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Hi,

Internally, in the stitching module it's assumed that an input image is a photo from camera (it's used for camera parameters estimation). The assumption is false for _pano1.jpg as it's not a photo, but a few transformed images stitched.

Too low match confidence means that all (or almost) matches will be classified as good ones. But when all matches are good the method decides that the images are too close and doesn't stitch them. It's reasonable, for instance, when a camera is fixed and there is small moving object. In such case almost all matches are good, but it's better not to stitch two images, and take only one as output.

Hi,

Internally, in the stitching module it's assumed that an input image is a photo from camera (it's used for camera parameters estimation). The assumption is false for _pano1.jpg as it's not a photo, but a few transformed images stitched.

Too low match confidence means that all (or almost) matches will be classified as good ones. But when all matches are good the method decides that the images are too close similar and doesn't stitch them. It's reasonable, for instance, when a camera is fixed and there is small moving object. In such case almost all matches are good, but it's better not to stitch two images, and take only one as output.

Hi,

Internally, in the stitching module it's assumed that an input image is a photo from camera (it's used for camera parameters estimation). The assumption is false for _pano1.jpg as it's not a photo, but a few transformed images stitched.

Too low match confidence means that all (or almost) matches will be classified as good ones. But when all matches are good the method decides that the images are too similar and doesn't stitch them. It's reasonable, for instance, when a camera is fixed and there is a small moving object. In such case almost all matches are good, but it's better not to stitch two images, and take only one as output.