Local Surf-Based Keypoint Transfer Segmentation

International audienceThis paper presents an improvement of the keypoint transfer method for the segmentation of 3D medical images. Our approach is based on 3D SURF keypoint extraction, instead of 3D SIFT in the original algorithm. This yields a significantly higher number of keypoints, which allows to use a local segmentation transfer approach. The resulting segmentation accuracy is significantly increased, and smaller organs can be segmented correctly. We also propose a keypoint selection step which provides a good balance between speed and accuracy. We illustrate the efficiency of our approach with comparisons against state of the art methods

Impact de la stratégie de décodage sur la traduction de modalité radar-optique d'images de télédétection

National audienceNumerous computer vision applications are tackled by deep learning approaches with an encoder-decoder architecture.In remote sensing imagery, this architecture is leveraged for radar to optical image translation to ensure all-weather availability ofoptical images. The network should not only correct radar distortions but also generate artifact-free optical images. This paperfocuses on the impact of the decoding strategy on the reconstruction resulting from our radar-to-optical translator : SARDINet.Three strategies are compared here : post-upsampling convolutions, transposed convolutions and sub-pixel convolutions.Pour de nombreuses applications en vision par ordinateur, des approches d'apprentissage profond basées sur une architecture encodeur-décodeur ont été proposées. En imagerie de télédétection, cette architecture est notamment employée pour la traduction de modalité radar vers optique afin de disposer d'images optiques indépendamment des conditions météorologiques. L'architecture neuronale doit donc prendre en compte les distorsions des images radar sans introduire d'artefacts optiques. Cet article étudie l'impact de la stratégie de décodage sur la reconstruction résultant de notre traducteur radar-optique : SARDINet. Trois stratégies sont comparées ici : la convolution post-sur-échantillonnage, la convolution transposée et la convolution sub-pixellique

Deep Learning of Radiometrical and Geometrical Sar Distorsions for Image Modality translations

International audienceMultimodal approaches for Earth Observations suffer from both the lack of interpretability of SAR images and the high sensitivity to meteorological conditions of optical images. Translation methods were implemented to solve them for specific tasks and areas. But these implementations lack of generalizability as they do not include samples with challenging characteristics. Firstly, this paper sums up the main problems that a general SAR to optical image translator should overcome. Then, a SAR Distorted Image to optical translator Network (SARDINet) alternating knowledgeable channel-wise spatial convolutions and cross-channel convolutions is implemented. It aims at solving a problem of major concern in remote sensing: translating layover disturbed SAR images into disturbance-free optical ones. SARDINet is trained through a classical and an adversarial framework and compared to cGAN and cycleGAN from the literature. Experimental results prove that adversarial approaches are more qualitative but worsen quantitative results

Impact de la stratégie de décodage sur la traduction de modalité radar-optique d'images de télédétection

National audienceNumerous computer vision applications are tackled by deep learning approaches with an encoder-decoder architecture.In remote sensing imagery, this architecture is leveraged for radar to optical image translation to ensure all-weather availability ofoptical images. The network should not only correct radar distortions but also generate artifact-free optical images. This paperfocuses on the impact of the decoding strategy on the reconstruction resulting from our radar-to-optical translator : SARDINet.Three strategies are compared here : post-upsampling convolutions, transposed convolutions and sub-pixel convolutions.Pour de nombreuses applications en vision par ordinateur, des approches d'apprentissage profond basées sur une architecture encodeur-décodeur ont été proposées. En imagerie de télédétection, cette architecture est notamment employée pour la traduction de modalité radar vers optique afin de disposer d'images optiques indépendamment des conditions météorologiques. L'architecture neuronale doit donc prendre en compte les distorsions des images radar sans introduire d'artefacts optiques. Cet article étudie l'impact de la stratégie de décodage sur la reconstruction résultant de notre traducteur radar-optique : SARDINet. Trois stratégies sont comparées ici : la convolution post-sur-échantillonnage, la convolution transposée et la convolution sub-pixellique

ISSLIDE: A new InSAR dataset for Slow SLIding area DEtection with machine learning

International audienceDue to the high data demand of machine learning algorithms, multiple datasets are emerging in remote sensing. But these datasets are costly and time consuming to annotate especially for change detection or natural phenomena monitoring. In particular, early warning systems on slow-moving disasters are lacking of training datasets as they require both geomorphological and SAR interferometry expertise. In this paper, (i) we propose a novel InSAR dataset for Slow SLIding area DEtection (ISSLIDE) with machine learning algorithms. The latter consists of manually annotated patches of generated interferograms over slow moving areas. (ii) We implement the segmentation of ISSLIDE interferograms with classical deep learning approaches. FCN, DeepLabV3 and U-Net-like architectures are explored to serve as baseline for future works. To the best of our knowledge, this is the first dataset adapted to machine learning and targeting slow sliding area detection

Towards a multi-Modal, multi-Temporal and multi-Phenomena dataset for natural phenomena observation through deep learning

International audienc

Towards a multi-Modal, multi-Temporal and multi-Phenomena dataset for natural phenomena observation through deep learning

International audienc

Occurrence of ESBL- and AmpC-Producing E. coli in French Griffon Vultures Feeding on Extensive Livestock Carcasses

International audienceDespite the fact that the selective pressure of antibiotics on wild birds is supposed to be very weak, they are considered potential vectors of antimicrobial resistance (AMR). Obligate scavengers such as vultures can present high proportions of resistance to extended-spectrum cephalosporins (ESC) and multi-drug-resistant (MDR) bacteria, partially due to feeding stations that are provisioned with livestock carcasses from intensive farming. Here we investigated whether griffon vultures (Gyps fulvus) from two populations located in the French Alps, which feed on livestock carcasses from extensive farms, may carry such resistant bacteria. Phenotypic and genotypic characterization showed an 11.8% proportion of ESC-resistant bacteria, including five extended-spectrum beta-lactamase (ESBL)-producing and one AmpC-producing E. coli. The five ESBL-positive E. coli were clonal and all came from the same vulture population, proving their spread between animals. The ESBL phenotype was due to a bla CTX-M-15 gene located on the chromosome. Both ESBL-and AmpC-positive E. coli belonged to minor STs (ST212 and ST3274, respectively); interestingly, ST212 has already been identified in wild birds around the world, including vultures. These results suggest that actions are needed to mitigate the spread of MDR bacteria through wild birds, particularly in commensal species