Dobias, M.[Martin]
* 2011: Real-time global prediction for temporally stable stereo
Dobie, G.
* 2020: Breast Cancer: Model Reconstruction and Image Registration From Segmented Deformed Image Using Visual and Force Based Analysis
Dobie, M.
* 1998: Content-Based Navigation from Images
Dobie, M.R.
* 1991: Data Structures for Image Processing in C
* 1994: Extracting curvilinear features from remotely sensed images using minimum cost path techniques
Dobigeon, N.
* 2009: Hierarchical Bayesian Sparse Image Reconstruction With Application to MRFM
* 2010: Bayesian Estimation of Linear Mixtures Using the Normal Compositional Model: Application to Hyperspectral Imagery
* 2010: Implementation Strategies for Hyperspectral Unmixing Using Bayesian Source Separation
* 2011: Enhancing Hyperspectral Image Unmixing With Spatial Correlations
* 2011: Nonlinear Unmixing of Hyperspectral Images Using a Generalized Bilinear Model
* 2012: Segmentation of Skin Lesions in 2-D and 3-D Ultrasound Images Using a Spatially Coherent Generalized Rayleigh Mixture Model
* 2012: Semi-Blind Sparse Image Reconstruction With Application to MRFM
* 2012: Supervised Nonlinear Spectral Unmixing Using a Postnonlinear Mixing Model for Hyperspectral Imagery
* 2013: Adaptive Markov Random Fields for Joint Unmixing and Segmentation of Hyperspectral Images
* 2013: Estimating the Granularity Coefficient of a Potts-Markov Random Field Within a Markov Chain Monte Carlo Algorithm
* 2013: Nonlinearity Detection in Hyperspectral Images Using a Polynomial Post-Nonlinear Mixing Model
* 2014: Bayesian fusion of multispectral and hyperspectral images with unknown sensor spectral response
* 2014: Computing the Cramer-Rao Bound of Markov Random Field Parameters: Application to the Ising and the Potts Models
* 2014: Inverse problem formulation for regularity estimation in images
* 2014: Joint Bayesian Estimation of Close Subspaces from Noisy Measurements
* 2014: Nonlinear Unmixing of Hyperspectral Images: Models and Algorithms
* 2014: Residual Component Analysis of Hyperspectral Images: Application to Joint Nonlinear Unmixing and Nonlinearity Detection
* 2014: Unsupervised Post-Nonlinear Unmixing of Hyperspectral Images Using a Hamiltonian Monte Carlo Algorithm
* 2015: Fast Fusion of Multi-Band Images Based on Solving a Sylvester Equation
* 2015: Hyperspectral and Multispectral Image Fusion Based on a Sparse Representation
* 2015: Nonlinear Hyperspectral Unmixing With Robust Nonnegative Matrix Factorization
* 2015: Toward Fast Transform Learning
* 2015: Unsupervised Unmixing of Hyperspectral Images Accounting for Endmember Variability
* 2016: Detection and Correction of Glitches in a Multiplexed Multichannel Data Stream: Application to the MADRAS Instrument
* 2016: Fast Single Image Super-Resolution Using a New Analytical Solution for L_2- L_2 Problems
* 2016: Multiband Image Fusion Based on Spectral Unmixing
* 2016: Online Unmixing of Multitemporal Hyperspectral Images Accounting for Spectral Variability
* 2016: R-FUSE: Robust Fast Fusion of Multiband Images Based on Solving a Sylvester Equation
* 2018: Detecting Changes Between Optical Images of Different Spatial and Spectral Resolutions: A Fusion-Based Approach
* 2018: Hyperspectral Image Unmixing With LiDAR Data-Aided Spatial Regularization
* 2019: Coupled dictionary learning for unsupervised change detection between multimodal remote sensing images
* 2019: Factor Analysis of Dynamic PET Images: Beyond Gaussian Noise
* 2019: Hierarchical Bayesian image analysis: From low-level modeling to robust supervised learning
* 2019: Non-linear unmixing of hyperspectral images using multiple-kernel self-organising maps
* 2019: Partially Asynchronous Distributed Unmixing of Hyperspectral Images
* 2020: Hierarchical Sparse Nonnegative Matrix Factorization for Hyperspectral Unmixing with Spectral Variability
* 2020: Matrix Cofactorization for Joint Spatial-Spectral Unmixing of Hyperspectral Images
* 2021: Provably Robust Blind Source Separation of Linear-Quadratic Near-Separable Mixtures
* 2022: Informed Spatial Regularizations For Fast Fusion Of Astronomical Images
* 2023: Guided Deep Generative Model-Based Spatial Regularization for Multiband Imaging Inverse Problems
* 2023: Probabilistic Simplex Component Analysis by Importance Sampling
Includes: Dobigeon, N. Dobigeon, N.[Nicolas]
41 for Dobigeon, N.
Dobis, L.[Lukas]
* 2022: CloudSatNet-1: FPGA-Based Hardware-Accelerated Quantized CNN for Satellite On-Board Cloud Coverage Classification