Grenoble Institute of Technology

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.

Frequential and color analysis for hair mask segmentation

Abstract: In this paper, we present an automatic method for hair segmentation. Our algorithm is divided into two steps. Firstly, we take information from frequential and color analysis in order to create binary masks as descriptor of the hair location. Secondly, we perform a 'matting treatment' which is a process to extract foreground object from an image. This approach is based on markers which positions are initialized from the fusion of frequential and color masks. At the end the matting treatment result is use to segment the hair. Results are evaluated using semi- manual segmentation references.

Strength of Highly Porous Ceramic Electrodes

Abstract: Highly porous ceramics are critical components of Solid Oxide Fuel Cells. Nevertheless, their mechanical properties are poor and not fully understood. Herein, Discrete Element simulations are used to quantitatively assess the relation between the particulate microstructure of highly porous ceramics and their mechanical properties. Partially sintered ceramics are numerically generated with complex microstructures including pore formers and bilayers. These microstructures are then tested in tension and compression to obtain their elastic and fracture behavior. Compiling experimental data from the literature and simulations results, an Orowan–Petch type relation between strength and particle size is proposed for highly porous ceramics. It is based on the local fracture model at the length scale of solid bonds between sintered particles.

Toward Real-Time Daily PQPF by an Analog Sorting Approach: Application to Flash-Flood Catchments

Abstract: Heavy-rainfall events are common in southern France and frequently result in devastating flash floods. Thus, an appropriate anticipation of future rainfall is required: for early flood warning, at least 12‐24 h in advance; for alerting operational services, at least 2‐3 days ahead. Precipitation forecasts are generally provided by numerical weather prediction models (NWP), and their associated uncertainty is generally estimatedthroughan ensemble approach. Precipitation forecastsalso have tobe adaptedto hydrologicalscales. This study describes an alternative approach to commonly used limited-area models. Probabilistic quantitative precipitation forecasts (PQPFs) are provided through an analog sorting technique, which directly links synoptic-scale NWPoutput to catchment-scale rainfallprobabilitydistributions. One issue concerns the latest developments in implementing a daily version of this technique into operational conditions. It is shown that the obtained PQPFs depend on the meteorological forecasts used for selecting analogous days and that the method has to be reoptimized when changing the source of synoptic forecasts, because of the NWP output uncertainties. Second, an evaluation of the PQPFs demonstrates that the analog technique performs well for early warning of heavy-rainfall events and provides useful information as potential input to a hydrological ensemble prediction system. It is shown that the obtained daily rainfall distributions can be unreliable. A statistical correction of the observed bias is proposed as a function of the no-rain frequency values, leading to a significant improvement in PQPF sharpness.