Abstract
Image Segmentation of Underwater Environment with Inhomogeneous Intensity Turns Out to Be One of the Most Challenging Topics These Years. in This Paper, We Try to Combine Co-Saliency Detection with Local Statistical Active Contour Model Together for Underwater Image Segmentation. the Cluster-Based Algorithm is First Taken for Co-Saliency Detection, Which Makes Salient Region in the Underwater Images Be Highlighted. the Local Statistical Active Contour Model, a Novel Region-Based Level Set Method, is Then Made Full Use of two Segment Underwater Images. It is Shown in Our Simulation Experiment that Our Proposed Scheme Could Achieve Great Segmentation Performance in Both Efficiency and Quality for Underwater Images.
Recommended Citation
Y. Zhu et al., "Underwater Image Segmentation with Co-Saliency Detection and Local Statistical Active Contour Model," OCEANS 2017 - Aberdeen, pp. 1 - 5, Institute of Electrical and Electronics Engineers, Oct 2017.
The definitive version is available at https://doi.org/10.1109/OCEANSE.2017.8084742
Department(s)
Engineering Management and Systems Engineering
Keywords and Phrases
Co-saliency detection; Level set; local statistical active contour model; Underwater image segmentation
International Standard Book Number (ISBN)
978-150905278-3
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Institute of Electrical and Electronics Engineers, All rights reserved.
Publication Date
25 Oct 2017
