Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page
Demo Data from SIGIR 2026 / reviewers in the wild / expert

José Luis Sanz

dblp:14/9233 · DBLP profile ↗
← Back
2ranked-venue papers
0as first author
0since 2021 · last 2011
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Graphics, computer vision, multimedia, augmented reality and games · 2

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer graphics and multimedia
1 paper
Image and video processing · 100%
Artificial intelligence
1 paper
Probabilistic and Bayesian machine learning · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Image and video processing › image decomposition › image separation
image source separation		 0.112010
Adaptive Langevin Sampler for Separation of t-Distribution Modelled Astrophysical Maps · IEEE Trans. Image Process. 2010
Machine learning › Probabilistic and Bayesian machine learning › monte carlo methods › markov chain monte carlo
langevin dynamics		 0.012010
Adaptive Langevin Sampler for Separation of t-Distribution Modelled Astrophysical Maps · IEEE Trans. Image Process. 2010
Machine learning › Probabilistic and Bayesian machine learning › monte carlo methods
markov chain monte carlo		 0.012010
Adaptive Langevin Sampler for Separation of t-Distribution Modelled Astrophysical Maps · IEEE Trans. Image Process. 2010

Methods — techniques the papers use, named apart from their topics

student's t-distribution priors · 0.2langevin stochastic equation · 0.2MCMC · 0.2
YearPublicationVenuePosition
2011 Filter design for the detection/estimation of the modulus of a vector: Application to polarization data
Francisco Argüeso, José Luis Sanz, Diego Herranz
 Signal Process.2
2010 Adaptive Langevin Sampler for Separation of t-Distribution Modelled Astrophysical Maps
abstract
We propose to model the image differentials of astrophysical source maps by Student's t-distribution and to use them in the Bayesian source separation method as priors. We introduce an efficient Markov Chain Monte Carlo (MCMC) sampling scheme to unmix the astrophysical sources and describe the derivation details. In this scheme, we use the Langevin stochastic equation for transitions, which enables parallel drawing of random samples from the posterior, and reduces the computation time significantly (by two orders of magnitude). In addition, Student's t-distribution parameters are updated throughout the iterations. The results on astrophysical source separation are assessed with two performance criteria defined in the pixel and the frequency domains.
Koray Kayabol, Ercan E. Kuruoglu, José Luis Sanz, Bülent Sankur, Emanuele Salerno, Diego Herranz
 IEEE Trans. Image Process.3