Seam-guided local alignment and stitching for large parallax images
Tianli Liao Chenyang Zhao Lei Li Heling Cao
Seam-cutting methods have been proven effective in the composition step of
image stitching, especially for images with parallax. However, the
effectiveness of seam-cutting usually depends on that images can be roughly
aligned such that there exists a local region where a plausible seam can be
found. For images with large parallax, current alignment methods often fall
short of expectations. In this paper, we propose a local alignment and
stitching method guided by seam quality evaluation. First, we use existing
image alignment and seam-cutting methods to calculate an initial seam and
evaluate the quality of pixels along the seam. Then, for pixels with low
qualities, we separate their enclosing patches in the aligned images and
locally align them by extracting modified dense correspondences via SIFT flow.
Finally, we composite the aligned patches via seam-cutting and merge them into
the original aligned result to generate the final mosaic. Experiments show that
compared with the state-of-the-art seam-cutting methods, our result is more
plausible and with fewer artifacts. The code will be available at
https://github.com/tlliao/Seam-guided-local-alignment.