Abstract
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.
Funder
Liuzhou Science and Technology Plan Project
Guangxi Science and Technology Major Special Project Guike
Hebei Innovation Capability Improvement Program
National Natural Science Foundation of China
Key Projects of Hebei Natural Science Foundation
Central Guidance on Local Science and Technology Development Fund of Hebei Province
Subject
Applied Mathematics,Instrumentation,Engineering (miscellaneous)