Dual-Fusion Active Contour Model with Semantic Information for Saliency Target Extraction of Underwater Images

Underwater vision research is the foundation of marine-related disciplines. The target contour extraction is significant for target tracking and visual information mining. Aiming to resolve the problem that conventional active contour models cannot effectively extract the contours of salient targets in underwater images, we propose a dual-fusion active contour model with semantic information. First, the saliency images are introduced as semantic information and salient target contours are extracted by fusing Chan-Vese and local binary fitting models. Then, the original underwater images are used to supplement the missing contour information by using the local image fitting. Compared with state-of-the-art contour extraction methods, our dual-fusion active contour model can effectively filter out background information and accurately extract salient target contours. Moreover, the proposed model achieves the best results in the quantitative comparison of MAE (mean absolute error), ER (error rate), and DR (detection rate) indicators and provides reliable prior knowledge for target tracking and visual information mining.

Automated summary

The research introduces a dual-fusion active contour model with semantic information to improve the accuracy of contour extraction in underwater images. Experimental results show that the proposed model achieves the best results in MAE, ER, and DR indicators, providing reliable prior knowledge for target tracking and visual information mining.