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首页> 外文会议>Biennial Australian Pattern Recognition Society Conference(DICTA2003) v.2; 2003; Sydney; AU >Dense Disparity Maps Respecting Occlusions and Object Separation Using Partial Differential Equations
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Dense Disparity Maps Respecting Occlusions and Object Separation Using Partial Differential Equations

机译：使用偏微分方程的遮挡和物体分离的密集视差图

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摘要

In this work, we present substantial enhancements to solve the stereo correspondence problem using a minimization and regulariza-tion formulation with a partial differential equations approach. For the first time it allows to respect occlusions and separation of objects. We introduce a boundary condition that makes it possible to estimate disparities of arbitrarily shaped regions and thus to explicitely handle occlusions. After calculating a dense disparity map, we detect occlusions and object boundaries, cut the disparity map at these boundaries, and minimize the energy functional once more resulting in more accurate estimates. We show that we can achieve a speed up of a factor of four by rectified image pairs and a correlation based algorithm to calculate an initial estimate. In addition, a priori knowledge provided as region of interest or location of object boundaries further improves both the speed and the quality of the estimation. The results demonstrate that the quality is greatly improved with the proposed approach.
机译：在这项工作中，我们提出了实质性的改进，以使用偏微分方程方法的最小化和正则化公式解决立体对应问题。它首次允许尊重物体的遮挡和分离。我们介绍了一个边界条件，该条件使得可以估计任意形状的区域的差异，从而显式地处理遮挡。在计算出密集的视差图之后，我们将检测遮挡和物体边界，在这些边界处切割视差图，并再次最小化能量函数，从而得出更准确的估计值。我们表明，通过校正的图像对和基于相关的算法来计算初始估计值，我们可以将速度提高四倍。另外，提供为关注区域或对象边界的位置的先验知识进一步提高了估计的速度和质量。结果表明，该方法大大提高了质量。

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