Policy packaging or policy patching? The development of complex energy efficiency policy mixes

The ambition of energy policy has long been to reduce carbon emissions, secure energy supply and provide affordable energy services. In recent years an increasing number of policy instruments has been introduced to promote energy efficiency across the EU. While previous research has analysed the effectiveness of individual policy instruments and their impact on the diffusion of particular energy efficient technologies or practices, our analysis takes a broader view and examines the mix of existing policies aimed at stimulating reductions in energy use. The empirical focus of the paper is on policy goals and instruments aimed at stimulating energy efficiency in buildings in Finland and the United Kingdom. We trace the development of the policy mixes during 2000- 2014 and analyse their emerging overall characteristics. The analysis is based on a mapping of policy goals and instruments, documentary analysis and semi-structured interviews with stakeholders. We find that both countries have increasingly complex policy mixes, encompassing a variety of goals and instruments and make use of a range of different instrument types to encourage users to reduce energy consumption. Despite the shared EU influence, the way in which the policy mixes have evolved in both countries were found to be quite different

De odyssee van den. Knjas poteiafcin. Naverteld uit net dagboeic van Kir ill, lid van het revolutionair Scheepsfcomitee.

OPLADEN-RUG0

Image Statistics based on Diffeomorphic Matching

We propose a new approach to deal with the first and second order statistics of a set of images. These statistics take into account the images characteristic deformations and their variations in intensity. The central algorithm is based on non-supervised diffeomorphic image matching (without landmarks or human intervention). As they convey the notion of the mean shape and colors of an object and the one of its common variations, such statistics of sets of images may be relevant in the context of object recognition, both in the segmentation of any of its representations and in the classification of them. The proposed approach has been tested on a small database of face images to compute a mean face and second order statistics. The results are very encouraging since, wheras the algorithm does not need any human intervention and is not specific to face image databases, the mean image looks like a real face and the characteristic modes of variation (deformation and intensity changes) are sensible

Imaging Methods for MEG/EEG Inverse Problem

Recovering electrical activity of the brain from MEG/EEG measurements is known as the MEEG inverse problem. It is an ill-posed problem in several senses. One is that there is further less data observed than data to recover. One way to address this issue is to search for regular solutions. We present here a framework for applying image processing filtering techniques to the MEEG inverse problem. Exprimentations are presented on synthetic dara and validation is carried out on one real MEG data set

A Benchmark Framework for Multiregion Analysis of Vesselness Filters

International audienceThis paper is an updated version of [1], following the correction of numerical errors. Vessel enhancement (aka vesselness) filters, are part of angiographic image processing for more than twenty years. Their popularity comes from their ability to enhance tubular structures while filtering out other structures, especially as a preliminary step of vessel segmentation. Choosing the right vesselness filter among the many available can be difficult, and their parametrization requires an accurate understanding of their underlying concepts and a genuine expertise. In particular, using default parameters is often not enough to reach satisfactory results on specific data. Currently, only few benchmarks are available to help the users choosing the best filter and its parameters for a given application. In this article, we present a generic framework to compare vesselness filters. We use this framework to compare seven gold standard filters. Our experiments are performed on three public datasets: the hepatic Ircad dataset (CT images), the Bullit dataset (brain MRA images) and the synthetic VascuSynth dataset. We analyse the results of these seven filters both quantitatively and qualitatively. In particular, we assess their performances in key areas: the organ of interest, the whole vascular network neighbourhood and the vessel neighbourhood split into several classes, based on their diameters. We also focus on the vessels bifurcations, which are often missed by vesselness filters. We provide the code of the benchmark, which includes upto-date C++ implementations of the seven filters, as well as the experimental setup (parameter optimization, result analysis, etc.). An online demonstrator is also provided to help the community apply and visually compare these vesselness filters

Vector-Valued Image Regularization with PDE's: A Common Framework for . . .

This report addresses the problem of vector-valued image regularization with variational methods and PDE's. From the study of existing global and local formalisms, we propose a new framework that unies a large number of previous methods within a generic local formulation. On one hand, resulting equations are more adapted to analyze the local geometric behaviors of the diusion processes. On the other hand, it can be used to design a new regularization PDE that takes important local smoothing properties into account. Specic numerical schemes are also naturally emerging from this formulation. Finally, we illustrate the capability of our approach to deal with classical image processing applications, such as color image restoration, inpainting, magnication and ow visualization