Locally Aligned Image Stitching Based on Multi-Feature and Super-Pixel Segmentation With Plane Protection

The number and accuracy of image feature matching directly affect the accuracy of image warping, which is gaining widespread attention in image stitching. The alignment accuracy is gradually improved from a single plane to a regular multi-plane warping model. To further improve the accuracy of image alignment, this paper proposes a multi-plane alignment method based on superpixel segmentation, which preserves the integrity of local planes as much as possible to reduce ghosting. Recent warps prove that line features provide strong correspondences, especially in low-textured cases. Moreover, image segmentation methods such as superpixel segmentation have the function of protecting the object's integrity. On the one hand, our approach is based on GMS matching and introduces superpixel segmentation to refine the matching in the feature matching stage. The homography combines line features to enrich as many matching points as possible. On the other hand, to solve the problem of misalignment caused by local warping, the proposed method makes full use of the characteristics of superpixels to perform irregular plane segmentation to avoid the traditional rectangular segmentation method from dividing different planes into the same grid. Experimental results demonstrate that the proposed method outperforms some state-of-the-art warps from both qualitative and quantitative aspects, including the as-projective-as-possible warp (APAP), the as-natural-as-possible warp (ANAP), the global similarity prior (GSP), etc.

Automated summary

The accuracy of image warping is crucial for image stitching, and this paper introduces a multi-plane alignment method based on superpixel segmentation and line features to improve matching accuracy. This method outperforms some state-of-the-art warps in both qualitative and quantitative aspects, demonstrating its effectiveness in image alignment.