Thierry Bouwmans
- Mathématiques, Image et Applications (MIA)
Présentation
Thierry Bouwmans is an Associate Professor at the University of La Rochelle, France. His research interests consist mainly in the detection of moving objects in challenging environments as it is a pre-step for behaviour detection in video surveillance. He has recently authored more than 100 papers in refereed international journals and conferences in the field of background modeling and foreground detection, and has co-edited two books in CRC Press (background/foreground separation for video surveillance, robust PCA via decomposition in low rank and sparse matrices). His research investigated particularly the use of fuzzy concepts, discriminative subspace learning models and robust PCA in video surveillance. It also concern full exhaustive surveys on mathematical tools used in foreground/background separation. He has been the lead guest editor of the special issue on “Background Modeling for Foreground Detection in Real-World Dynamic Scenes” in the journal Machine Vision and Applications. He has been the invited talk in the International Workshop on Background Models Challenge at ACCV 2012. He has supervised five Ph.D. students in this field. He is the coordinator of the BGSlibrary and LRS library. He is the creator and the administrator of the Background Subtraction Web Site (33 115 visits and 17 636 visitors). He is a reviewer for prestigious international journals including IEEE (Trans. on Image Processing, Trans. on Multimedia, Trans. on CSVT, etc.), SPRINGER (IJCV, MVA, etc.) and ELSEVIER (CVIU, PR, PRL, etc.), and top-level conferences such as CVPR, ICPR, ICIP, AVSS, etc.
Publications
Publications
Handbook on "Robust Low-Rank and Sparse Matrix Decomposition: Applications in Image and Video ProcessingOuvrages hal-01373013v1 |
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Handbook on "Background Modeling and Foreground Detection for Video SurveillanceT. Bouwmans, F. Porikli, B. Hörferlin, A. Vacavant. Taylor and Francis Group, CRC Press, 2014 |
Local Change Detection from Video Scene: An End-to-End Encoder–Decoder-Type Deep Neural Network Architecture with Feature PoolingPattern Recognition and Computer Vision in the New AI Era, 09, WORLD SCIENTIFIC, pp.461-486, 2025, Series in Computer Vision, ⟨10.1142/9789819807154_0019⟩ |
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Toward a Complete System for Pupil Monitoring Using Deep Learning for Digital TwinsDigital Twins and Simulation Technology, 1, Chapman and Hall/CRC, pp.167-185, 2025, ⟨10.1201/9781003582489-12⟩ |
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Toward Graph Refactoring in Latent Space for Underwater Moving Object Detection for Digital TwinsDigital Twins and Simulation Technology, 1, Chapman and Hall/CRC, pp.150-166, 2025, ⟨10.1201/9781003582489-11⟩ |
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Graph Neural Networks for Moving Objects Detection in VideosPattern Recognition and Computer Vision in the New AI Era, 09, WORLD SCIENTIFIC, pp.121-143, 2025, Series in Computer Vision, 978-981-98-0714-7. ⟨10.1142/9789819807154_0006⟩ |
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A Transductive and Inductive GNNs for Physical Moving Objects Detection in Surface Scenes for Digital TwinsDigital Twins and Simulation Technology, 1, Chapman and Hall/CRC, pp.133-149, 2025, 9781032949390. ⟨10.1201/9781003582489-10⟩ |
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Moving Objects Detection for Video Surveillance Applications in Society 5.0Technological Prospects and Social Applications of Society 5.0, 1, Chapman and Hall/CRC, pp.133-144, 2023, ⟨10.1201/9781003324720-12⟩ |
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Real-Time Implementations of Background Subtraction for IoT Applications in Computer Vision and Internet of ThingsL. Sharma, M. Carpenter, CRC Press, Taylor and Francis Group. Handbook on "Computer Vision and Internet of Things: Technologies and Applications", 1, Chapman and Hall/CRC, 2022, ⟨10.1201/9781003244165⟩ |
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Moving Objects Detection in Video Processing: A Graph Signal Processing Approach for Background SubtractionArtificial Intelligence, 1, Chapman and Hall/CRC, pp.171-181, 2021, ⟨10.1201/9781003140351-18⟩ |
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From Moving Objects Detection to Classification and Recognition: A Review for Smart EnvironmentsTowards Smart World, 1, Chapman and Hall/CRC, pp.289-316, 2020, ⟨10.1201/9781003056751-17⟩ |
