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This master’s thesis proposes a method solving multi-person 3D pose estimation with a
few calibrated camera views in an outdoor soccer environment. Despite of a significant
enhancement in recent years due to the large progress in Deep Learning, the 3D human
pose estimation in an outdoor environment still requires a lot of improvement to be
considered as resolved. Variations in lighting conditions, the movement of the camera
reducing the clearness of the image together with the occlusions between the players
on the soccer pitch and the low resolution of them make the 3D pose estimation of the
players particularly challenging. To perform accurate results, each step of the multiview 3D human pose estimator is optimized. Human detection and segmentation is
improved by eliminating irrelevant detected players via projection-based methods. An
efficient 2D pose detector suitable for estimating the skeleton of low resolution players is
used. A multi-way matching algorithm accross multiple views is introduced. Whatever
the number of players in the visible part of the pitch by the cameras, the bounding
boxes of the detected players are gathered in all camera views. The 2D skeletons of a
player are triangulated by pairs to form a 3D pose. A 3D pictorial structure (3DPS) is
applied to select the best 3D pose combination. In the case of inaccurate 3D skeleton,
a bundle adjustement is performed to refine the 3D pose. The proposed approach gives
accurate results in an outdoor environment.

3D reconstruction --- 2D pose estimation --- multi-view 3D pose estimation --- Ingénierie, informatique & technologie > Ingénierie électrique & électronique --- Ingénierie, informatique & technologie > Sciences informatiques

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Histopathological studies of aquatic animals refer to the microscopic examination of tissues and organs in order to detect deviations from the expected microscopic or macroscopic structure. Information obtained from the study of histomorphological lesions in aquatic animals can be a useful addition when determining the general state of health of aquatic animals, especially if chronic stressors and/or pathogens are present. Compared to mammals, postmortem autolysis progresses very rapidly in most aquatic organisms. This fact makes histopathological examination quite complex and demanding, not only in a histotechnical sense. A prerequisite for a successful study is the baseline knowledge of physiological processes and histological architecture of the studied species. Therefore, the aim of this Special Issue is to contribute to the current state of knowledge on the histopathology of aquatic animals and to provide a professional and encyclopedic tool for biologists and veterinarians.

Research & information: general --- Biology, life sciences --- Fisheries & related industries --- micro-nano plastics --- fish --- organism model --- histopathology --- blood biomarkers --- microlipoma --- liver --- Barbus balcanicus --- 3D reconstruction --- nutrition --- aquaculture --- fishmeal replacement --- land animal proteins --- histology --- intestinal microbiota --- Sparus aurata --- toxicity --- effect --- invertebrate --- mussels --- Aeromonas spp. --- rainbow trout --- bacteria --- infection --- antinutritional factors --- soybean --- gut health --- environmental monitoring --- histopathological biomarkers --- histopathological alterations --- fish gills --- atrazine --- Purkinje --- cerebellum --- myocytes --- toxicology --- IP3Rs --- Vistonis Lake --- physico-chemical parameters --- gills --- HSPs --- MARKs --- Na+-K+ ATPase --- hepatorenal --- pathology --- toxicosis --- biomarkers --- adult Xenopus laevis --- micro-nano plastics --- fish --- organism model --- histopathology --- blood biomarkers --- microlipoma --- liver --- Barbus balcanicus --- 3D reconstruction --- nutrition --- aquaculture --- fishmeal replacement --- land animal proteins --- histology --- intestinal microbiota --- Sparus aurata --- toxicity --- effect --- invertebrate --- mussels --- Aeromonas spp. --- rainbow trout --- bacteria --- infection --- antinutritional factors --- soybean --- gut health --- environmental monitoring --- histopathological biomarkers --- histopathological alterations --- fish gills --- atrazine --- Purkinje --- cerebellum --- myocytes --- toxicology --- IP3Rs --- Vistonis Lake --- physico-chemical parameters --- gills --- HSPs --- MARKs --- Na+-K+ ATPase --- hepatorenal --- pathology --- toxicosis --- biomarkers --- adult Xenopus laevis

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Augmented reality is a key technology that will facilitate a major paradigm shift in the way users interact with data and has only just recently been recognized as a viable solution for solving many critical needs. In practical terms, this innovation can be used to visualize data from hundreds of sensors simultaneously, overlaying relevant and actionable information over your environment through a headset. Semantic 3D reconstruction unlocks the promise of AR technology, possessing a far greater availability of semantic information. Although, there are several methods currently available as post-processing approaches to extract semantic information from the reconstructed 3D models, the results obtained results have been uncertain and evenly incorrect. Thus, it is necessary to explore or develop a novel 3D reconstruction approach to automatically recover 3D geometry model and obtained semantic information simultaneously. The rapid advent of deep learning brought new opportunities to the field of semantic 3D reconstruction from photo collections. Deep learning-based methods are not only able to extract semantic information but can also enhance fundamental techniques in semantic 3D reconstruction, techniques which include feature matching or tracking, stereo matching, camera pose estimation, and use of multi-view stereo methods. Moreover, deep learning techniques can be used to extract priors from photo collections, and this obtained information can in turn improve the quality of 3D reconstruction.

