Advances in Imaging and Electron Physics (Adv Imag Electron Phys )
Description
 Impact factor0.71Show impact factor historyImpact factorYear
 5year impact0.88
 Cited halflife8.10
 Immediacy index0.13
 Eigenfactor0.00
 Article influence0.43
 Other titlesAdvances in imaging and electron physics, Imaging and electron physics
 ISSN10765670
 OCLC30535280
 Document typeJournal / Magazine / Newspaper
Publications in this journal
 Advances in Imaging and Electron Physics 01/2014; 181:125208.
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ABSTRACT: We present variational models to perform texture analysis and/or extraction for image processing. We focus on second order decomposition models. Variational decomposition models have been studied extensively during the past decades. The most famous one is the RudinOsherFatemi model. We first recall most classical first order models . Then we deal with second order ones : we detail the mathematical framework, theoretical models and numerical implementation. We end with two 3D applications. Eventually, an appendix includes the mathematical tools that are used to perfom this study and Matlab codes are provided.Advances in Imaging and Electron Physics 01/2014; 
Article: Logarithmic Wavelets
Advances in Imaging and Electron Physics 01/2014; 183:4198.  [Show abstract] [Hide abstract]
ABSTRACT: The present paper deals with image segmentation, which constitutes a crucial step in image processing. In fact, the initial grey levels number is generally too large to permit the analysis in good conditions of the considered image and it is necessary to define regions (segments) whose pixels possess some properties in common, in terms of homogeneity, entropy, texture… The segmentation quality is also linked to the pertinence of boundaries separating regions (high level of contrast for example). To address this segmentation goal, a lot of methods exist, generally depending on the choice of some arbitrary tools like metrics, similarity or homogeneity parameters and sometimes on an a priori knowledge concerning the desired number of classes. We have decided to locate our study in the LIP (Logarithmic Image Processing) framework because of this Model compatibility with the Human Visual System. First we propose LIP versions of classical algorithms like multithresholding, kmeans and region growing (Part 2 and Part 3). For this last technique, we present a “systolic” approach. A special highlight is given on Hierarchical classifications (Part 4), because they suppress some subjective initial hypotheses concerning for example:  the moment where a region becomes inhomogeneous and must be divided  what is the number of significant classes present in the studied image In fact, such methods have the advantage of producing on one hand all the possible segmentations and on the other hand a “cost” function based on an ultrametric concept which permits to decide what are the most pertinent levels of classification. This 4th part of the paper ends with a novel “Gravitational Clustering” algorithm starting from the universal attraction law of Newton.Advances in Imaging and Electron Physics 01/2013; 177:144.  [Show abstract] [Hide abstract]
ABSTRACT: A modern scanning transmission electron microscope (STEM) fitted with an energydispersive Xray spectroscopy (EDS) system can quickly and easily produce spectrum image (SI) datasets that contain so much information (hundreds to thousands of megabytes) that they cannot be comprehensively interrogated by a human analyst. Therefore, advanced mathematical techniques are needed to glean materials science and engineering insight into the processing–structure–property relationships of the examined material from the SI data. This review discusses recent advances in the application of multivariate statistical analysis (MVSA) methods to STEMEDS SI experiments. In particular, the fundamental mathematics of principal component analysis (PCA) and related methods are reviewed, and advanced methods such as multivariate curve resolution (MCR) are discussed. The applications of PCA and MCRbased techniques to solve difficult materials science problems, such as the analysis of a particle fully embedded in a matrix phase, are discussed, as well as the effects that can confuse the results of MVSA computations. Possible future advances and areas in need of further study are also discussed.Advances in Imaging and Electron Physics 01/2011; 168:249295.  [Show abstract] [Hide abstract]
ABSTRACT: Stochastic artificial neural networks are a computational paradigm for pattern recognition applications that offer a good compromise between resource requirements and recognition accuracy. However, such resource savings can be exploited only when the network is implemented in hardware, while its software simulation suffers from poor performance and lack of established analysis/synthesis tools compared with the “traditional” deterministic artificial neural network. This work presents a synthesis approach based on genetic algorithms, where a set of different structures of a feedforward stochastic network is explored by a genetic optimization routine to obtain a reasonable accuracy with as low a resource consumption as possible. The structure of the neural network resulting from the optimization can consequently be used for its hardware synthesis on a programmable device. The approach is validated by an experimental section, where different genetic representations are compared and extensively commented.Advances in Imaging and Electron Physics 01/2011; 168:163. 
Chapter: Gamut Mapping
01/2010: pages 134; , ISBN: 9780123810175  Advances in Imaging and Electron Physics 01/2009; 157:259281.
 Advances in Imaging and Electron Physics 01/2009; 157:317350.
 Advances in Imaging and Electron Physics 01/2009; 157:95139.
 Advances in Imaging and Electron Physics 01/2009; 157:132.
 Advances in Imaging and Electron Physics 01/2009; 157:213258.
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ABSTRACT: Two commercial companies, AEI in the United Kingdom and Siemens in Germany, both of which produced transmission electron microscopes in the second half of the twentieth century, planned to enter the scanning transmission electron microscope market. A brief account of their endeavors is given, based on published material and information provided by those involved.Advances in Imaging and Electron Physics 01/2009; 159:187219.  Advances in Imaging and Electron Physics 01/2008; 151:241362.
 Advances in Imaging and Electron Physics 01/2006; 142:159.
 Advances in Imaging and Electron Physics 01/2006; 139:75177.
Data provided are for informational purposes only. Although carefully collected, accuracy cannot be guaranteed. The impact factor represents a rough estimation of the journal's impact factor and does not reflect the actual current impact factor. Publisher conditions are provided by RoMEO. Differing provisions from the publisher's actual policy or licence agreement may be applicable.
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