Fablet, R.
* 1998: Motion Characterization from Temporal Cooccurrences of Local Motion-Based Measures for Video Indexing
* 1999: Moving Object Detection in Color Image Sequences using Region-Level Graph Labeling
* 2000: Non Parametric Statistical Analysis of Scene Activity for Motion-Based Video Indexing and Retrieval
* 2000: Statistical Motion-based Object Indexing Using Optic Flow Field
* 2001: Motion Recognition Using Spatio-temporal Random Walks in Sequence of 2D Motion-related Measurements
* 2001: MRF-based Moving Object Detection from MPEG Coded Video
* 2001: Non-Parametric Motion Activity Analysis for Statistical Retrieval with Partial Query
* 2001: Non-Parametric Motion Recognition Using Temporal Multiscale Gibbs Models
* 2002: Automatic Detection and Tracking of Human Motion with a View-Based Representation
* 2002: Nonparametric motion characterization using causal probabilistic models for video indexing and retrieval
* 2003: Motion recognition using nonparametric image motion models estimated from temporal and multiscale co-occurrence statistics
* 2003: Robust statistical registration of 3D ultrasound images using texture information
* 2003: Robust time-frequency model estimation in Otolith images for fish age and growth analysis
* 2004: Automatic fish age estimation from otolith images using statistical learning
* 2004: Automatic morphological detection of otolith nucleus
* 2004: Dense Estimation of Motion and Appearance in Layers, The
* 2004: Multiscale segmentation of textured sonar images using cooccurence statistics
* 2005: Variational Multi-Wavelet Restoration of Noisy Images
* 2006: Automatic morphological detection of otolith nucleus
* 2006: Interpolating Orientation Fields: An Axiomatic Approach
* 2006: Orientation Interpolation and Applications
* 2006: Variational Level-Set Reconstruction of Accretionary Morphogenesis from Images
* 2008: 2D Image-based reconstruction of shape deformation of biological structures using a level-set representation
* 2008: Weakly supervised learning using proportion-based information: An application to fisheries acoustics
* 2009: Combining image-level and object-level inference for weakly supervised object recognition. Application to fisheries acoustics
* 2009: Seabed Segmentation Using Optimized Statistics of Sonar Textures
* 2009: Shape geodesics for boundary-based object recognition and retrieval
* 2010: Spatial Statistics of Visual Keypoints for Texture Recognition
* 2010: Variational fronts tracking in sea surface temperature images
* 2010: Variational Region-Based Segmentation Using Multiple Texture Statistics
* 2010: Variational shape matching for shape classification and retrieval
* 2010: Weakly Supervised Classification of Objects in Images Using Soft Random Forests
* 2011: Multi-resolution missing data interpolation in SST image series
* 2011: Multivariate log-Gaussian Cox models of elementary shapes for recognizing natural scene categories
* 2011: Object recognition using proportion-based prior information: Application to fisheries acoustics
* 2011: Visual textures as realizations of multivariate log-Gaussian Cox processes
* 2012: Keypoint-Based Analysis of Sonar Images: Application to Seabed Recognition
* 2013: Geodesics-Based Image Registration: Applications To Biological And Medical Images Depicting Concentric Ring Patterns
* 2013: Spatio-temporal segmentation and estimation of ocean surface currents from satellite sea surface temperature fields
* 2014: Multiscale Analysis of Geometric Planar Deformations: Application to Wild Animal Electronic Tracking and Satellite Ocean Observation Data
* 2014: Ocean surface current retrieval using a non homogeneous Markov-switching multi-regime model
* 2014: Segmentation of Mesoscale Ocean Surface Dynamics Using Satellite SST and SSH Observations
* 2015: Missing data super-resolution using non-local and statistical priors
* 2015: Non-homogeneous priors in a Bayesian latent class model for ocean color inversion
* 2015: Non-parametric Ensemble Kalman methods for the inpainting of noisy dynamic textures
* 2016: Convolutional Neural Networks for object recognition on mobile devices: A case study
* 2016: Deep learning for ocean remote sensing: an application of convolutional neural networks for super-resolution on satellite-derived SST data
* 2016: Texture synthesis guided by a low-resolution image
* 2017: Data-driven assimilation of irregularly-sampled image time series
* 2017: Locally-adapted convolution-based super-resolution of irregularly-sampled ocean remote sensing data
* 2018: Improving Mesoscale Altimetric Data From a Multitracer Convolutional Processing of Standard Satellite-Derived Products
* 2018: Neural Network Based Kalman Filters for the Spatio-Temporal Interpolation of Satellite-Derived Sea Surface Temperature
* 2018: Spatio-Temporal Interpolation of Cloudy SST Fields Using Conditional Analog Data Assimilation
* 2018: SWOT Spatial Scales in the Western Mediterranean Sea Derived from Pseudo-Observations and an Ad Hoc Filtering
* 2019: Data-Driven Interpolation of Sea Level Anomalies Using Analog Data Assimilation
* 2019: Ship Identification and Characterization in Sentinel-1 SAR Images with Multi-Task Deep Learning
* 2020: Intercomparison of Data-Driven and Learning-Based Interpolations of Along-Track Nadir and Wide-Swath SWOT Altimetry Observations
* 2020: Neural Network Approaches to Reconstruct Phytoplankton Time-Series in the Global Ocean
* 2020: Residual Networks as Flows of Diffeomorphisms
* 2021: Data-Driven Interpolation of Sea Surface Suspended Concentrations Derived from Ocean Colour Remote Sensing Data
* 2021: Unsupervised Reconstruction of Sea Surface Currents from AIS Maritime Traffic Data Using Trainable Variational Models
* 2022: End-to-End Neural Interpolation of Satellite-Derived Sea Surface Suspended Sediment Concentrations
* 2022: GeoTrackNet: A Maritime Anomaly Detector Using Probabilistic Neural Network Representation of AIS Tracks and A Contrario Detection
* 2022: Neural Network Approaches to Reconstruct Phytoplankton Time-Series in the Global Ocean
* 2022: Semantic Segmentation of Metoceanic Processes Using SAR Observations and Deep Learning
Includes: Fablet, R. Fablet, R.[Ronan]
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