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Deep learning based background subtraction : a systematic surveyHandbook of Pattern Recognition and Computer Vision, 6, WORLD SCIENTIFIC, pp.51-73, 2020, ⟨10.1142/9789811211072_0003⟩ |
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Visual Surveillance of Human Activities: Background Subtraction Challenges and MethodsHandbook on “From Visual Surveillance to Internet of Things: technology and applications”, CRC Press, Taylor and Francis Group, 2019 |
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Visual Surveillance of Natural Environments: Background Subtraction Challenges and MethodsHandbook on “From Visual Surveillance to Internet of Things: technology and applications”, CRC Press, Taylor and Francis Group, 2019 |
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LRSLibrary: Low-Rank and Sparse tools for Background Modeling and Subtraction in VideosCRC Press, Taylor and Francis Group. Handbook on "Robust Low-Rank and Sparse Matrix Decomposition: Applications in Image and Video Processing", , 2016 |
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Stochastic RPCA for Background/Foreground SeparationHandbook on "Robust Low-Rank and Sparse Matrix Decomposition: Applications in Image and Video Processing, CRC Press, Taylor and Francis Group, 2016 |
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Robust Principal Component Analysis via Decomposition into Low-rank and Sparse Matrices: An overviewHandbook on "Robust Low-Rank and Sparse Matrix Decomposition: Applications in Image and Video Processing", CRC Press, Taylor and Francis Group, 2016 |
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BGS Library: A Library Framework for Algorithm’s Evaluation in Foreground/Background SegmentationHandbook on "Background Modeling and Foreground Detection for Video Surveillance", Chapter 23, , 2014, 9781482205374 |
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Recent Approaches in Background Modeling for Static CamerasHandbook on "Background Modeling and Foreground Detection for Video Surveillance", Chapter 1, CRC Press, 2014, 9781482205374 |
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Traditional Approaches in Background Modeling for Static CamerasHandbook on "Background Modeling and Foreground Detection for Video Surveillance", Chapter 2, , 2014, 9781482205374 |
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Background Subtraction For Visual Surveillance: A Fuzzy ApproachHandbook on Soft Computing for Video Surveillance, CRC Press, pp.103-134, 2012, ⟨10.1201/b11631-6⟩ |
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Robust Principal Component Analysis for Background Subtraction: Systematic Evaluation and Comparative AnalysisPrincipal Component Analysis, Book 1, INTECH, pp.223-238, 2012 |
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Statistical Background Modeling for Foreground Detection: A SurveyHandbook of Pattern Recognition and Computer Vision, World Scientific Publishing, pp.181-199, 2010 |
Anomalous Sound Detection Based on Graph Neural Networks for Forest PreservationWorkshop on Computational Intelligence, UKCI 2025, Sep 2025, Edinburgh, United Kingdom. pp.438-450, ⟨10.1007/978-3-032-07938-1_37⟩ |
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Accelerating Traditional Object Detection Methods on Sophisticated Embedded SystemsInternational Conference on Intelligent Systems and Pattern Recognition, Jun 2024, Istanbul (Turquie), France. pp.220-234, ⟨10.1007/978-3-031-82153-0_17⟩ |
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Accelerating 4K Morphological Image Operations on PYNQ-Z1 and Kria KV260 FPGA Boards Using the Vitis Vision LibraryInternational Conference on Intelligent Systems, Blockchain, and Communication Technologies, May 2025, Cairo, Egypt, France. pp.1-16, ⟨10.1007/978-3-032-09562-6_1⟩ |
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Anomalous Sound Detection For Road Surveillance Based On Graph Signal ProcessingThe 32nd European Conference on Signal Processing : EUSIPCO 2024, Aug 2024, Lyon, France. ⟨10.23919/EUSIPCO63174.2024.10715291⟩ |
Accelerating Image Processing Functions on ZYNQ-FPGA Using the Vitis Vision Library2024 3rd International Conference on Advanced Electrical Engineering (ICAEE), Nov 2024, Sidi-Bel-Abbes, France. pp.1-6, ⟨10.1109/ICAEE61760.2024.10783257⟩ |
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Higher-Order Sparse Convolutions in Graph Neural NetworksICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Jun 2023, Rhodes Island, France. pp.1-5, ⟨10.1109/ICASSP49357.2023.10096494⟩ |
