从光滑表面图像恢复对极几何

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摘要翻译：
我们提出了四种从光滑表面图像中恢复对极几何形状的方法。在现有的对极几何恢复方法中，都使用了在这类图像中找不到的对应特征点。第一种方法是基于寻找光照产生的相应特征点（ICPM-光照特征点法(PM)）。第二种方法是基于光滑物体外极到轮廓的切线产生的对应切点（OTPM-Outline Att切PM）。这两种方法对真实图像的光照特征点和轮廓线的位置都是已知的，误差很小，因此，这两种方法对真实图像的光照特征点和轮廓线的位置都是准确的，可以得到正确的结果。但是第二种方法要么局限于特殊类型的场景，要么局限于受限的摄像机运动。我们还考虑了另外两种方法，即CCPM（曲线特征PM）和CTPM（曲线切线PM）。CCPM方法是利用等距曲线之间曲率的相关性在等距曲线上寻找对应点。CTPM方法是根据切线到等影射曲线外极线的性质映射到切线到相应的等影射曲线外极线上。基于几乎精确对应点对知识的标准方法(SM)。该方法已经在对真实图像上进行了SM实现和测试。不幸的是，后两种方法只给出了包括“好”解在内的有限解子集。例外是“无限极”。其主要原因是光滑物体亮度不变的假设不准确。但是轮廓和光照特征点不受这种不准确性的影响。因此，第一对方法给出了精确的结果。
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英文标题：
《Recovering Epipolar Geometry from Images of Smooth Surfaces》
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作者：
Oleg Kupervasser
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最新提交年份：
2012
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Computer Vision and Pattern Recognition        计算机视觉与模式识别
分类描述：Covers image processing, computer vision, pattern recognition, and scene understanding. Roughly includes material in ACM Subject Classes I.2.10, I.4, and I.5.
涵盖图像处理、计算机视觉、模式识别和场景理解。大致包括ACM课程I.2.10、I.4和I.5中的材料。
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一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
  We present four methods for recovering the epipolar geometry from images of smooth surfaces. In the existing methods for recovering epipolar geometry corresponding feature points are used that cannot be found in such images. The first method is based on finding corresponding characteristic points created by illumination (ICPM - illumination characteristic points' method (PM)). The second method is based on correspondent tangency points created by tangents from epipoles to outline of smooth bodies (OTPM - outline tangent PM). These two methods are exact and give correct results for real images, because positions of the corresponding illumination characteristic points and corresponding outline are known with small errors. But the second method is limited either to special type of scenes or to restricted camera motion. We also consider two more methods which are termed CCPM (curve characteristic PM) and CTPM (curve tangent PM), for searching epipolar geometry for images of smooth bodies based on a set of level curves with constant illumination intensity. The CCPM method is based on searching correspondent points on isophoto curves with the help of correlation of curvatures between these lines. The CTPM method is based on property of the tangential to isophoto curve epipolar line to map into the tangential to correspondent isophoto curves epipolar line. The standard method (SM) based on knowledge of pairs of the almost exact correspondent points. The methods have been implemented and tested by SM on pairs of real images. Unfortunately, the last two methods give us only a finite subset of solutions including "good" solution. Exception is "epipoles in infinity". The main reason is inaccuracy of assumption of constant brightness for smooth bodies. But outline and illumination characteristic points are not influenced by this inaccuracy. So, the first pair of methods gives exact results.
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PDF链接：
https://arxiv.org/pdf/1106.0823

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关键词：Presentation Intelligence Recognition Computation correlation 例外 outline 对应点 method 切线