Self Organizing Neural Network

L. Maddalena, A. Petrosino, “A Self-organizing Approach to Detection of Moving Patterns for Real-Time Applications”, Advances in Brain, Vision, and Artificial Intelligence, LNCS 2007, Volume 4729, 2007.

L. Maddalena, A. Petrosino, “A self-organizing neural system for background and foreground modeling”, International Conference on Artificial Neural Networks, ICANN 2008, LNCS 5163, Part 1, pages 652-661, 2008.

L. Maddalena, A. Petrosino, “Neural Model-Based Segmentation of Image Motion”, KES 2008, Volume 5177, pages 57-64, 2008.

L. Maddalena, A. Petrosino, “3D Neural Model-Based Stopped Object Detection”, International Conference on Image Analysis and Processing, ICIAP 2009, pages 585-593, 2009.

L. Maddalena, A. Petrosino, “A Self-Organizing Approach to Background Subtraction for Visual Surveillance Applications”, IEEE Transactions on Image Processing, Volume 17, Issue 7, pages 1168-1177, July 2008.

L. Maddalena, A. Petrosino, “Multivalued Background/Foreground Separation for Moving Object Detection”, International Workshop on Fuzzy Logic and Applications, WILF 2009, Volume 5571, pages 263-270, Palermo, Italy, June 2009.

L. Maddalena, A. Petrosino, “Self Organizing and Fuzzy Modelling for Parked Vehicles Detection”, Advanced Concepts for Intelligent Vision Systems, ACVIS 2009, LNCS 5807, pages 422–433, 2009.

L. Maddalena, A. Petrosino, “A fuzzy spatial coherence-based approach to background/foreground separation for moving object detection”, Neural Computing and Applications, NCA 2009, pages 1-8, 2009.

L. Maddalena, A. Petrosino, “The SOBS Algorithm: What Are the Limits?”, IEEE Workshop on Change Detection, CVPR 2012, June 2012.

L. Maddalena, A. Petrosino, “The 3dSOBS+ algorithm for moving object detection”, Computer Vision and Image Understanding, CVIU 2014, May 2014.

L. Maddalena, A. Petrosino, "Self-organizing background subtraction using color and depth data", Multimedia Tools and Applications", October 2018.

Y. Singh, P. Gupta, V. Yadav, “Implementation of a Self-Organizing Approach to Background Subtraction for Visual Surveillance Applications”, IJCSNS International Journal of Computer Science and Network Security, Vol. 10, No.3, pages 136-143, March 2010.

M. Chacon-Muguia, S. Gonzalez-Duarte, P. Vega, “Simplified SOM-neural model for video segmentation of moving objects”, International Joint Conference on Neural Networks, IJCNN 2009, pages 474-480, 2009.

M. Chacon-Murguia, S. Gonzalez-Duarte, “An Adaptive Neural-Fuzzy Approach for Object Detection in Dynamic Backgrounds for Surveillance Systems”, IEEE Transactions on Industrial Electronics, 2011.

M. Chacon-Murguia, G. Ramirez-Alonso, S. Gonzalez-Duarte, “Improvement of a Neural-Fuzzy Motion Detection Vision Model for Complex Scenario Conditions”, International Joint Conference on Neural Networks, IJCNN 2013, 2013.

J. Ramirez-Quintana, M. Chacon-Murguia, “Self-Organizing Retinotopic Maps Applied to Background Modeling for Dynamic Object Segmentation in Video Sequences”, International Joint Conference on Neural Networks, IJCNN 2013, August 2013.

G. Ramirez-Alonso, M. Chacon-Murguia, “Segmentation of Dynamic Objects in Video Sequences Fusing the Strengths of a Background Subtraction Model, Optical Flow and Matting Algorithms”, IEEE Southwest Symposium on Image Analysis and Interpretation, SSIAI 2014, April 2014.

J. Ramirez-Quintana, M. Chacon-Murguia, “Self-adaptive SOM-CNN neural system for dynamic object detection in normal and complex scenarios”, Pattern Recognition, September 2014.

G. Ramirez-Alonso, M. Chacon-Murguia, “Object detection in video sequences by a temporal modular self-adaptive SOM”, Neural Computing and Applications, March 2015.

G. Ramirez-Alonso, M. Chacon-Murguia, “Auto-Adaptive Parallel SOM Architecture with a modular analysis for dynamic object segmentation in videos”, Neurocomputing, November 2015.

Z. Zhao, X. Zhang, Y. Fang, “Stacked Multi-layer Self-Organizing Map for Background Modeling”, IEEE Transactions on Image Processing, 2015.

G. Gemignani, A. Rozza, “A Novel Background Subtraction Approach based on Multi-layered Self-Organizing Maps”, IEEE International Conference on Image Processing, September 2015.

G. Gemignani, A. Rozza, “A Robust Approach for the Background Subtraction based on Multi-layered Self-Organizing Maps”, IEEE Transactions on Image Processing , 2016.

E. Lopez-Rubio, R. Luque-Baena, E. Domínguez, "Foreground detection in video sequences with probabilistic self-organizing maps", International Journal of Neural Systems, pages 225-246, 2011.

M. Molina-Cabello, E. Lopez-Rubio, R. Luque-Baena, E. Domínguez, E. Palomo, "Pixel Features for Self-organizing Map based Detection of Foreground Objects in Dynamic Environments", International Joint Conference SOCO 2016-CISIS 2016-ICEUTE 2016, pages 247-255, October 2016.

M. Molina-Cabello, E. Lopez-Rubio, R. Luque-Baena, E. Domínguez, E. Palomo, "Foreground object detection for video surveillance by fuzzy logic based estimation of pixel illumination states", Logic Journal of the IGPL, September 2018.

Z. Xu, S. Zhu, Y. Cheng, "Object detection via superpixel and 3D self-organizing background subtraction", Chinese Control And Decision Conference ,CCDC 2017,pages 1754-1759, Chongqing, China, 2017.

Y. Du, C. Yuan, W. Hu, S. Maybank, "Spatio-temporal self-organizing map deep network for dynamic object detection from videos", IEEE Conference on Computer Vison and Pattern Recognition, CVPR 2017, June 2017.

J. Kim, J. Cho, "An online graph-based anomalous change detection strategy for unsupervised video surveillance", EURASIP Journal on Image and Video Processing, 2019.

S. Lu, X. Ma, "Adaptive random‑based self‑organizing background subtraction for moving detection", International Journal of Machine Learning and Cybernetics, 2019.

M. Nagaraju, B. Babu, M. Somayajulu, K. Sarma, A. Vetagiri, “An accurate foreground moving object detection based on segmentation techniques and optimal classifier”, Wiley Concurrency and Computation Practice and Experience, November 2021.