Is Multi-model Feature Matching Better for Endoscopic Motion Estimation?
Is Multi-model Feature Matching Better for Endoscopic Motion Estimation?
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DOI：
10.1007/978-3-319-13410-9_9
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发表时间：
2014-01-01
期刊：
影响因子：
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通讯作者：
Hager GD
中科院分区：
文献类型：
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作者：
Xiang X;Mirota D;Reiter A;Hager GD
摄像机运动估计是内窥镜可视化的标准但关键的步骤。它是由2D图像中检测到的特征的位置和对应关系的变化的影响。特征检测器和描述符各不相同，但最广泛使用的仍然是SIFT。实践者通常也采用其特征匹配策略，将内点定义为经过全局仿射变换的特征对。然而，对于内窥镜视频，我们很好奇是否更适合将特征聚类到多个组中。我们仍然可以在每个组中执行与SIFT中相同的转换。这种多模型的想法最近已经在多仿射工作中进行了研究，该工作在微创内窥镜图像上的重新投影误差方面优于Lowe的SIFT，该图像具有手动标记的SIFT特征的地面实况匹配。由于它们的区别在于匹配，估计运动的准确性增益归因于整体多仿射特征匹配算法。但是，更具体地，匹配准则和点搜索可以与SIFT中内置的那些相同。我们认为，真实的变化只是运动模型的验证。我们要么实施一个单一的全球运动模型或采用一组多个本地的。在本文中，我们调查如何敏感的估计运动的影响，在特征匹配假设的运动模型的数量。虽然灵敏度可以分析评估，我们提出了一个实证分析，在留一交叉验证设置，而不需要标签的地面真理匹配。然后，灵敏度由运动估计序列的方差来表征。我们提出了一系列的定量比较，如准确性和方差之间的多仿射运动模型和全局仿射模型。
Camera motion estimation is a standard yet critical step to endoscopic visualization. It is affected by the variation of locations and correspondences of features detected in 2D images. Feature detectors and descriptors vary, though one of the most widely used remains SIFT. Practitioners usually also adopt its feature matching strategy, which defines inliers as the feature pairs subjecting to a global affine transformation. However, for endoscopic videos, we are curious if it is more suitable to cluster features into multiple groups. We can still enforce the same transformation as in SIFT within each group. Such a multi-model idea has been recently examined in the Multi-Affine work, which outperforms Lowe’s SIFT in terms of re-projection error on minimally invasive endoscopic images with manually labelled ground-truth matches of SIFT features. Since their difference lies in matching, the accuracy gain of estimated motion is attributed to the holistic Multi-Affine feature matching algorithm. But, more concretely, the matching criterion and point searching can be the same as those built in SIFT. We argue that the real variation is only the motion model verification. We either enforce a single global motion model or employ a group of multiple local ones. In this paper, we investigate how sensitive the estimated motion is affected by the number of motion models assumed in feature matching. While the sensitivity can be analytically evaluated, we present an empirical analysis in a leaving-one-out cross validation setting without requiring labels of ground-truth matches. Then, the sensitivity is characterized by the variance of a sequence of motion estimates. We present a series of quantitative comparison such as accuracy and variance between Multi-Affine motion models and the global affine model.