Variational Bayesian learning for background subtraction based on local fusion feature-Reference-Cited by-同舟云学术

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Variational Bayesian learning for background subtraction based on local fusion feature

Published:2016-07-20 Issue:8 Volume:10 Page:884-893
ISSN:1751-9640
Container-title:IET Computer Vision
language:en
Short-container-title:IET Computer Vision

Author:

Yan Junhua¹,Wang Shunfei¹,Xie Tianxia¹,Yang Yong¹,Wang Jiayi¹

Affiliation:

1. College of AstronauticsNanjing University of Aeronautics and AstronauticsNanjingPeople's Republic of China

Funder

Fundamental Research Fund of Shandong University

China Aerospace Science and Technology Corporation

Chinese Aeronautical Establishment

National Natural Science Foundation of China

Publisher

Institution of Engineering and Technology (IET)

Subject

Computer Vision and Pattern Recognition,Software

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1049/iet-cvi.2016.0075

Reference22 articles.

1. Background subtraction techniques: a review

2. A texture-based method for modeling the background and detecting moving objects

3. False Positive Reduction in Mammographic Mass Detection Using Local Binary Patterns

4. Improving background subtraction using Local Binary Similarity Patterns

5. Detecting objects using color and depth segmentation with Kinect sensor

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1. English Translation Teaching Model of Flipped Classroom Based on the Fusion Algorithm of Network Communication and Artificial Intelligence;Wireless Communications and Mobile Computing;2021-10-31

2. Background subtraction for moving object detection: explorations of recent developments and challenges;The Visual Computer;2021-08-16

3. A novel background subtraction algorithm based on parallel vision and Bayesian GANs;Neurocomputing;2020-06

4. On the role and the importance of features for background modeling and foreground detection;Computer Science Review;2018-05

5. Dynamic background subtraction method based on spatio‐temporal classification;IET Computer Vision;2018-02-19

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