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Stereo Matching Algorithm Based on Double Components Model

机译:基于双组件模型的立体声匹配算法

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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:微小的电线是对无人机飞行安全的巨大威胁。因为它们仅具有远离背景的几个像素,而大多数现有立体声匹配方法需要支撑区域的某个区域以提高鲁棒性,或者假设相邻像素的深度依赖性满足全局或半全局的要求优化方法。因此,当图像包含微小的电线时,会有一些误报甚至发生故障。一种新的立体声匹配算法基于双重组件模型批准了本文。根据不同的纹理类型,输入图像被分解成两个独立的分量图像。一个只包含稀疏的线纹理图像,另一个包含所有剩余部分。每个组件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巡逻UAV复杂场景的深度图像,除了大尺寸对象之外可以检测微小的电线。与目前的主流方法相比,它具有明显的优势。

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