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Enhancing a disparity map by color segmentation

机译：通过颜色分割增强视差图

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摘要

Depth perception is a crucial but greedy task for most mobile robots to navigate in their environment and avoid obstacles. Generally, it does not need to be completed at permanent full precision but can be done in a coarse-to-fine strategy. In the present paper, a novel method is proposed to enhance a very sparse disparity map provided by a block matching strategy for example. To that purpose, the region and edge maps of the initial image are successively analyzed. The segmentation is achieved on the sole chrominance information to produce stable and homogeneous regions, closely related to the real boundaries of the objects. A particular attention is given to remove errors due to occlusions, by making the depth and region maps cooperate. In a second stage, the luminance variations are analyzed to fill up the depth map. Finally, the edge map computed on the luminance component is used to alleviate problems due to under-segmentation. The experiments show that the initial disparity map is successfully improved without any minimization process. A comparison is made regarding the segmentation method, whether it performs directly in the spatial domain (region-growing) or it jointly uses the color and space domains (mean-shift). In addition, two luminance-chrominance color spaces are studied: L~*u~*v~* and ρφθ.
机译：对于大多数移动机器人来说，深度感知是一项至关重要但贪婪的任务，它需要在环境中导航并避开障碍物。通常，它不需要以永久的全精度完成，而是可以采用从粗到精的策略来完成。在本文中，提出了一种新颖的方法来增强例如由块匹配策略提供的非常稀疏的视差图。为此，先要分析初始图像的区域和边缘图。在唯一的色度信息上实现分割，以产生与对象的真实边界紧密相关的稳定且均匀的区域。通过使深度图和区域图配合使用，特别注意消除由于遮挡引起的错误。在第二阶段，分析亮度变化以填充深度图。最后，在亮度分量上计算的边缘图可用于缓解由于分割不足而引起的问题。实验表明，无需任何最小化过程即可成功改善初始视差图。对分割方法进行了比较，无论是直接在空间域中执行（区域增长）还是联合使用颜色和空间域（均值移动）。另外，研究了两个亮度色度色空间：L〜* u〜* v〜*和ρφθ。

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