Perception-based energy functions in seam-cutting

TL;DR

This paper introduces a perception-based energy function for seam-cutting in image stitching, incorporating human perception factors like color discrimination and saliency to produce more visually seamless results.

Contribution

It proposes a novel perception-aware energy function that improves seam invisibility by modeling human perception in the seam-cutting process.

Findings

Outperforms traditional energy functions in seam-cutting quality.

Results are more consistent with human perception according to user studies.

Method integrates easily into existing stitching pipelines.

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.