Institution
Grenoble Institute of Technology
Education•Grenoble, France•
About: Grenoble Institute of Technology is a education organization based out in Grenoble, France. It is known for research contribution in the topics: Hyperspectral imaging & Geology. The organization has 3427 authors who have published 5345 publications receiving 137158 citations. The organization is also known as: Grenoble INP.
Papers published on a yearly basis
Papers
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TL;DR: The ultimate goal of the proposed framework is to be able to extract spatial features which better model the attributes of different objects in the remote sensed imagery, which enables better performances on classification.
Abstract: Extended attribute profiles (EAPs) have been widely used for the classification of high-resolution hyperspectral images. EAPs are obtained by computing a sequence of attribute operators. Attribute filters (AFs) are connected operators, so they can modify an image by only merging its flat zones. These filters are effective when dealing with very high resolution images since they preserve the geometrical characteristics of the regions that are not removed from the image. However, AFs, being connected filters, suffer the problem of “leakage” (i.e., regions related to different structures in the image that happen to be connected by spurious links will be considered as a single object). Objects expected to disappear at a certain threshold remain present when they are connected with other objects in the image. The attributes of small objects will be mixed with their larger connected objects. In this paper, we propose a novel framework for morphological AFs with partial reconstruction and extend it to the classification of high-resolution hyperspectral images. The ultimate goal of the proposed framework is to be able to extract spatial features which better model the attributes of different objects in the remote sensed imagery, which enables better performances on classification. An important characteristic of the presented approach is that it is very robust to the ranges of rescaled principal components, as well as the selection of attribute values. Our experimental results, conducted using a variety of hyperspectral images, indicate that the proposed framework for AFs with partial reconstruction provides state-of-the-art classification results. Compared to the methods using only single EAP and stacking all EAPs computed by existing attribute opening and closing together, the proposed framework benefits significant improvements in overall classification accuracy.
35 citations
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TL;DR: In this paper, a method to get 3D information on paper fibrous microstructures using X-ray synchrotron microtomography imaging has been proposed to better understand the links between the manufacturing conditions, the resulting microstructural and mechanical properties of the paper fiber networks, together with the morphology of fibres and fibre-fibre bonds.
Abstract: Abstract By using X-ray synchrotron microtomography imaging, this work aims at proposing a method to get 3D information on paper fibrous microstructures. Such technique is useful to better understand the links between the manufacturing conditions, the resulting microstructural and mechanical properties of the paper fibrous networks, together with the morphology of fibres and fibre-fibre bonds. Its usefulness is illustrated for the 3D analysis of model papers being produced by changing the wet pressing conditions. It is demonstrated that the image analysis allows the changes of parameters describing, for example, the fibre cross section shape and inclination, the bond area surfaces, the distance between bonds to be followed with respect to the processing conditions for a large set of fibres and bonds. The distributions of properties that can be drawn from this experimental analysis will allow mechanical or physical discrete modelling approaches for papers to be enriched.
35 citations
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TL;DR: In this paper, a data-driven method to obtain an adapted dictionary is proposed, where inter-channels links are taken into account in the spatial multivariate model, and shift-invariance is used for the temporal model.
Abstract: This article addresses the issue of representing electroencephalographic (EEG) signals in an efficient way. While classical approaches use a fixed Gabor dictionary to analyze EEG signals, this article proposes a data-driven method to obtain an adapted dictionary. To reach an efficient dictionary learning, appropriate spatial and temporal modeling is required. Inter-channels links are taken into account in the spatial multivariate model, and shift-invariance is used for the temporal model. Multivariate learned kernels are informative (a few atoms code plentiful energy) and interpretable (the atoms can have a physiological meaning). Using real EEG data, the proposed method is shown to outperform the classical multichannel matching pursuit used with a Gabor dictionary, as measured by the representative power of the learned dictionary and its spatial flexibility. Moreover, dictionary learning can capture interpretable patterns: this ability is illustrated on real data, learning a P300 evoked potential.
35 citations
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TL;DR: In this paper, the performance of tin doped indium oxide (ITO) layers grown by MOCVD from different indium and tin precursors is investigated, and the best films present a resistivity of 2.5 × 10−4 Ω cm and a transmittance higher than 84% for high deposition temperatures.
35 citations
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TL;DR: In this paper, the authors investigated the best location to loop the network considering a given target of distributed generation penetration and proposed several algorithms to get closer to the optimal solution and tested on a real French network.
35 citations
Authors
Showing all 3527 results
Name | H-index | Papers | Citations |
---|---|---|---|
J. F. Macías-Pérez | 134 | 486 | 94715 |
J-Y. Hostachy | 119 | 716 | 65686 |
Alain Dufresne | 111 | 358 | 45904 |
David Brown | 105 | 1257 | 46827 |
Raphael Noel Tieulent | 89 | 417 | 24926 |
Antonio Plaza | 79 | 631 | 29775 |
G. Conesa Balbastre | 76 | 208 | 18800 |
Jocelyn Chanussot | 73 | 614 | 27949 |
Ekhard K. H. Salje | 70 | 581 | 19938 |
Richard Wilson | 70 | 809 | 21477 |
Jerome Bouvier | 70 | 278 | 13724 |
David Maurin | 68 | 215 | 17295 |
Alessandro Gandini | 67 | 348 | 19813 |
Matthieu Tristram | 67 | 143 | 17188 |
D. Santos | 65 | 113 | 15648 |