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Found 8 papers, 1 papers with code
Seam-guided local alignment and stitching for large parallax images
However, the effectiveness of seam-cutting usually depends on that images can be roughly aligned such that there exists a local region where a plausible seam can be found.
Natural Image Stitching Using Depth Maps
Then, we draw a triangulation of the target image and estimate multiple local homographies, one per triangle, based on the locations of their vertices, the rectified depth values and the epipolar geometry.
Learning-based Natural Geometric Matching with Homography Prior
Then, we parametrize homography transformation with 9 parameters in full connected layer of our network, to better characterize large viewpoint variations compared with affine transformation.
Coarse-to-fine Seam Estimation for Image Stitching
For pixels on the seam, we develop a patch-point evaluation algorithm concentrating more on the correlation and variation of them.
Graph-based Hypothesis Generation for Parallax-tolerant Image Stitching
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.
Ratio-Preserving Half-Cylindrical Warps for Natural Image Stitching
The pixel selection strategy then samples the points in horizontal and reconstructs the image via interpolation to further reduce horizontal distortion by maintaining the ratio as similarity.
Single-Perspective Warps in Natural Image Stitching
Results of image stitching can be perceptually divided into single-perspective and multiple-perspective.
Perception-based energy functions in seam-cutting
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.
