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Three-dimensional object recovery from two-dimensional images: a new a

机译:从二维图像中恢复三维物体:一个新的

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Abstract: This paper considers the work done by Thomas Marill in a series of papers on the recognition of two-dimensional wire- frame figures as 3D objects without the use of models. Marill discovered that if one minimizes the standard deviation of the angles found at each vertex of the figure, the likelihood of the computer interpretation of the figure matching the human interpretation is much higher than might be expected a priori. Here it is observed that the human mind's tendency to simplify inputs and find patterns even where there are none might be at least partly responsible for the observed phenomenon. It is conjectured that if this is indeed the case, it should be possible to get similar behavior from minimizing the standard deviation of other features. In particular, segment length presents itself as being an excellent choice of a test feature - it is very different from angles and is less computationally intensive. Thus another approach is considered: minimum standard deviation of segment magnitudes is explored in lieu of minimum standard deviation of angles. Marill's original experiment is then carefully repeated with several additional figure s that were deliberately chosen not to have all equal angles. The experiment is described in detail, and all the failures of Marill's algorithm are carefully studied and explained. The problem, of straight angles is touched upon and the difficulties of solving it as a special case are briefly discussed. A new program is the written to minimize the standard deviation of segment magnitudes instead of minimizing the standard deviation of angles. This program is run on the same test figures as the original algorithm. Its successes and failures are noted and explained, and its behaviors are studied. The results of the two algorithms zero then compared and the differences noted. It is of particular interest that the two algorithms have different areas of failure, suggesting that a combined algorithms should be able to produce better results than either one alone. This and some other avenues of future work are suggested. Finally, some comments about the basic behaviors of both algorithms are made. !6
机译:摘要:本文考虑了托马斯·马里尔(Thomas Marill)在不使用模型将二维线框图形识别为3D对象的一系列论文中所做的工作。 Marill发现,如果最小化在图形的每个顶点上找到的角度的标准偏差,则图形对计算机进行的解释与人类的解释相匹配的可能性比先验预期要高得多。在这里可以观察到,人脑简化输入和寻找模式的趋势,即使在没有人的情况下,也可能至少部分负责观察到的现象。可以猜想,如果确实是这种情况,则应该可以通过最小化其他特征的标准偏差来获得类似的行为。特别是,段长本身是测试特征的绝佳选择-它与角度有很大的不同,并且计算强度较低。因此,考虑了另一种方法:探索片段大小的最小标准偏差代替角度的最小标准偏差。然后仔细地重复Marill的原始实验,并刻意选择其他几个不具有相等角度的图形。详细描述了实验,并仔细研究和解释了Marill算法的所有失败。解决了直角问题,并简要讨论了解决特例的困难。编写了一个新程序,以最小化片段大小的标准偏差,而不是最小化角度的标准偏差。该程序在与原始算法相同的测试图上运行。记录并解释了它的成功和失败,并研究了它的行为。然后比较两种算法的结果为零,并记录差异。两种算法具有不同的故障区域尤其令人感兴趣,这表明组合的算法应该比单独使用任何一种算法都能产生更好的结果。建议使用此方法和其他一些将来的方法。最后,对这两种算法的基本行为进行了一些评论。 !6

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