2023 - Research.com Computer Science in Portugal Leader Award
2022 - Research.com Computer Science in Portugal Leader Award
2010 - IEEE Fellow For contributions to pattern recognition and computer vision
2008 - Fellow of the International Association for Pattern Recognition (IAPR) For contributions to unsupervised and supervised learning, image analysis, and wavelet-based image restoration.
The scientist’s investigation covers issues in Artificial intelligence, Pattern recognition, Algorithm, Mathematical optimization and Deconvolution. His Artificial intelligence research integrates issues from Machine learning and Expectation–maximization algorithm. Mário A. T. Figueiredo has researched Pattern recognition in several fields, including Prior probability, Bayesian probability, Cluster analysis and Parametric statistics.
Mário A. T. Figueiredo focuses mostly in the field of Algorithm, narrowing it down to topics relating to Wavelet transform and, in certain cases, Convolution. In the field of Mathematical optimization, his study on Optimization problem overlaps with subjects such as Basis pursuit. His Deconvolution research includes themes of Regularization, Inpainting and Inverse problem.
Mário A. T. Figueiredo mainly investigates Artificial intelligence, Pattern recognition, Algorithm, Machine learning and Cluster analysis. His biological study spans a wide range of topics, including Computer vision and Expectation–maximization algorithm. He has included themes like Mathematical optimization, Noise reduction and Image restoration in his Algorithm study.
His Mathematical optimization study often links to related topics such as Deconvolution. The Image restoration study combines topics in areas such as Wavelet, Wavelet transform, Inverse problem and Convex optimization. His Cluster analysis study combines topics from a wide range of disciplines, such as Norm and Data mining.
His main research concerns Artificial intelligence, Algorithm, Pattern recognition, Noise reduction and Image. His Algorithm research includes elements of Probability density function, Importance sampling, Image processing, Noise and Generalization. His Pattern recognition research is multidisciplinary, relying on both Noise, Filter, Bayesian probability and Random field.
His Noise reduction research incorporates themes from Poisson distribution, Iterative method and Hyperspectral imaging. Inpainting is closely connected to Image restoration in his research, which is encompassed under the umbrella topic of Mixture model. His research in Deblurring intersects with topics in Convolution and Inverse problem.
His primary areas of study are Artificial intelligence, Pattern recognition, Algorithm, Mathematical optimization and Deblurring. His Artificial intelligence study incorporates themes from Generalization and Computer vision. His studies in Pattern recognition integrate themes in fields like Prior probability, Cluster analysis, Expectation–maximization algorithm, Matrix decomposition and Generative model.
His Iterative method study in the realm of Algorithm interacts with subjects such as Noise measurement. In his research, Scaling is intimately related to Gradient descent, which falls under the overarching field of Mathematical optimization. His work carried out in the field of Deblurring brings together such families of science as Noise reduction and Inverse problem.
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.
Gradient Projection for Sparse Reconstruction: Application to Compressed Sensing and Other Inverse Problems
M.A.T. Figueiredo;R.D. Nowak;S.J. Wright.
IEEE Journal of Selected Topics in Signal Processing (2007)
Unsupervised learning of finite mixture models
M.A.T. Figueiredo;A.K. Jain.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2002)
Sparse Reconstruction by Separable Approximation
S.J. Wright;R.D. Nowak;M.A.T. Figueiredo.
IEEE Transactions on Signal Processing (2009)
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)
An EM algorithm for wavelet-based image restoration
M.A.T. Figueiredo;R.D. Nowak.
IEEE Transactions on Image Processing (2003)
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)
Image classification for content-based indexing
A. Vailaya;M.A.T. Figueiredo;A.K. Jain;Hong-Jiang Zhang.
IEEE Transactions on Image Processing (2001)
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 multinomial logistic regression: fast algorithms and generalization bounds
B. Krishnapuram;L. Carin;M.A.T. Figueiredo;A.J. Hartemink.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2005)
On the Role of Sparse and Redundant Representations in Image Processing
Michael Elad;Mario A T Figueiredo;Yi Ma.
Proceedings of the IEEE (2010)
If you think any of the details on this page are incorrect, let us know.
We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below: