Multi-frame Super Resolution based on Sparse Coding

The high fidelity reconstruction of compressed and low-resolution magnetic resonance (MR) data is essential for simultaneously improving patient care, accuracy in diagnosis and quality in clinical research. Sponsored by the Royal Society through the Newton Mobility Grant Scheme, a half-day workshop on reconstruction schemes for MR data was held on the 17th of August 2016 to discuss new ideas from related research fields that could be useful to overcome the shortcomings of the conventional reconstruction methods that have been evaluated up to date. Participants were 21 university students, computer scientists, image analysts, engineers and physicists from institutions from 6 different countries. This presentation describes two pipelines for achieving higher image resolution based on sparse coding

Dynamic tree-structured sparse RPCA via column subset selection for background modeling and foreground detection

Video analysis often begins with background subtraction, which consists of creation of a background model that allows distinguishing foreground pixels. Recent evaluation of background subtraction techniques demonstrated that there are still considerable challenges facing these methods. Processing per-pixel basis from the background is not only time-consuming but also can dramatically affect foreground region detection, if region cohesion and contiguity is not considered in the model. We present a new method in which we regard the image sequence to be made up of the sum of a low-rank background matrix and a dynamic tree-structured sparse matrix, and solve the decomposition using our approximated Robust Principal Component Analysis method extended to handle camera motion. Furthermore, to reduce the curse of dimensionality and scale, we introduce a low-rank background modeling via Column Subset Selection that reduces the order of complexity, decreases computation time, and eliminates the huge storage need for large videos

Workshop on reconstruction schemes for magnetic resonance data: summary of findings and recommendations

[EN] The high fidelity reconstruction of compressed and low-resolution magnetic resonance (MR) data is essential for simultaneously improving patient care, accuracy in diagnosis and quality in clinical research. Sponsored by the Royal Society through the Newton Mobility Grant Scheme, we held a half-day workshop on reconstruction schemes for MR data on the 17 of August 2016 to discuss new ideas from related research fields that could be useful to overcome the shortcomings of the conventional reconstruction methods that have been evaluated up to date. Participants were 21 university students, computer scientists, image analysts, engineers and physicists from institutions from 6 different countries. The discussion evolved around exploring new avenues to achieve high resolution, high quality and fast acquisition of MR imaging. In this article, we summarise the topics covered throughout the workshop and make recommendations for ongoing and future works.The workshop was sponsored by the Royal Society through the Newton Mobility Grant NI150340 to E.O.-I. and M.C.V.H. M.C.V.H. is funded by Row Fogo Charitable Trust; R.O.R. is funded by the Ministry of Education, Research, Culture and Sports of Valencia (Spain) under the programme VALi+d 2015; E.O.-I. is funded by Bogazici University, and the research presented at the workshop was supported by TUBITAK Career Development Grant 112E036, EU Marie Curie IRG Grant FP7-PEOPLE-RG-2009 256528, Tubitak 1001 Research Grant 115S219, and Bogazici University BAP Grant 10844SUP; I.M. is funded by core funds from the University of Edinburgh, including the Scottish Funding Council; A.J.V.B. is funded by the Marie Sklodowska Curie scholarship which is part of the European Union's H2020 Framework Programme (H2020-MSCA-ITN-2014) under the grant agreement number 642685 MacSeNet; and V.G.O. and P.F. are privately funded.Ozturk-Isik, E.; Marshall, I.; Filipiak, P.; Benjamin, AJV.; Ones, VG.; Ortiz-Ramón, R.; Valdes Hernandez, MDC. (2017). Workshop on reconstruction schemes for magnetic resonance data: summary of findings and recommendations. Royal Society Open Science. 4(2):1-4. https://doi.org/10.1098/rsos.160731144

