matching algorithms)
| | 5 | No.5 Revision |
wait:
"out of a local database of 200 pictures" --
are you trying to do some arbitrary "object detection" like this ?
then you're most probably on the wrong bus.
(it won't ever work ! feature matching is for finding parts of a knownsingle, known scene scene only, it can never tell you, if it's NOT there, no matter, which features you choose, it's a constraint of the matching algorithms)algorithms. it's main application is finding a homography between a part of the scene and the whole, NOT detection.)
| | 6 | No.6 Revision |
wait:
"out of a local database of 200 pictures" --
are you trying to do some arbitrary "object detection" like this ?
then you're most probably on the wrong bus.
(it won't ever work ! feature matching is for finding parts of a single, known scene only, it can never tell you, if it's NOT there, no matter, which features you choose, it's a constraint of the matching algorithms. it's main application is finding a homography between a part of the scene and the whole, NOT detection.)
forget that idea immediately, please, it's a waste of time. instead do some research on depp learning, SSD nets, YOLO, etc. those can be re-trained with quite sparse data on your special problem set, and you can run the inference later from opencv again.
| | 7 | No.7 Revision |
wait:
"out of a local database of 200 pictures" --
are you trying to do some arbitrary "object detection" like this ?
then you're most probably on the wrong bus.
(it won't ever work ! feature matching is for finding parts of a single, known scene only, it can never tell you, if it's NOT there, no matter, which features you choose, it's a constraint of the matching algorithms. it's main application is finding a homography between a part of the scene and the whole, NOT detection.)
are you also aware, that for 200 db images you would need 200 detectAndCompare() calls & matching ?
forget that idea immediately, please, it's a waste of time. instead do some research on depp learning, SSD nets, YOLO, etc. those can be re-trained with quite sparse data on your special problem set, and you can run the inference later from opencv again.