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Time-varying Signals Recovery via Graph Neural NetworksICASSP 2023 - IEEE International Conference on Acoustics, Speech and Signal Processing, Jun 2023, Rhodes Island, Greece. pp.1-5, ⟨10.1109/ICASSP49357.2023.10096168⟩ |
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On the Trade-off between Over-smoothing and Over-squashing in Deep Graph Neural NetworksCIKM 2023 - The 32nd ACM International Conference on Information and Knowledge Management, Oct 2023, Birmingham United Kingdom, United Kingdom. pp.566-576, ⟨10.1145/3583780.3614997⟩ |
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Higher-order Sparse Convolutions in Graph Neural NetworksICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech, and Signal Processing, Jun 2023, Rhodes Island, Greece. pp.1-5, ⟨10.1109/ICASSP49357.2023.10096494⟩ |
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Inductive Graph Neural Networks for Moving Object Segmentation2023 IEEE International Conference on Image Processing (ICIP), Oct 2023, Kuala Lumpur, Malaysia. pp.2730-2734, ⟨10.1109/ICIP49359.2023.10222668⟩ |
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Hypergraph Convolutional Networks for Weakly-Supervised Semantic Segmentation2022 IEEE International Conference on Image Processing (ICIP), Oct 2022, Bordeaux, France. pp.16-20, ⟨10.1109/ICIP46576.2022.9897774⟩ |
An End to End Encoder-Decoder Network with Multi-scale Feature Pulling for Detecting Local Changes From Video Scene2022 18th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), Nov 2022, Madrid, Spain. pp.1-8, ⟨10.1109/AVSS56176.2022.9959141⟩ |
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Graph CNN for Moving Object Detection in Complex Environments from Unseen Videos2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), Oct 2021, Montreal, Canada. pp.225-233, ⟨10.1109/ICCVW54120.2021.00030⟩ |
Background/Foreground Separation: Guided Attention based Adversarial Modeling (GAAM) versus Robust Subspace Learning Methods2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), Oct 2021, Montreal, Canada. pp.181-188, ⟨10.1109/ICCVW54120.2021.00025⟩ |
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The Emerging Field of Graph Signal Processing for Moving Object SegmentationInternational Workshop on Frontiers of Computer Vision, IW-FCV 2021, Feb 2021, Daegu, South Korea. pp.31-45, ⟨10.1007/978-3-030-81638-4_3⟩ |
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Robust Foreground Segmentation in RGBD Data from Complex Scenes Using Adversarial NetworksInternational Workshop on Frontiers of Computer Vision, IW-FCV 2021, Feb 2021, Daegu, South Korea. pp.3-16, ⟨10.1007/978-3-030-81638-4_1⟩ |
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Moving Object Detection for Event-based Vision using Graph Spectral Clustering2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), Oct 2021, Montreal, Canada. pp.876-884, ⟨10.1109/ICCVW54120.2021.00103⟩ |
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GraphBGS: Background Subtraction via Recovery of Graph Signals2020 25th International Conference on Pattern Recognition (ICPR), Jan 2021, Milan, Italy. pp.6881-6888, ⟨10.1109/ICPR48806.2021.9412999⟩ |
Semi-Supervised Background Subtraction Of Unseen Videos: Minimization Of The Total Variation Of Graph Signals2020 IEEE International Conference on Image Processing (ICIP), Oct 2020, Abu Dhabi, United Arab Emirates. pp.3224-3228, ⟨10.1109/ICIP40778.2020.9190887⟩ |
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Estimation of the Hidden Message Length in Steganography: A Deep Learning ApproachInternational Conference on Machine Learning for Networking, MLN 2019, Dec 2019, Paris, France. pp.333-341, ⟨10.1007/978-3-030-45778-5_22⟩ |
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On the Minimization of Sobolev Norms of Time-Varying Graph Signals: Estimation of New Coronavirus Disease 2019 CasesIEEE International Workshop on Machine Learning for Signal Processing, Sep 2020, Espoo, Finland. pp.1-6, ⟨10.1109/MLSP49062.2020.9231810⟩ |
Towards an Effective Approach for Face Recognition with DCGANs Data AugmentationInternational Symposium on Visual Computing, Oct 2020, San Diego, United States. pp.463-475, ⟨10.1007/978-3-030-64556-4_36⟩ |
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Unsupervised Adversarial Learning for Dynamic Background ModelingInternational Workshop on Frontiers of Computer Vision, IW-FCV 2020, Feb 2020, Ibusuki, Japan. ⟨10.1007/978-981-15-4818-5_19⟩ |