Estimating multilevel models for categorical data via generalized least squares

Montero et al. (2002) proposed a strategy to formulate multilevel models related to a contingency table sample. This methodology is based on the application of the general linear model to hierarchical categorical data. In this paper we applied the method to a multilevel logistic regression model using simulated data. We find that the estimates of the random parameters are inadmissible in some circumstances; large bias and negative estimates of the variance are expected for unbalanced data sets. In order to correct the estimates we propose to use a numerical technique based on the Truncated Singular Value Decomposition (TSVD) in the solution of the problem of generalized least squares associated to the estimation of the random parameters. Finally a simulation study is presented to shows the effectiveness of this technique for reducing the bias of the estimates.Montero, Castell & Ojeda (2002) propusieron una estrategia para formular modelos multinivel para tablas de contingencia basada en la aplicación del modelo lineal general a datos categóricos jerárquicos. Aplicando el método a un modelo de regresión logística multinivel con datos simulados, encontramos que las estimaciones de los parámetros aleatorios son inadmisibles en ciertas situaciones, con sesgos grandes y estimaciones negativas de la varianza cuando los conjuntos de datos son desbalanceados. Para corregir los estimadores proponemos una técnica basada en descomposición de valores singulares truncados en la solución de mínimos cuadrados generalizados para estimar los parámetros aleatorios. Mediante simulación mostramos la efectividad de la técnica en cuanto a la reducción del sesgo de los estimadores

ESTIMATING MULTILEVEL MODELS FOR CATEGORICAL DATA VIA GENERALIZED LEAST SQUARES ESTIMACION DE MODELOS MULTINIVEL PARA DATOS CATEGORICOS A TRAVES DE MINIMOS CUADRADOS GENERALIZADOS

Montero et al. (2002) proposed a strategy to formulate multilevel models related to a contingency table sample. This methodology is based on the application of the general linear model to hierarchical categorical data. In this paper we applied the method to a multilevel logistic regression model using simulated data. We find that the estimates of the random parameters are inadmissible in some circumstances; large bias and negative estimates of the variance are expected for unbalanced data sets. In order to correct the estimates we propose to use a numerical technique based on the Truncated Singular Value Decomposition (TSVD) in the solution of the problem of generalized least squares associated to the estimation of the random parameters. Finally a simulation study is presented to shows the effectiveness of this technique for reducing the bias of the estimates.Montero, Castell & Ojeda (2002) propusieron una estrategia para formular modelos multinivel para tablas de contingencia basada en la aplicación del modelo lineal general a datos categóricos jerárquicos. Aplicando el método a un modelo de regresión logística multinivel con datos simulados, encontramos que las estimaciones de los parámetros aleatorios son inadmisibles en ciertas situaciones, con sesgos grandes y estimaciones negativas de la varianza cuando los conjuntos de datos son desbalanceados. Para corregir los estimadores proponemos una técnica basada en descomposición de valores singulares truncados en la solución de mínimos cuadrados generalizados para estimar los parámetros aleatorios. Mediante simulación mostramos la efectividad de la técnica en cuanto a la reducción del sesgo de los estimadores

Estimacion de modelos multinivel para datos categoricos a traves de minimos cuadrados generalizados

Montero, Castell and amp; Ojeda (2002) propusieron una estrategia para formular modelos multinivel para tablas de contingencia basada en la aplicación del modelo lineal general a datos categóricos jerárquicos. Aplicando el método a un modelo de regresión logística multinivel con datos simulados, encontramos que las estimaciones de los parámetros aleatorios son inadmisibles en ciertas situaciones, con sesgos grandes y estimaciones negativas de la varianza cuando los conjuntos de datos son desbalanceados. Para corregir los estimadores proponemos una técnica basada en descomposición de valores singulares truncados en la solución de mínimos cuadrados generalizados para estimar los parámetros aleatorios. Mediante simulación mostramos la efectividad de la técnica en cuanto a la reducción del sesgo de los estimadores.Montero et al. (2002) proposed a strategy to formulate multilevel models related to a contingency table sample. This methodology is based on the application of the general linear model to hierarchical categorical data. In this paper we applied the method to a multilevel logistic regression model using simulated data. We find that the estimates of the random parameters are inadmissible in some circumstances; large bias and negative estimates of the variance are expected for unbalanced data sets. In order to correct the estimates we propose to use a numerical technique based on the Truncated Singular Value Decomposition (TSVD) in the solution of the problem of generalized least squares associated to the estimation of the random parameters. Finally a simulation study is presented to shows the effectiveness of this technique for reducing the bias of the estimates

Calculating Traffic based on Road Sensor Data

Road sensors gather a lot of statistical data about traffic. In this paper, we discuss how a measure for the amount of traffic on the roads can be derived from this data, such that the measure is independent of the number and placement of sensors, and the calculations can be performed quickly for large amounts of data. We discuss how a graph of the road sensors can be constructed, and how the number of cars and car-kilometers can be estimated on this graph. Further, methods for dealing with missing data are presented, and the benefits of principal component analysis are discussed