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首页> 外文会议>International Conference on Computer Vision Theory and Applications >Improved Confidence Measures for Variational Optical Flow
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Improved Confidence Measures for Variational Optical Flow

机译：改进变分光流的置信度量

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In the last decades variational optical flow algorithms have been intensively studied by the computer vision community. However, relatively few effort has been made to obtain robust confidence measures for the estimated flow field. As many applications do not require the whole flow field, it would be helpful to identify the parts of the field where the flow is most accurate. We propse a confidence measure based on the energy functional that is minimized during the optical flow calculation and analyze the performance of different data terms. For evaluation, 7 datasets of the Middlebury benchmark are used. The results show that the accuracy of the flow field can be improved by 53.3% if points are selected according to the proposed confidence measure. The suggested method leads to an improvement of 35.2% compared to classical confidence measures.
机译：在过去的几十年中，计算机视觉界已经集中研究了变分光流算法。然而，已经采取了相对较少的努力，以获得估计的流场的强大置信度措施。由于许多应用程序不需要整个流字段，识别流量最准确的字段的部分会有所帮助。我们基于在光学流量计算期间最小化的能量功能来提出置信度量，并分析不同数据项的性能。对于评估，使用了7个中跨度基准的数据集。结果表明，如果根据所提出的置信度量选择点，则可以提高流场的准确性53.3％。与古典置信度量相比，建议的方法导致35.2％的提高。

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