Multitemporal Very High Resolution from Space: Outcome of the 2016 IEEE GRSS Data Fusion Contest

In this paper, the scientific outcomes of the 2016 Data Fusion Contest organized by the Image Analysis and Data Fusion Technical Committee of the IEEE Geoscience and Remote Sensing Society are discussed. The 2016 Contest was an open topic competition based on a multitemporal and multimodal dataset, which included a temporal pair of very high resolution panchromatic and multispectral Deimos-2 images and a video captured by the Iris camera on-board the International Space Station. The problems addressed and the techniques proposed by the participants to the Contest spanned across a rather broad range of topics, and mixed ideas and methodologies from the remote sensing, video processing, and computer vision. In particular, the winning team developed a deep learning method to jointly address spatial scene labeling and temporal activity modeling using the available image and video data. The second place team proposed a random field model to simultaneously perform coregistration of multitemporal data, semantic segmentation, and change detection. The methodological key ideas of both these approaches and the main results of the corresponding experimental validation are discussed in this paper

AUTOMATIC MRF-BASED REGISTRATION OF HIGH RESOLUTION SATELLITE VIDEO DATA

In this paper we propose a deformable registration framework for high resolution satellite video data able to automatically and accurately co-register satellite video frames and/or register them to a reference map/image. The proposed approach performs non-rigid registration, formulates a Markov Random Fields (MRF) model, while efficient linear programming is employed for reaching the lowest potential of the cost function. The developed approach has been applied and validated on satellite video sequences from Skybox Imaging and compared with a rigid, descriptor-based registration method. Regarding the computational performance, both the MRF-based and the descriptor-based methods were quite efficient, with the first one converging in some minutes and the second in some seconds. Regarding the registration accuracy the proposed MRF-based method significantly outperformed the descriptor-based one in all the performing experiments

Macroporous Poly(norbornadiene) is a Fast Oxygen Scavenger Material at Room Temperature

Emulsion templated norbornadiene is cured via ROMP yielding macroporous poly(norbornadiene)foams of 76% porosity exhibiting appealing stiffness combined with considerable ductility. The foams are readily oxidized in the presence of air at room temperature exhibiting an oxygen uptake capacity of more than 300 mg O2/g foam. In closed volumes of air a final oxygen level of a maximum of 5 ppm can be achieved after several hours at room temperature. The synergism of the porous morphology and the chemical nature of the polymer allows for the first example of a purely organic oxygen scavenger material with properties distinctly surpassing the state-of-the art in the field.<br /

Synthesis of Na-hydrazino- and Aza-peptoids based on substance P: C-terminal fragments and their trypsin inhibitory effect

Journal URL: http://www.springerlink.com/content/104405/AbstractSerine protease inhibitors (Serpins) is a group of proteins with similar structures, which were first identified as a set of proteins able to inhibit proteases function. The human plasma proteins antithrombin and antitrypsin, which play key roles in controlling blood coagulation and inflammation, respectively, were the first members of the serpins superfamily to be extensively studied. Trypsin-like serine proteases are essential for many biological processes. Because of this a large number of synthetic peptides have been designed and synthesized, based on the structure of inhibitors, active against trypsin or chymotrypsin. In the present work we have synthesized a series of Na-hydrazinopeptoids and aza-peptoids and studied their trypsin inhibitory effect. These peptidomimetics are expected to show enhanced metabolitic stability and bioavailability in comparison with natural parent peptidic analogs. All the syntheses were carried out stepwise by SPPS, using the Fmoc/ But methodology on the solid support 2-chlorotrityl chloride resin and DIC/HOBt as coupling reagent. The products were purified (HPLC) and identified (ESI-MS). Their inhibitory effect against trypsin activity has partly measured, while other compounds are under investigation

Context Aware 3D CNNs for Brain Tumor Segmentation

International audienceIn this work we propose a novel deep learning based pipeline for the task of brain tumor segmentation. Our pipeline consists of three primary components: (i) a preprocessing stage that exploits histogram standardization to mitigate inaccuracies in measured brain modalities, (ii) a first prediction stage that uses the V-Net deep learning architecture to output dense, per voxel class probabilities, and (iii) a prediction refinement stage that uses a Conditional Random Field (CRF) with a bilateral filtering objective for better context awareness. Additionally, we compare the V-Net architecture with a custom 3D Residual Network architecture, trained on a multi-view strategy, and our ablation experiments indicate that V-Net outperforms the 3D ResNet-18 with all bells and whistles, while fully connected CRFs as post processing, boost the performance of both networks. We report competitive results on the BraTS 2018 validation and test set

U-ReSNet: Ultimate Coupling of Registration and Segmentation with Deep Nets

International audienceIn this study, we propose a 3D deep neural network called U-ReSNet, a joint framework that can accurately register and segment medical volumes. The proposed network learns to automatically generate linear and elastic deformation models, trained by minimizing the mean square error and the local cross correlation similarity metrics. In parallel, a coupled architecture is integrated, seeking to provide segmentation maps for anatomies or tissue patterns using an additional decoder part trained with the dice coefficient metric. U-ReSNet is trained in an end to end fashion, while due to this joint optimization the generated network features are more informative leading to promising results compared to other deep learning-based methods existing in the literature. We evaluated the proposed architecture using the publicly available OASIS 3 dataset, measuring the dice coefficient metric for both registration and segmentation tasks. Our promising results indicate the potentials of our method which is composed from a convolutional architecture that is extremely simple and light in terms of parameters. Our code is publicly available https://github.com/TheoEst/coupling_registration_segmentation