Abstract
In this study, a video object segmentation approach using convex optimization of foreground and background distributions is proposed. The proposed approach consists of four stages. First, optical flow computation and superpixel segmentation are performed on video frames. Second, convex optimization with a mixed energy function is employed to estimate the initial foreground and background distributions of video frames. Third, binary label maps for video frames are generated by maximum a posteriori (MAP) estimation. Fourth, the binary label maps are refined to obtain the final video object segmentation maps. Based on the experimental results obtained in this study, the performance of the proposed approach is better than those of three comparison approaches.
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.