Technology: general issues --- History of engineering & technology --- feature tracking --- superpixel --- structure from motion --- three-dimensional reconstruction --- local feature --- multi-view stereo --- construction hazard --- safety education --- photoreality --- virtual reality --- anatomization --- audio classification --- olfactory display --- deep learning --- transfer learning --- inception model --- augmented reality --- higher education --- scientific production --- web of science --- bibliometric analysis --- scientific mapping --- applications in subject areas --- interactive learning environments --- 3P model --- primary education --- educational technology --- mobile lip reading system --- lightweight neural network --- face correction --- virtual reality (VR) --- computer vision --- projection mapping --- 3D face model --- super-resolution --- radial curve --- Dynamic Time Warping --- semantic 3D reconstruction --- eye-in-hand vision system --- robotic manipulator --- probabilistic fusion --- graph-based refinement --- 3D modelling --- 3D representation --- game engine --- laser scanning --- panoramic photography --- super-resolution reconstruction --- generative adversarial networks --- dense convolutional networks --- texture loss --- WGAN-GP --- orientation --- positioning --- viewpoint --- image matching --- algorithm --- transformation --- ADHD --- EDAH --- assessment --- continuous performance test --- Photometric Stereo (PS) --- 3D reconstruction --- fully convolutional network (FCN) --- semi-immersive virtual reality --- children --- cooperative games --- empowerment --- perception --- motor planning --- problem-solving --- area of interest --- wayfinding --- spatial information --- one-shot learning --- gesture recognition --- GREN --- skeleton-based --- 3D composition --- pre-visualization --- stereo vision --- 360° video --- n/a

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The Special Issue collects papers showing the progress made in key areas of photogrammetry and remote sensing such as modern and/or forthcoming sensors, improvements in data processing strategies and assessment of their reliability, application of innovations as proof of the contribution offered in the observation of the natural and built environment with better understanding of phenomena at required spatial scale.

Technology: general issues --- History of engineering & technology --- VHR tri-stereo satellite imagery --- digital elevation model --- isolated objects --- dense image matching --- change detection --- natural disasters --- deep learning --- threshold selection --- optical flow estimation --- Structure from Motion (SfM) --- 3D reconstruction --- noise estimation --- point clouds --- roughness --- surface reconstruction --- mesh model --- visibility constraints --- volumetric methods --- dense point cloud --- multiple view stereo (MVS) --- dense image matching (DIM) --- photogrammetry --- computer vision --- Copernicus --- Sentinel-1 --- Sentinel-2 --- InSAR --- damage proxy map --- Beirut --- Lebanon --- explosion --- radiometric calibration --- modeling --- geometric error --- high-precision calibration --- preprocessing --- enhancement --- point cloud --- image processing --- image histogram --- UAV --- camera calibration --- GNSS-assisted block orientation --- dome effect --- Monte Carlo simulation --- soil moisture content --- artificial neural network --- sample optimization --- synthetic aperture radar --- optical remote sensing image --- VHR tri-stereo satellite imagery --- digital elevation model --- isolated objects --- dense image matching --- change detection --- natural disasters --- deep learning --- threshold selection --- optical flow estimation --- Structure from Motion (SfM) --- 3D reconstruction --- noise estimation --- point clouds --- roughness --- surface reconstruction --- mesh model --- visibility constraints --- volumetric methods --- dense point cloud --- multiple view stereo (MVS) --- dense image matching (DIM) --- photogrammetry --- computer vision --- Copernicus --- Sentinel-1 --- Sentinel-2 --- InSAR --- damage proxy map --- Beirut --- Lebanon --- explosion --- radiometric calibration --- modeling --- geometric error --- high-precision calibration --- preprocessing --- enhancement --- point cloud --- image processing --- image histogram --- UAV --- camera calibration --- GNSS-assisted block orientation --- dome effect --- Monte Carlo simulation --- soil moisture content --- artificial neural network --- sample optimization --- synthetic aperture radar --- optical remote sensing image

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The use of machine learning in mechanics is booming. Algorithms inspired by developments in the field of artificial intelligence today cover increasingly varied fields of application. This book illustrates recent results on coupling machine learning with computational mechanics, particularly for the construction of surrogate models or reduced order models. The articles contained in this compilation were presented at the EUROMECH Colloquium 597, « Reduced Order Modeling in Mechanics of Materials », held in Bad Herrenalb, Germany, from August 28th to August 31th 2018. In this book, Artificial Neural Networks are coupled to physics-based models. The tensor format of simulation data is exploited in surrogate models or for data pruning. Various reduced order models are proposed via machine learning strategies applied to simulation data. Since reduced order models have specific approximation errors, error estimators are also proposed in this book. The proposed numerical examples are very close to engineering problems. The reader would find this book to be a useful reference in identifying progress in machine learning and reduced order modeling for computational mechanics.

supervised machine learning --- proper orthogonal decomposition (POD) --- PGD compression --- stabilization --- nonlinear reduced order model --- gappy POD --- symplectic model order reduction --- neural network --- snapshot proper orthogonal decomposition --- 3D reconstruction --- microstructure property linkage --- nonlinear material behaviour --- proper orthogonal decomposition --- reduced basis --- ECSW --- geometric nonlinearity --- POD --- model order reduction --- elasto-viscoplasticity --- sampling --- surrogate modeling --- model reduction --- enhanced POD --- archive --- modal analysis --- low-rank approximation --- computational homogenization --- artificial neural networks --- unsupervised machine learning --- large strain --- reduced-order model --- proper generalised decomposition (PGD) --- a priori enrichment --- elastoviscoplastic behavior --- error indicator --- computational homogenisation --- empirical cubature method --- nonlinear structural mechanics --- reduced integration domain --- model order reduction (MOR) --- structure preservation of symplecticity --- heterogeneous data --- reduced order modeling (ROM) --- parameter-dependent model --- data science --- Hencky strain --- dynamic extrapolation --- tensor-train decomposition --- hyper-reduction --- empirical cubature --- randomised SVD --- machine learning --- inverse problem plasticity --- proper symplectic decomposition (PSD) --- finite deformation --- Hamiltonian system --- DEIM --- GNAT