Thierry Urruty

TL;DR: In this paper, the authors proposed a survey that covers both qualitative and quantitative analysis applied to novel medical imaging techniques to monitor the decline of renal function, and discussed how texture analysis and machine learning techniques have emerged in recent clinical researches in order to improve renal dysfunction monitoring and prediction.

TL;DR: A framework improving robustness for image forgery detection based on a camera identification model based on convolutional neural networks and an in-depth supervision of the layer and an experimental analysis of the influence of the learned features is presented.

TL;DR: The Aneurysm Detection and segMentation (ADAM) challenge as mentioned in this paper was organized in which methods for automatic UIA detection and segmentation were developed and submitted to be evaluated on a diverse clinical TOF-MRA dataset.

TL;DR: A new HSI dataset for the remotesensing community, specifically designed for Hyperspectral remote sensing retrieval and classification is proposed, and results prove that the physical measurements and optical properties of the scene contained in the HSI contribute in an accurate image content description than the information provided by theRGB image presentation.

TL;DR: An efficient and effective retrieval framework which includes a vectorization technique combined with a pseudo relevance model to transform any similarity matching model (between images) to a vector space model providing a score is presented.