  The seam-driven approach has been proven fairly effective for
parallax-tolerant image stitching, whose strategy is to search for an invisible
seam from finite representative hypotheses of local alignment. In this paper,
we propose a graph-based hypothesis generation and a seam-guided local
alignment for improving the effectiveness and the efficiency of the seam-driven
approach. The experiment demonstrates the significant reduction of number of
hypotheses and the improved quality of naturalness of final stitching results,
comparing to the state-of-the-art method SEAGULL.
