极空间与最优采样策略

J. Monaco, A. Bovik, L. Cormack
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引用次数: 1

摘要

如果没有精确的校准信息,就像主动双目视觉系统经常出现的情况一样,极线的确定就站不住脚了。然而,即使没有即时的几何知识,对相应点的搜索也可以限制在称为极外空间的区域。对于一幅图像中的每个点,我们将另一幅图像中相应的极空间定义为所有可能的系统几何上所有相关的极线的并。极外空间消除了校准的需要,但代价是增加了搜索区域。缓解这种增加的一种方法是应用空间变采样或注视点策略。虽然这种策略在立体视觉任务中的应用并不新鲜,但很少有专门为立体视觉任务量身定制的注视方案。在本文中,我们推导了一些定理的基础,这些定理提供了一种方法来获得给定的一组极空间的最优抽样格式。最优采样方案被定义为使每极空间的平均面积最小的策略。
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Epipolar Spaces and Optimal Sampling Strategies
If precise calibration information is unavailable, as is often the case for active binocular vision systems, the determination of epipolar lines becomes untenable. Yet, even without instantaneous knowledge of the geometry, the search for corresponding points can be restricted to areas called epipolar spaces. For each point in one image, we define the corresponding epipolar space in the other image as the union of all associated epipolar lines over all possible system geometries. Epipolar spaces eliminate the need for calibration at the cost of an increased search region. One approach to mitigate this increase is the application of a space variant sampling or foveation strategy. While the application of such strategies to stereo vision tasks is not new, only rarely has a foveation scheme been specifically tailored for a stereo vision task. In this paper we derive a foundation of theorems that provide a means for obtaining optimal sampling schemes for a given set of epipolar spaces. An optimal sampling scheme is defined as a strategy that minimizes the average area per epipolar space.
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