2023 - Research.com Computer Science in Portugal Leader Award
2022 - Research.com Computer Science in Portugal Leader Award
Jose M. Bioucas-Dias mostly deals with Hyperspectral imaging, Artificial intelligence, Pattern recognition, Algorithm and Optimization problem. His Hyperspectral imaging research is multidisciplinary, incorporating perspectives in Image resolution, Contextual image classification and Multispectral image. Jose M. Bioucas-Dias is interested in Pixel, which is a field of Artificial intelligence.
His Pattern recognition research integrates issues from Regression analysis, Spatial analysis and Subspace topology. His study in the fields of Augmented Lagrangian method under the domain of Algorithm overlaps with other disciplines such as Expectation–maximization algorithm. As part of one scientific family, he deals mainly with the area of Optimization problem, narrowing it down to issues related to the Computational complexity theory, and often Cut, Markov chain and Absolute phase.
Jose M. Bioucas-Dias mainly focuses on Artificial intelligence, Hyperspectral imaging, Pattern recognition, Algorithm and Pixel. His research in Artificial intelligence intersects with topics in Machine learning and Computer vision. His research integrates issues of Subspace topology and Spectral signature in his study of Hyperspectral imaging.
The Pattern recognition study combines topics in areas such as Image, Noise reduction, Multinomial logistic regression and Posterior probability. His Algorithm study combines topics from a wide range of disciplines, such as Image restoration, Mathematical optimization and Inverse problem. His Pixel study integrates concerns from other disciplines, such as Algorithm design and Noise.
His primary scientific interests are in Hyperspectral imaging, Artificial intelligence, Pattern recognition, Algorithm and Noise reduction. His Hyperspectral imaging study is focused on Remote sensing in general. His work on Artificial intelligence is being expanded to include thematically relevant topics such as Computer vision.
In general Pattern recognition, his work in Feature extraction is often linked to Matrix decomposition linking many areas of study. His Algorithm study combines topics in areas such as Inverse problem, Importance sampling, Noise, Minimum mean square error and Image restoration. His research investigates the connection between Multispectral image and topics such as Image resolution that intersect with issues in Superresolution.
His primary areas of study are Artificial intelligence, Hyperspectral imaging, Pattern recognition, Noise reduction and Multispectral image. His work deals with themes such as Spectral bands, Inverse problem and Computer vision, which intersect with Artificial intelligence. His research in Hyperspectral imaging is mostly focused on Endmember.
The study incorporates disciplines such as Shrinkage, Regularization, Spectral signature and Feature in addition to Pattern recognition. His work carried out in the field of Noise reduction brings together such families of science as Sharpening, Noise, Iterative method, Algorithm and Noise measurement. In his research, Point spread function is intimately related to Image resolution, which falls under the overarching field of Multispectral image.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches
J. M. Bioucas-Dias;A. Plaza;N. Dobigeon;M. Parente.
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (2012)
A New TwIST: Two-Step Iterative Shrinkage/Thresholding Algorithms for Image Restoration
J.M. Bioucas-Dias;M.A.T. Figueiredo.
IEEE Transactions on Image Processing (2007)
Hyperspectral Remote Sensing Data Analysis and Future Challenges
J. M. Bioucas-Dias;A. Plaza;G. Camps-Valls;P. Scheunders.
IEEE Geoscience and Remote Sensing Magazine (2013)
Fast Image Recovery Using Variable Splitting and Constrained Optimization
Manya V Afonso;José M Bioucas-Dias;Mário A T Figueiredo.
IEEE Transactions on Image Processing (2010)
Hyperspectral Subspace Identification
J.M. Bioucas-Dias;J.M.P. Nascimento.
IEEE Transactions on Geoscience and Remote Sensing (2008)
An Augmented Lagrangian Approach to the Constrained Optimization Formulation of Imaging Inverse Problems
M V Afonso;José M Bioucas-Dias;Mário A T Figueiredo.
IEEE Transactions on Image Processing (2011)
Sparse Unmixing of Hyperspectral Data
Marian-Daniel Iordache;J M Bioucas-Dias;A Plaza.
IEEE Transactions on Geoscience and Remote Sensing (2011)
Spectral–Spatial Hyperspectral Image Segmentation Using Subspace Multinomial Logistic Regression and Markov Random Fields
Jun Li;J. M. Bioucas-Dias;A. Plaza.
IEEE Transactions on Geoscience and Remote Sensing (2012)
Total Variation Spatial Regularization for Sparse Hyperspectral Unmixing
M.-D Iordache;J. M. Bioucas-Dias;A. Plaza.
IEEE Transactions on Geoscience and Remote Sensing (2012)
Majorization–Minimization Algorithms for Wavelet-Based Image Restoration
M.A.T. Figueiredo;J.M. Bioucas-Dias;R.D. Nowak.
IEEE Transactions on Image Processing (2007)
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