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Dynamic Background Subtraction Using Least Square Adversarial Learning2020 IEEE International Conference on Image Processing (ICIP), Oct 2020, Abu Dhabi, United Arab Emirates. pp.3204-3208, ⟨10.1109/ICIP40778.2020.9191235⟩ |
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Dual Information-Based Background Model For Moving Object Detection2020 IEEE International Conference on Image Processing (ICIP), Oct 2020, Abu Dhabi, United Arab Emirates. pp.3219-3223, ⟨10.1109/ICIP40778.2020.9190811⟩ |
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Complete Moving Object Detection in the Context of Robust Subspace Learning2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW), Oct 2019, Seoul, South Korea. pp.661-668, ⟨10.1109/ICCVW.2019.00080⟩ |
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Moving Objects Segmentation Based on DeepSphere in Video SurveillanceInternational Symposium on Visual Computing, ISVC 2019, Oct 2019, Tahoe City, United States. pp.307-319, ⟨10.1007/978-3-030-33723-0_25⟩ |
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Robust PCA and Robust Subspace Tracking: A Comparative EvaluationIEEE Statistical Signal Processing Workshop, SSP 2018, Jun 2018, Freiburg, Germany |
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Moving Object Detection on RGB-D Videos using Graph Regularized Spatiotemporal RPCAInternational Workshop on Background learning for detection and tracking from RGBD videos, ICIAP 2017, Sep 2017, Catania, Italy |
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Improving OR-PCA via Smoothed Spatially-Consistent Low-rank Modeling for Background SubtractionACM Symposium on Applied Computing, SAC 2017, Apr 2017, Marrakech, Morocco |
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SBMI-LTD: Stationary Background Model Initialization based on Low-rank Tensor DecompositionACM Symposium on Applied Computing, SAC 2017, Apr 2017, Marrakech, Morocco |
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Superpixels based Manifold Structured Sparse RPCA for Moving Object DetectionInternational Workshop on Activity Monitoring by Multiple Distributed Sensing, BMVC 2017, Sep 2017, Londres, United Kingdom |
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Multi-Feature Fusion Based Background Subtraction For Video Sequences With Strong Background ChangesInternational Conference on Image Processing, ICIP, Sep 2017, Beijing, China |
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Motion-Aware Graph Regularized RPCA for Background Modeling of Complex Scenes,Scene Background Modeling Contest, International Conference on Pattern Recognition, ICPR 2016, Dec 2016, Cancun, Mexico |
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Online Weighted One-Class Ensemble for Feature Selection in Background/Foreground SeparationInternational Conference on Pattern Recognition, ICPR 2016, Dec 2016, Cancun, Mexico. pp.2216-2221 |
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Combining ARF and OR-PCA for Robust Background Subtraction of Noisy VideosInternational Conference in Image Analysis and Applications, ICIAP 2015, Sep 2015, Genova, Italy. pp.340 - 351, ⟨10.1007/978-3-319-23234-8_32⟩ |
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OR-PCA with MRF for Robust Foreground Detection in Highly Dynamic BackgroundsAsian Conference on Computer Vision, ACCV 2014, Nov 2015, Singapore, Singapore. pp.284 - 299, ⟨10.1007/978-3-319-16811-1_19⟩ |
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Depth extended online RPCA with spatiotemporal constraints for robust background subtractionKorea-Japan Workshop on Frontiers of Computer Vision, FCV 2015, Jan 2015, Mokpo, South Korea. pp.1 - 6, ⟨10.1109/FCV.2015.7103745⟩ |
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Comparison of Matrix Completion Algorithms for Background Initialization in VideosSBMI 2015 Workshop in conjunction with ICIAP 2015, Sep 2015, Genoa, Italy. pp.510-518, ⟨10.1007/978-3-319-23222-5_62⟩ |
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An eXtended Center-Symmetric Local Binary Pattern for Background Modeling and Subtraction in VideosInternational Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISAPP 2015, Mar 2015, Berlin, Germany. ⟨10.5220/0005266303950402⟩ |
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Online Stochastic Tensor Decomposition for Background Subtraction in Multispectral Video SequencesRSL-CV 2015 in conjunction with ICCV 2015, Dec 2015, Santiago du Chili, Chile. pp.946-953, ⟨10.1109/ICCVW.2015.125⟩ |
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Stochastic Decomposition into Low Rank and Sparse Tensor for Robust Background Subtraction6th International Conference on Imaging for Crime Prevention and Detection (ICDP 2015), Jul 2015, Londres, United Kingdom. ⟨10.1049/ic.2015.0105⟩ |
