A Universal Foreground Segmentation Technique using Deep-Neural Network
Published 2020 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
A Universal Foreground Segmentation Technique using Deep-Neural Network
Authors
Keywords
-
Journal
MULTIMEDIA TOOLS AND APPLICATIONS
Volume -, Issue -, Pages -
Publisher
Springer Science and Business Media LLC
Online
2020-05-06
DOI
10.1007/s11042-020-08977-5
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Deepside: A general deep framework for salient object detection
- (2019) Keren Fu et al. NEUROCOMPUTING
- SegFast-V2: Semantic image segmentation with less parameters in deep learning for autonomous driving
- (2019) Swarnendu Ghosh et al. International Journal of Machine Learning and Cybernetics
- A sequential search-space shrinking using CNN transfer learning and a Radon projection pool for medical image retrieval
- (2018) Amin Khatami et al. EXPERT SYSTEMS WITH APPLICATIONS
- Frankenstein: Learning Deep Face Representations Using Small Data
- (2018) Guosheng Hu et al. IEEE TRANSACTIONS ON IMAGE PROCESSING
- Deep Background Modeling Using Fully Convolutional Network
- (2018) Lu Yang et al. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
- Fine-tuning CNN Image Retrieval with No Human Annotation
- (2018) Filip Radenovic et al. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
- A deep convolutional neural network for video sequence background subtraction
- (2018) Mohammadreza Babaee et al. PATTERN RECOGNITION
- Combining CNN streams of RGB-D and skeletal data for human activity recognition
- (2018) Pushpajit Khaire et al. PATTERN RECOGNITION LETTERS
- $M^{4}CD$ : A Robust Change Detection Method for Intelligent Visual Surveillance
- (2018) Kunfeng Wang et al. IEEE Access
- SD-CNN: a Shallow-Deep CNN for Improved Breast Cancer Diagnosis
- (2018) Fei Gao et al. COMPUTERIZED MEDICAL IMAGING AND GRAPHICS
- WeSamBE: A Weight-Sample-Based Method for Background Subtraction
- (2017) Shengqin Jiang et al. IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
- T-CNN: Tubelets with Convolutional Neural Networks for Object Detection from Videos
- (2017) Kai Kang et al. IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
- Pixel Modeling Using Histograms Based on Fuzzy Partitions for Dynamic Background Subtraction
- (2017) Zhi Zeng et al. IEEE TRANSACTIONS ON FUZZY SYSTEMS
- Convolutional neural network-based encoding and decoding of visual object recognition in space and time
- (2017) K. Seeliger et al. NEUROIMAGE
- Interactive deep learning method for segmenting moving objects
- (2017) Yi Wang et al. PATTERN RECOGNITION LETTERS
- Interval-Valued Model Level Fuzzy Aggregation-Based Background Subtraction
- (2017) Pojala Chiranjeevi et al. IEEE Transactions on Cybernetics
- Universal Background Subtraction Using Word Consensus Models
- (2016) Pierre-Luc St-Charles et al. IEEE TRANSACTIONS ON IMAGE PROCESSING
- Auto-Adaptive Parallel SOM Architecture with a modular analysis for dynamic object segmentation in videos
- (2016) Graciela Ramírez-Alonso et al. NEUROCOMPUTING
- SuBSENSE: A Universal Change Detection Method With Local Adaptive Sensitivity
- (2015) Pierre-Luc St-Charles et al. IEEE TRANSACTIONS ON IMAGE PROCESSING
- Human detection in surveillance videos and its applications - a review
- (2013) Manoranjan Paul et al. EURASIP Journal on Advances in Signal Processing
- Intelligent visual surveillance — A survey
- (2010) In Su Kim et al. INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS
- A fuzzy spatial coherence-based approach to background/foreground separation for moving object detection
- (2009) Lucia Maddalena et al. NEURAL COMPUTING & APPLICATIONS
- A Self-Organizing Approach to Background Subtraction for Visual Surveillance Applications
- (2008) L. Maddalena et al. IEEE TRANSACTIONS ON IMAGE PROCESSING
Find the ideal target journal for your manuscript
Explore over 38,000 international journals covering a vast array of academic fields.
SearchCreate your own webinar
Interested in hosting your own webinar? Check the schedule and propose your idea to the Peeref Content Team.
Create Now