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Misalignment-eliminated warping image stitching method with grid-based motion statistics matching
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AI
DOI：10.1007/s11042-022-12064-2.png)
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Abstract
En 中文
Aligning images is one of the main goals of image stitching. Limited by matching accuracy and deformability, the results of current mainstream stitching approaches in large parallax scenes usually contain obvious stitching errors. A misalignment-eliminated warping image stitching based on grid-based motion statistics (GMS) matching is proposed. A matching approach from coarse-to-fine composed of oriented FAST and rotated BRIEF (ORB) and GMS is integrated to provide more accurate and higher number of inliers for subsequent warping. The local homography with global similarity transformation constraint is used to warp the images to achieve initial image alignment. For the projection biases after local warping, a post-processing step based on thin plate spline (TPS) is proposed for further correction. Both qualitative and quantitative comparisons in experiments of challenging cases show that this method can accurately align images while maintaining a natural look at the same time.
Keywords:
Image stitching
Image alignment
Local warping
Image registration
Journal
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3
Papers:
1.9W
Citations:
3.2W