An Estimation of the Fundamental Mateix Using Hybrid Statistics
An Estimation of the Fundamental Mateix Using Hybrid Statistics
复制标题
使用混合统计对基本 Mateix 进行估计
DOI：
10.1109/vcip.2013.6706341
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发表时间：
2013
期刊：
影响因子：
--
通讯作者：
Ryo Okutani and Yoshimitsu Kuroki
中科院分区：
文献类型：
--
作者：
Hariyama M;Shimoda M.;Ryo Okutani and Yoshimitsu Kuroki
极线约束中的基本矩阵代表了不同观点的重要信息。可以使用七个以上的对应关键点来估计该矩阵。最大似然估计可以修正相应关键点坐标的误差，准确计算基础矩阵。基础矩阵的精度取决于对应关键点的精度；因此，相应关键点的准确提取起着重要作用。 SIFT（尺度不变特征变换）表示每个关键点的特征向量，它对几何变化和光度变化具有鲁棒性。该属性有助于寻找相应关键点的高度辨别力。然而，SIFT提取的对应关键点可能存在较大误差，例如对应关键点不匹配。这些对应的关键点影响基础矩阵的准确性。该方法不仅利用极线方程误差的统计量，而且利用关键点消除前后误差方差的比值来消除不匹配的对应关键点。实验结果表明，该方法比传统方法更准确地估计基本矩阵。
The fundamental matrix in epipolar constraint represents important information from different viewpoints. This matrix can be estimated using more than seven corresponding keypoints. The maximum-likelihood estimation can correct errors of coordinates of corresponding keypoints, and calculates the fundamental matrix accurately. The accuracy of the fundamental matrix depends on the accuracy of corresponding keypoints; therefore, exact extraction of the corresponding keypoints plays an important role. SIFT (Scale Invariant Feature Transform) represents a feature vector for each keypoint, which is robust against geometrical changes and photometric changes. This property contributes to a high level of discrimination for finding corresponding keypoints. However, SIFT may extract corresponding keypoints with large errors, such as mismatched corresponding keypoints. These corresponding keypoints affect the accuracy of the fundamental matrix. The proposed method eliminates the mismatched corresponding keypoints using not only the statistics of epipolar equation error but also the ratio of the variances of the error before and after the keypoints' elimination. Experimental results demonstrate that the proposed method estimates the fundamental matrix more accurately than conventional methods.