  Image stitching is to construct panoramic images with wider field of vision
(FOV) from some images captured from different viewing positions. To solve the
problem of fusion ghosting in the stitched image, seam-driven methods avoid the
misalignment area to fuse images by predicting the best seam. Currently, as
standard tools of the OpenCV library, dynamic programming (DP) and GraphCut
(GC) are still the only commonly used seam prediction methods despite the fact
that they were both proposed two decades ago. However, GC can get excellent
seam quality but poor real-time performance while DP method has good efficiency
but poor seam quality. In this paper, we propose a deep learning based seam
prediction method (DSeam) for the sake of high seam quality with high
efficiency. To overcome the difficulty of the seam description in network and
no GroundTruth for training we design a selective consistency loss combining
the seam shape constraint and seam quality constraint to supervise the network
learning. By the constraint of the selection of consistency loss, we implicitly
defined the mask boundaries as seams and transform seam prediction into mask
prediction. To our knowledge, the proposed DSeam is the first deep learning
based seam prediction method for image stitching. Extensive experimental
results well demonstrate the superior performance of our proposed Dseam method
which is 15 times faster than the classic GC seam prediction method in OpenCV
2.4.9 with similar seam quality.
