Title: Perception-based energy functions in seam-cutting,
Abstract: Image stitching is challenging in consumer-level photography, due to
alignment difficulties in unconstrained shooting environment. Recent studies
show that seam-cutting approaches can effectively relieve artifacts generated
by local misalignment. Normally, seam-cutting is described in terms of energy
minimization, however, few of existing methods consider human perception in
their energy functions, which sometimes causes that a seam with minimum energy
is not most invisible in the overlapping region. In this paper, we propose a
novel perception-based energy function in the seam-cutting framework, which
considers the nonlinearity and the nonuniformity of human perception in energy
minimization. Our perception-based approach adopts a sigmoid metric to
characterize the perception of color discrimination, and a saliency weight to
simulate that human eyes incline to pay more attention to salient objects. In
addition, our seam-cutting composition can be easily implemented into other
stitching pipelines. Experiments show that our method outperforms the
seam-cutting method of the normal energy function, and a user study
demonstrates that our composed results are more consistent with human
perception.