Adaptive depth constraint-based underwater binocular image feature matching

Abstract

Objective In this paper, addressing sparse feature points and unique epipolar constraint, an adaptive depth constraint-based underwater feature matching (ADC-UFM) scheme is proposed. Methods By combining the features from accelerated segment test (FAST) operator with scale invariant feature transform (SIFT) descriptors such that the matching accuracy can be dramatically improved. By introducing the underwater refractive factor, the matching constraint model (MCM) can be effectively established, and thereby contributing to eliminating mismatched points. The adaptive threshold choosing (ATC) module is finely devised to extremely preserve image feature information in changeable underwater environments. 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 real-time requirements. Conclusion The results of this study can provide a reliable guarantee for the subsequent underwater 3D reconstruction based on binocular vision system.