Camera localization using mutual information-based multiplane tracking
Camera localization using mutual information-based multiplane tracking
复制标题
使用基于相互信息的多平面跟踪进行相机定位
DOI：
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发表时间：
2013
期刊：
影响因子：
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通讯作者：
Éric Marchand
中科院分区：
文献类型：
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作者：
Bertrand Delabarre;Éric Marchand
本文研究了利用三维信息提供时空相干性，对场景扰动具有鲁棒性的密集视觉跟踪。该方法基于一种分段平面场景视觉跟踪算法，其目的是通过考虑场景中平面的相对位置，估计刚性三维变换的参数，从而最小化观测图像与参考模板之间的误差。这种方法的主要缺点是用于执行最小化的配准函数（差的平方和），因为它对场景变化的鲁棒性非常差。在本文中，跟踪过程适应于考虑两个更复杂的配准函数。首先是条件方差之和。由于该算法对全局光照变化具有不变性，因此在保持较低计算复杂度的同时具有较强的鲁棒性。然后，考虑互信息。在这种情况下，复杂性更大，但对非全局照明变化，镜面或遮挡的鲁棒性也更强。在描述了所提出的方法之后，在不同的照明条件下对不同的场景进行了测试，以评估各自的效率。
This paper deals with dense visual tracking robust towards scene perturbations using 3D information to provide a space-time coherency. The proposed method is based on an piecewise-planar scenes visual tracking algorithm which aims to minimize an error between an observed image and a reference template by estimating the parameters of a rigid 3D transformation taking into acount the relative positions of the planes in the scene. The major drawback of this approch stems from the registration function used to perform the minimization (the sum of squared differences) as it is very poorly robust towards scene variations. In this paper, the tracking process is adapted to take into account two more complex registration functions. First, the sum of conditional variance. Since it is invariant to global illumination variations, the proposed algorithm is robust with relation to those conditions whilst keeping a low computation complexity. Then, the mutual information is considered. In that case the complexity is greater but so is the robustness towards non global illumination variations, specularities or occlusions. The proposed approaches, after being described, are tested on different scenes under varying illumination conditions to assess their respective efficiency.
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DOI：
10.1007/978-1-4939-7647-8_1
发表时间：
2018
期刊：
Neuromethods
影响因子：
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作者：
Joshi,AnandA
通讯作者：
Joshi,AnandA