Abstract
In this paper, we propose a two-stage algorithm, named watershed-constrained image segmentation, for exploring complete edge-closed regions from edges. In the first stage, the input image is pre-processed and the image gradient information is obtained using a gradient operator. Anchors are then obtained from the gradient information. Finally, initial edges are obtained by intelligently connecting the anchors. In the second stage, a marker-based watershed algorithm is adopted to obtain marker points from the gradient information obtained in the first stage. A Gaussian filtered image is then used as the input image to obtain a watershed hyper-segmented edge map. Finally, complete edge-closed regions are obtained by combining the initial edges and the hyper-segmented edge map and searching for weak edges. The image segmentation results are then obtained from the edge-closed regions, demonstrating the excellent performance of our proposed algorithm on various images and videos.
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.
