图像拼接
单应性
重影
歧管(流体力学)
歧管对齐
计算机科学
失真(音乐)
人工智能
计算机视觉
特征(语言学)
数学
非线性降维
降维
计算机网络
带宽(计算)
放大器
投射试验
哲学
工程类
统计
机械工程
射影空间
语言学
标识
DOI:10.1109/tmm.2022.3161839
摘要
Image stitching usually relies on spatial transformations to perform the overlap alignment and distortion mitigation. This paper presents a manifold optimization method to seek these transformations. The purpose is not to present a new formulation of image stitching, as the proposed method uses common transformations such as homography to align feature correspondences in the overlap and similarity transformations to preserve the shape. Instead, the proposed method is based on a new treatment of these transformations as elements of a prescribed matrix manifold. Its advantage lies in its more effective and efficient optimization in the manifold domain. Specifically, spatially varying homographies are computed by an efficient second-order minimization (ESM) of the geometric error of aligning feature correspondences, but with their intrinsic manifold parameterization. To mitigate the distortion, the interpolation between homography and similarity transformation is performed on a general matrix manifold. These on-manifold operations improve the stitching quality with fewer ghosting and distortion artifacts. The experiments show our manifold optimization for image stitching outperforms other methods.
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