基于自适应深度约束的水下双目图像特征匹配

Adaptive depth constraint-based underwater binocular image feature matching

    摘要
  • 摘要:
      目的  针对水下双目图像特征点稀疏、极线约束模型失效等难题，提出一种基于自适应深度约束的水下图像特征匹配(ADC-UFM)算法。
      方法  结合FAST算子与SIFT描述子，提高图像匹配精度；提出基于水下折射因子的特征匹配约束模型（MCM），有效剔除误匹配点；提出自适应阈值选取（ATC）方法，最大限度地保留复杂水下环境下的图像特征信息。
      结果  实验结果显示，ADC-UFM算法优于现有的SIFT，SURF和UCC-SIFT等典型方法，匹配准确率可达85.2%，满足实时匹配需求。
      结论  研究成果可为基于双目视觉系统的水下三维重建提供关键技术支撑。

     

    Abstract:
      Objective   In this paper, to address sparse feature points and unique epipolar constraints, an adaptive depth constraint-based underwater feature matching (ADC-UFM) scheme is proposed.
      Methods  By combining a features from accelerated segment test (FAST) operator with scale invariant feature transform (SIFT) descriptors, the matching accuracy can be dramatically improved. By introducing an underwater refractive factor, the matching constraint model (MCM) can be effectively established, thereby contributing to eliminating mismatched points. The adaptive threshold choosing (ATC) module is finely devised to preserve image feature information in changeable underwater environments to an extreme extent.
      Results  Comprehensive experiments show that the proposed ADC-UFM scheme can outperform typical matching schemes including SIFT, speeded-up robust features (SURF) and SIFT feature matching based on underwater curve constraint (UCC-SIFT), which not only achieves 85.2% matching accuracy but also meets the real-time requirements.
      Conclusion  The results of this study can provide a reliable guarantee for subsequent underwater 3D reconstruction based on the binocular vision system.

     

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