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摘要: 可靠对应点的建立是计算机视觉中的一个基本问题, 它是许多已有算法的前题假设. 本文提出了对应流形的概念, 并给出了通过学习对应流形的视图剔除错误匹配点的学习方法. 该方法不依赖于被估计的参数模型, 克服了传统方法计算效率随着错误匹配比例的增大或被估参数个数的增长而急剧下降的缺点; 同时弥补了错误匹配的剔除与被估模型的选择及其参数估计相耦合的不足. 实验结果表明该方法能有效地剔除错误匹配点, 验证了所给模型的适用性与合理性. 为可靠对应关系的建立提供一种新的高效的方法.Abstract: Finding reliable corresponding points between two images is a fundamental problem in computer vision, especially with the development of L∞ vision framework. This paper introduce the correspondence manifold and propose a novel scheme to reject outliers by learning upward views of the manifold. The proposed scheme is independent of the parametric model to be estimated and overcomes the following limitations of the available methods in the published works: efficiency sharply goes down with the increase of outlier percentage and the number of the estimated model parameters; outlier rejecting is coupled with model selection and model estimation. Experiments on real image pairs show the excellent performance of our proposed scheme.
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Key words:
- Computer vision /
- point correspondence /
- outlier rejection /
- diagnostic
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