Underwater object detection based on bi-dimensional empirical mode decomposition and Gaussian Mixture Model approach-Reference-Cited by-同舟云学术

Underwater object detection based on bi-dimensional empirical mode decomposition and Gaussian Mixture Model approach

Published:2021-12 Issue: Volume:66 Page:101469
ISSN:1574-9541
Container-title:Ecological Informatics
language:en
Short-container-title:Ecological Informatics

Author:

Mathias Ajisha,Dhanalakshmi Samiappan,Kumar R.,Narayanamoorthi R.

Publisher

Elsevier BV

Subject

Applied Mathematics,Computational Theory and Mathematics,Computer Science Applications,Ecological Modeling,Modeling and Simulation,Ecology,Ecology, Evolution, Behavior and Systematics

Reference34 articles.

1. Enhancing underwater images and videos by fusion;Ancuti,2012

2. Initial results in underwater single image dehazing;Carlevaris-Bianco,2010

3. Underwater fish tracking for moving cameras based on deformable multiple kernels;Chuang;IEEE Trans. Syst. Man Cybernet. Syst.,2016

4. ZooplanktonNet: Deep Convolutional Network for Zooplankton Classification. OCEANS 2016 - Shanghai;Dai,2016

Cited by 23 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Real-time underwater object detection technology for complex underwater environments based on deep learning;Ecological Informatics;2024-09

2. Floating object detection using double-labelled domain generalization;Engineering Applications of Artificial Intelligence;2024-07

3. PE-Transformer: Path enhanced transformer for improving underwater object detection;Expert Systems with Applications;2024-07

4. Improved Deep Learning Approach For Underwater Salient Object Detection Using Spectral Residual and Fuzzy C-Means Clustering;Journal of Electrical Engineering & Technology;2024-03-02

5. YWnet: A convolutional block attention-based fusion deep learning method for complex underwater small target detection;Ecological Informatics;2024-03