Abstract
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.
Highlights
Since underwater vision research is the basis of marinerelated disciplines, the rapid development of underwater image processing technology is inevitable [1,2]
This section tested the proposed method on intensity-heterogeneous underwater images captured from underwater videos downloaded from the NATURE FOOTAGE website and Fish Dataset
Aiming to resolve the problem that conventional active contour models cannot effectively extract the contours of the salient object in underwater images, we proposed a dual-fusion active contour model with semantic information
Summary
The development and utilization of the ocean have gradually become an important development direction. Liu et al [7] proposed an improved level set algorithm based on the gradient descent method and applied it to segment underwater biological images. Wei et al [8] improved the Kmeans algorithm to segment underwater image backgrounds and addressed the issue of improper K value determination. This algorithm can minimize the impact of the initial centroid position of a grayscale image. The clustering algorithms mentioned above are greatly affected by the local gray unevenness of underwater images. Clustering algorithms contain local convergence errors and are only suitable for underwater images with a single background gray level
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
