利用点特征匹配实现视频稳定

Nithin Kumar Brahamadevara, GAE Satish Kumar, Purna Goud Palusa, Dinesh Bandaru
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引用次数: 0

摘要

一种视频防抖技术,既能保持拍摄对象的稳定,又能消除手抖。我们的网络拓扑结构特别用于同时稳定背景和前景,同时让用户有机会调整稳定重点。我们还提供了一个实时帧翘曲刚性移动最小二乘网格逼近。为了明确地推断出在全局刚性和局部柔性之间隐含平衡的刚性移动最小二乘翘曲,使用了一个线性卷积网络。我们的方法是全自动的,不需要用户准备或输入。使用视频稳定是至关重要的,在业余和专业拍摄。因此,有几种机械的、光学的和计算的解决方案。在静止图像摄影中,稳定也可用于拍摄长时间曝光的手持照片。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Utilizing the Point Feature Matching for Video Stabilization
A technique for video stabilization that maintains the subject steady while also eliminating hand shaking. Our network topology is especially made to stabilize both the background and the foreground simultaneously while giving the user the opportunity to adjust the stabilization emphasis. We additionally offer a real-time frame-warping stiff moving least squares grid approximation. To explicitly infer the stiff moving least squares warping, which implicitly balances between global rigidity and local flexibility, a linear convolutional network is utilised. Our method is fully automated and requires no user preparation or input. The use of video stabilization is crucial in both amateur and professional filming. As a result, there are several mechanical, optical, and computational solutions. Stabilization may be used to capture handheld photos with lengthy exposure durations in still image photography as well.
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