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Motion Driven Tonal Stabilization

机译:运动驱动音调稳定

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This paper addresses the problem of tonal fluctuation in videos. Due to the automatic settings of consumer cameras, the colors of objects in image sequences might change over time. We propose here a fast and computationally light method to stabilize this tonal appearance, while remaining robust to motion and occlusions. To do so, a minimally viable color correction model is used, in conjunction with an effective estimation of dominant motion. The final solution is a temporally weighted correction, explicitly driven by the motion magnitude, both visually efficient and very fast, with potential to real time processing. Experimental results obtained on a variety of sequences outperform the current state of the art in terms of tonal stability, at a much reduced computational complexity.
机译:本文解决了视频中音调波动的问题。由于消费类相机的自动设置,图像序列中对象的颜色可能会随时间变化。我们在这里提出一种快速且计算轻便的方法来稳定这种色调外观,同时保持对运动和遮挡的鲁棒性。为此,结合了对主导运动的有效估计,使用了最小可行的色彩校正模型。最终的解决方案是时间加权校正,由运动幅度显式驱动,视觉有效且非常快,具有实时处理的潜力。就音调稳定性而言,在各种序列上获得的实验结果均优于现有技术,大大降低了计算复杂度。

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