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Stereo Matching Algorithm Based on Double Components Model

机译:基于双分量模型的立体匹配算法

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摘要

The tiny wires are the great threat to the safety of the UAV flight. Because they have only several pixels isolated far from the background, while most of the existing stereo matching methods require a certain area of the support region to improve the robustness, or assume the depth dependence of the neighboring pixels to meet requirement of global or semi global optimization method. So there will be some false alarms even failures when images contains tiny wires. A new stereo matching algorithm is approved in the paper based on double components model. According to different texture types the input image is decomposed into two independent component images. One contains only sparse wire texture image and another contains all remaining parts. Different matching schemes are adopted for each component image pairs. Experiment proved that the algorithm can effectively calculate the depth image of complex scene of patrol UAV, which can detect tiny wires besides the large size objects. Compared with the current mainstream method it has obvious advantages.
机译:细小的电线是对无人机飞行安全的巨大威胁。因为它们只有几个像素与背景隔离,而大多数现有的立体匹配方法都需要支撑区域的某个区域来提高鲁棒性,或者假设相邻像素的深度相关性来满足全局或半全局的要求优化方法。因此,当图像中包含细小的电线时,将会出现一些误报甚至故障。基于双分量模型,本文提出了一种新的立体匹配算法。根据不同的纹理类型,将输入图像分解为两个独立的成分图像。一个仅包含稀疏的导线纹理图像,另一个包含所有其余部分。每个分量图像对采用不同的匹配方案。实验证明,该算法可以有效地计算出巡逻无人机复杂场景的深度图像,除了可以检测大型物体外,还可以检测到细小的电线。与目前的主流方法相比,它具有明显的优势。

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