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Background Subtraction via Superpixel-Based Online Matrix Decomposition with Structured Foreground ConstraintsRSL-CV 2015 in conjunction with ICCV 2015, Dec 2015, Santiago du Chili, Chile. ⟨10.1109/ICCVW.2015.123⟩ |
OR-PCA with Dynamic Feature Selection for Robust Background SubtractionACM Symposium On Applied Computing, SAC 2015, Apr 2015, Salamanca, Spain |
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Double-constrained RPCA based on saliency maps for foreground detection in automated maritime surveillanceISBC 2015 Workshop conjunction with AVSS 2015, Aug 2015, Karlsruhe, Germany. pp.1-6, ⟨10.1109/AVSS.2015.7301753⟩ |
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Incremental and Multi-feature Tensor Subspace Learning Applied for Background Modeling and SubtractionInternational Conference on Image Analysis and Recognition, ICIAR 2014, Oct 2014, Vilamoura, Algarve, Portugal. ⟨10.1007/978-3-319-11758-4_11⟩ |
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Moving Object Detection via Robust Low Rank Matrix Decomposition with IRLS schemeInternational Symposium on Visual Computing, ISVC 2012, Jul 2012, Greece. pp.665-674 |
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Foreground Detection by Robust PCA solved via a Linearized Alternating Direction MethodInternational Conference on Image Analysis and Recognition, ICIAR 2012, Jun 2012, Portugal. pp.115-122 |
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Foreground Detection via Robust Low Rank Matrix Decomposition including Spatio-Temporal ConstraintInternational Workshop on Background Model Challenges, ACCV 2012,, Nov 2012, Daejeon, South Korea. pp.315-320 |
Foreground detection based on low-rank and block-sparse matrix decompositionInternational Conference on Image Processing (ICIP), Sep 2012, United States. pp.1225-1228 |
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Foreground Detection via Robust Low Rank Matrix Factorization including Spatial Constraint with Iterative Reweighted RegressionInternational Conference on Pattern Recognition, ICPR 2012, Nov 2012, Tsukuba, Japan. pp.2805-2808 |
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Moving Object Detection by Robust PCA solved via a Linearized Symmetric Alternating Direction MethodInternational Symposium on Visual Computing, ISVC 2012, Jul 2012, Greece. pp.427-436 |
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Background modeling via a supervised subspace learningInternational Conference on Image, Video Processing and Computer Vision, Jul 2010, Orlando, United States. pp.1-7 |
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Background Modeling via Incremental Maximum Margin CriterionACCV 2010 Workshops, Nov 2010, New Zealand. pp.394-403 |
Fuzzy Statistical Modeling of Dynamic Backgrounds for Moving Object Detection in Infrared VideosCVPR Workshop: International Workshop on Object Tracking and Classification in and Beyond the Visible Spectrum,, Jun 2009, Miami, United States. pp.60-65, ⟨10.1109/CVPR.2009.5204109⟩ |
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Fuzzy Integral for Moving Object DetectionFUZZ-IEEE 2008, Jun 2008, Hong-Kong, Hong Kong SAR China. pp.1729-1736, ⟨10.1109/FUZZY.2008.4630604⟩ |
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Foreground Detection using the Choquet Integral9th International Workshop on Image Analysis for Multimedia Interactive Services, May 2008, Klagenfurt, Austria. pp.187-190, ⟨10.1109/WIAMIS.2008.9⟩ |
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A Fuzzy Approach for Background SubtractionICIP 2008, Oct 2008, San Diego, United States. pp.2648-2651 |
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Fuzzy Foreground Detection for Infrared VideosOTCBVS 2008, Jun 2008, Anchorage, United States. pp.1-6, ⟨10.1109/CVPRW.2008.4563057⟩ |
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Type-2 Fuzzy Mixture of Gaussians Model: Application to Background ModelingISVC 2008, Dec 2008, Las Vegas, United States. pp.772-781 |
Object detection in underwater imagesCMM06, Colloque CARACTERISATION DU MILIEU MARIN, Oct 2006, Brest, France. pp.8 |
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De l'importance des méthodes de suppression de fond pour la détection d'objets mobiles dans des vidéos acquises par des cameras fixes : Etat de l'art, Nouvelles Approches et PerspectivesTraitement des images [eess.IV]. Université de La Rochelle, 2014 |