Listar Institute of New Imaging Technologies (INIT) por título
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Space.time interpolation of daily air temperatures
UCLA Department of Statistics (2012)We propose a model to describe the mean function as well as the spatio-temporal covariance structure of 15 years of both maximum and minimum daily temperature data from 190 stations throughout the region of Catalonia ... -
Sparse Multi-modal probabilistic Latent Semantic Analysis for Single-Image Super-Resolution
Elsevier (2018)This paper presents a novel single-image super-resolution (SR) approach based on latent topics in order to take advantage of the semantics pervading the topic space when super-resolving images. Image semantics has shown ...
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Spatial Cox processes in an infinite-dimensional framework
Sociedad de Estadística e Investigación Operativa (Spanish Society of Statistics and Operations Research) (2021-04-29)We introduce a new class of spatial Cox processes driven by a Hilbert-valued randomlog-intensity. We adopt a parametric framework in the spectral domain, to estimateits spatial functional correlation structure. Specifically, ... -
Spatial pattern analysis using hybrid models: an application to the Hellenic seismicity
Springer Verlag (2017)Earthquakes are one of the most destructive natural disasters and the spatial distribution of their epi- centres generally shows diverse interaction structures at different spatial scales. In this paper, we use a ...
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Spatial pattern modelling of wildfires in Catalonia, Spain 2004–2008
Elsevier (2013)The paper has three objectives: firstly, to evaluate how the extent of clustering in wildfires differs across the years they occurred; secondly, to analyse the influence of covariates on trends in the intensity of wildfire ...
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Spatial point processes and neural networks: A convenient couple
Elsevier (2022)Different spatial point process models and techniques have been developed in the past decades to facilitate the statistical analysis of spatial point patterns. However, in some cases the spatial point process methodology ...
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Spatial recurrences for pedestrian classification
Springer U.S. (2012-09)In this work, a framework is proposed for pedestrian classification based on spatial recurrences in the form of recurrence plots. This representation is more general and potentially more discriminative than a histogram of ...
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Spatial threshold exceedance analysis through marked point processes
Wiley- Blackwell (2012)Indicators of recurrence, persistence and, in general, distribution patterns of extremal events defined by random field threshold exceedances provide relevant information on critical phenomena for risk assessment. Such ...
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Spatio-temporal analysis with short- and long-memory dependence: a state-space approach
Springer Berlin Heidelberg (2018)This paper deals with the estimation and prediction problems of spatio-temporal processes by using state-space methodology. The spatio-temporal process is represented through an infinite moving average decomposition. This ...
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Spatio-temporal evolution modeling of environmental and natural phenomena
UCLA Department of Statistics (2012)This short paper introduces the special issue containing six selected papers coming from the International Workshop on Spatio-temporal Modeling (METMA V) held in San- tiago de Compostela (Spain), from 30 June to 2 July 2010.
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Spatio-temporal modeling of traffic accidents incidence on urban road networks based on an explicit network triangulation
Taylor and Francis Group (2022-07-29)Traffic deaths and injuries are one of the major global public health concerns. The present study considers accident records in an urban environment to explore and analyze spatial and temporal in the incidence of road ...
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Spatio-temporal point patterns on linear networks: Pseudo-separable intensity estimation
Elsevier (2020)Aside from reviewing different intensity estimation schemesfor point processes on linear networks, this paper introducestwo Voronoi-based intensity estimation approaches for spatio-temporal linear network point processes. ...
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Spatio-temporal prediction of Baltimore crime events using CLSTM neural networks
IEEE (2020-11-09)Crime activity in many cities worldwide causes significant damages to the lives of victims and their surrounding communities. It is a public disorder problem, and big cities experience large amounts of crime events. ...
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Spatio-temporal small area surveillance of the COVID-19 pandemic
Elsevier (2022)The emergence of COVID-19 requires new effective tools for epidemiological surveillance. Spatio-temporal disease mapping models, which allow dealing with small units of analysis, are a priority in this context. These models ...
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Spatio-temporal statistical methods in environmental and biometrical problems
Elsevier (2017)This is the editorial letter for the Special Issue dedicated to the VIII International Workshop on Spatio-temporal Modelling (METMAVIII) which took place in Valencia (Spain) from 1 to 3 June 2016, and to the second ...
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Spatio-temporal stochastic modelling of environmental hazards
Copyright © 2015 Elsevier (2015-09)This is the editorial letter for the Special Issue dedicated to the joint VII International Workshop on Spatio-temporal Modelling (METMAVII) and the 2014 meeting of the research group for Statistical Applications to ...
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Spatiotemporal Prediction of Nitrogen Dioxide Based on Graph Neural Networks
Springer (2022-11-10)Air quality prediction, especially spatiotemporal prediction, is still a challenging issue. Considering the impact of numerous factors on air quality causes difficulties in integrating these factors in a spatiotemporal ...
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Spatio‐temporal classification in point patterns under the presence of clutter.
Wiley (2019-08-23)We consider the problem of detection of features in the presence of clutter forspatio-temporal point patterns. In previous studies, related to the spatial context,Kth nearest-neighbor distances to classify points between ...
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Spectral–Spatial Pixel Characterization Using Gabor Filters for Hyperspectral Image Classification
Institute of Electrical and Electronics Engineers (IEEE) (2013)This paper presents a spectral-spatial pixel characterization method for hyperspectral images. The characterization is based on textural features obtained using Gabor filters over a selected set of spectral bands. This ...
