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Shape From Inconsistent Silhouette

机译：轮廓不一致的形状

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摘要

Shape from silhouette (SfS) is the general term used to refer to the techniques that obtain a volume estimate from a set of binary images. In a first step, a number of images are taken from different positions around the scene of interest. Later, each image is segmented to produce binary masks, also called silhouettes, to delimit the objects of interest. Finally, the volume estimate is obtained as the maximal one which yields the silhouettes. The set of silhouettes is usually considered to be consistent which means that there exists at least one volume which completely explains them. However, silhouettes are normally inconsistent due to inaccurate calibration or erroneous silhouette extraction techniques. In spite of that, SfS techniques reconstruct only that part of the volume which projects consistently in all the silhouettes, leaving the rest unreconstructed. In this paper, we extend the idea of SfS to be used with sets of inconsistent silhouettes. We propose a fast technique for estimating that part of the volume which projects inconsistently and propose a criteria for classifying it by minimizing the probability of miss-classification taking into account the 2D error detection probabilities of the silhouettes. A number of theoretical and empirical results are given, showing that the proposed method reduces the reconstruction error.
机译：轮廓形状（SfS）是用于指代从一组二进制图像中获取体积估计值的技术的总称。第一步，从感兴趣场景周围的不同位置拍摄大量图像。随后，将每个图像进行分割以生成二进制蒙版（也称为轮廓），以划定感兴趣的对象。最终，获得的体积估算值是产生轮廓的最大值。通常认为这组轮廓是一致的，这意味着至少存在一个完整解释它们的体积。但是，由于校准不正确或轮廓提取技术错误，轮廓通常不一致。尽管如此，SfS技术仅重建在所有轮廓中一致投影的那部分体积，而其余部分则未重建。在本文中，我们扩展了SfS的概念，以用于一组不一致的轮廓。我们提出了一种快速的技术来估计投影不一致的那部分体积，并提出了一种标准，通过考虑轮廓的2D错误检测概率，通过最小化未分类的概率来对其进行分类。给出了许多理论和经验结果，表明该方法降低了重建误差。

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