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Measurement Method of Underwater Target Based on Binocular Vision

机译:基于双目视觉的水下目标测量方法

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Traditional measurement methods are obtained by calculating the global disparity map. The accuracy of these methods is low and the speed is slow. Under the condition of uneven illumination, low contrast and obvious noise, they can't meet the measurement requirements well. An underwater image enhancement method based on Contrast Limited Adaptive Histogram Equalization and image weighting fusion is proposed. For the measurement of underwater target, only two matching points are solved in the algorithm design, which improves the accuracy and speed of underwater ranging. The idea of coarse-to-fine matching is adopted, that is, the SAD algorithm is used in matching area to complete the coarse matching such that the matching windows can be determined firstly. Then the stereo matching algorithm based on NCC and Census fusion is used to decide the final matching point. The algorithm is insensitive to light and can extract edges and comers better. Finally, three-dimensional reconstruction is carried out to restore the three-dimensional coordinates of the endpoints to be measured, and the real size can be obtained by calculating the Euclidean distance between the two points. The experimental results show that the algorithm runs fast and robustly without calculating the disparity of the whole image, and the results meet the accuracy requirements.
机译:传统的测量方法是通过计算全局视差图获得的。这些方法的准确性低,速度慢。在光照不均匀,对比度低,噪声明显的情况下,它们不能很好地满足测量要求。提出了一种基于对比度受限自适应直方图均衡和图像加权融合的水下图像增强方法。对于水下目标的测量,算法设计中只解决了两个匹配点,提高了水下测距的准确性和速度。采用了从粗到精匹配的思想,即在匹配区域中使用SAD算法来完成粗匹配,这样就可以首先确定匹配窗口。然后使用基于NCC和人口普查融合的立体匹配算法来确定最终的匹配点。该算法对光不敏感,可以更好地提取边缘和拐角。最后,进行三维重建以恢复要测量的端点的三维坐标,并且可以通过计算两点之间的欧几里得距离来获得实际大小。实验结果表明,该算法运行快速,鲁棒,无需计算整个图像的视差,结果满足精度要求。

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