Road information has a fundamental role in modern society. Road extraction from optical satellite images is an economic and efficient way to obtain and update a transportation database. This paper presents an integrated method to extract urban main-road centerlines from satellite optical images. The proposed method has four main steps. First, general adaptive neighborhood is introduced to implement spectral-spatial classification to segment the images into two categories: road and nonroad groups. Second, road groups and homogeneous property, measured by local Geary's C, are fused to improve road-group accuracy. Third, road shape features are used to extract reliable road segments. Finally, local linear kernel smoothing regression is performed to extract smooth road centerlines. Road networks are then generated using tensor voting. The proposed method is tested and subsequently validated using a large set of multispectral high-resolution images. A comparison with several existing methods shows that the proposed method is more suitable for urban main-road centerline extraction.

Convolutional Neural Networks (CNN) has achieved a great success in image recognition task by automatically learning a hierarchical feature representation from raw data. While the majority of Time-Series Classification (TSC) literature is focused on 1D signals, this paper uses Recurrence Plots (RP) to transform time-series into 2D texture images and then take advantage of the deep CNN classifier. Image representation of time-series introduces different feature types that are not available for 1D signals, and therefore TSC can be treated as texture image recognition task. CNN model also allows learning different levels of representations together with a classifier, jointly and automatically. Therefore, using RP and CNN in a unified framework is expected to boost the recognition rate of TSC. Experimental results on the UCR time-series classification archive demonstrate competitive accuracy of the proposed approach, compared not only to the existing deep architectures, but also to the state-of-the art TSC algorithms.

International audienceAn original 3D stochastic model, based on the truncated plurigaussian random fields, has been adapted to simulate the complex microstructure of SOC electrodes. The representativeness of the virtual microstructures has been checked on several synchrotron X-ray and FIB-SEM tomographic reconstructions obtained on typical LSCF, LSC and Ni-YSZ electrodes. The validation step has been carried out by comparing numbers of electrode morphological properties as well as the phase effective diffusivities. This analysis has shown that the synthetic media mimic accurately the complex microstructure of typical SOC electrodes. The model capability to simulate different types of promising electrode architectures has also been investigated. It has been shown that the model is able to generate virtual electrode prepared by infiltration resulting in a uniform and continuous thin layer covering a scaffold. With a local thresholding depending on the position, continuous graded electrodes can be also produced. Finally, the model offers the possibility to introduce different correlation lengths for each phase in order to control the local topology of the interfaces. All these cases illustrate the model flexibility to generate various SOC microstructures. This validated and flexible model can be used for further numerical microstructural optimizations to improve the SOC performances

Elastic compression is recommended in prophylaxis and the treatment of venous disorder of the human leg. However, the mechanisms of compression are not completely understood and the response of internal tissues to the external pressure is partially unknown. To address this later issue, a 3D FE model of a human leg is developed. The geometry is derived from 3D CT scans. The FE model is made up of soft tissues and rigid bones. An inverse method is applied to identify the properties of soft tissues which are modelled as hyperelastic, near-incompressible, homogeneous and isotropic materials. The principle is to calibrate the constitutive properties using CT scans carried out with and without the presence of a compression sock. The deformed geometry computed by the calibrated FE model is in agreement with the geometry deduced from the CT scans. The model also provides the internal pressure distribution, which may lead to medical exploitation in the future.