Institute for Sensory Research, Department of Biomedical and Chemical Engineering, Syracuse University, Syracuse, New York, United States of America.
PLoS One. 2019 Jul 29;14(7):e0219052. doi: 10.1371/journal.pone.0219052. eCollection 2019.
The stereo correspondence problem exists because false matches between the images from multiple sensors camouflage the true (veridical) matches. True matches are correspondences between image points that have the same generative source; false matches are correspondences between similar image points that have different sources. This problem of selecting true matches among false ones must be overcome by both biological and artificial stereo systems in order for them to be useful depth sensors. The proposed re-examination of this fundamental issue shows that false matches form a symmetrical pattern in the array of all possible matches, with true matches forming the axis of symmetry. The patterning of false matches can therefore be used to locate true matches and derive the depth profile of the surface that gave rise to them. This reverses the traditional strategy, which treats false matches as noise. The new approach is particularly well-suited to extract the 3-D locations and shapes of camouflaged surfaces and to work in scenes characterized by high degrees of clutter. We demonstrate that the symmetry of false-match signals can be exploited to identify surfaces in random-dot stereograms. This strategy permits novel depth computations for target detection, localization, and identification by machine-vision systems, accounts for physiological and psychophysical findings that are otherwise puzzling and makes possible new ways for combining stereo and motion signals.
立体对应问题的存在是因为多个传感器的图像之间的错误匹配掩盖了真实（真实）的匹配。真实的匹配是具有相同生成源的图像点之间的对应关系；错误的匹配是具有不同来源的相似图像点之间的对应关系。为了使生物和人工立体系统成为有用的深度传感器，它们必须克服从错误匹配中选择真实匹配的问题。对这一基本问题的重新审视表明，错误匹配在所有可能匹配的阵列中形成对称模式，而真实匹配形成对称轴。因此，可以利用错误匹配的模式来定位真实匹配，并得出产生它们的表面的深度轮廓。这与传统策略相反，传统策略将错误匹配视为噪声。新方法特别适合提取伪装表面的 3-D 位置和形状，并在具有高度杂乱度的场景中工作。我们证明可以利用错误匹配信号的对称性来识别随机点立体图像中的表面。这种策略允许机器视觉系统进行新的深度计算，用于目标检测、定位和识别，解释否则令人困惑的生理和心理物理发现，并为立体和运动信号的组合开辟新途径。
