Federated Learning in Computer Vision

Federated Learning (FL) has recently emerged as a novel machine learning paradigm allowing to preserve privacy and to account for the distributed nature of the learning process in many real-world settings. Computer vision tasks deal with huge datasets often with critical privacy issues, therefore many federated learning approaches have been presented to exploit its distributed and privacy-preserving nature. Firstly, this paper introduces the different FL settings used in computer vision and the main challenges that need to be tackled. Then, it provides a comprehensive overview of the different strategies used for FL in vision applications and presents several different approaches for image classification, object detection, semantic segmentation and for focused settings in face recognition and medical imaging. For the various approaches the considered FL setting, the employed data and methodologies and the achieved results are thoroughly discussed

Unsupervised domain adaptation for mobile semantic segmentation based on cycle consistency and feature alignment

The supervised training of deep networks for semantic segmentation requires a huge amount of labeled real world data. To solve this issue, a commonly exploited workaround is to use synthetic data for training, but deep networks show a critical performance drop when analyzing data with slightly different statistical properties with respect to the training set. In this work, we propose a novel Unsupervised Domain Adaptation (UDA) strategy to address the domain shift issue between real world and synthetic representations. An adversarial model, based on the cycle consistency framework, performs the mapping between the synthetic and real domain. The data is then fed to a MobileNet-v2 architecture that performs the semantic segmentation task. An additional couple of discriminators, working at the feature level of the MobileNet-v2, allows to better align the features of the two domain distributions and to further improve the performance. Finally, the consistency of the semantic maps is exploited. After an initial supervised training on synthetic data, the whole UDA architecture is trained end-to-end considering all its components at once. Experimental results show how the proposed strategy is able to obtain impressive performance in adapting a segmentation network trained on synthetic data to real world scenarios. The usage of the lightweight MobileNet-v2 architecture allows its deployment on devices with limited computational resources as the ones employed in autonomous vehicles

Noise reduction in muon tomography for detecting high density objects

The muon tomography technique, based on multiple Coulomb scattering of cosmic ray muons, has been proposed as a tool to detect the presence of high density objects inside closed volumes. In this paper a new and innovative method is presented to handle the density fluctuations (noise) of reconstructed images, a well known problem of this technique. The effectiveness of our method is evaluated using experimental data obtained with a muon tomography prototype located at the Legnaro National Laboratories (LNL) of the Istituto Nazionale di Fisica Nucleare (INFN). The results reported in this paper, obtained with real cosmic ray data, show that with appropriate image filtering and muon momentum classification, the muon tomography technique can detect high density materials, such as lead, albeit surrounded by light or medium density material, in short times. A comparison with algorithms published in literature is also presented

Pairwise Similarities for Scene Segmentation combining Color and Depth data

none5noThe advent of cheap consumer level depth-aware cameras and the steady advances with dense stereo algorithms urge the exploitation of combined photometric and geometric information to attain a more robust scene understanding. To this end, segmentation is a fundamental task, since it can be used to feed with meaningfully grouped data the following steps in a more complex pipeline. Color segmentation has been explored thoroughly in the image processing literature, as much as geometric-based clustering has been widely adopted with 3D data. We introduce a novel approach that mixes both features to overcome the ambiguity that arises when using only one kind of information. This idea has already appeared in recent techniques, however they often work by combining color and depth data in a common Euclidean space. By contrast, we avoid any embedding by virtue of a game-theoretic clustering schema that leverages on specially crafted pairwise similarities.restrictedF. Bergamasco; A. Albarelli; A. Torsello; M. Favaro;P. ZanuttighF., Bergamasco; A., Albarelli; A., Torsello; M., Favaro; Zanuttigh, Pietr

Scene Segmentation Driven by Deep Learning and Surface Fitting

This paper proposes a joint color and depth segmentation scheme exploiting together geometrical clues and a learning stage. The approach starts from an initial over-segmentation based on spectral clustering. The input data is also fed to a Convolutional Neural Network (CNN) thus producing a per-pixel descriptor vector for each scene sample. An iterative merging procedure is then used to recombine the segments into the regions corresponding to the various objects and surfaces. The proposed algorithm starts by considering all the adjacent segments and computing a similarity metric according to the CNN features. The couples of segments with higher similarity are considered for merging. Finally the algorithm uses a NURBS surface fitting scheme on the segments in order to understand if the selected couples correspond to a single surface. The comparison with state-of-the-art methods shows how the proposed method provides an accurate and reliable scene segmentation

An approach to assess flooding and erosion risk for open beaches in a changing climate

This paper examines the vulnerability to flooding and erosion of four open beach study sites in Europe. A framework for the quantitative estimation of present and future coastal flood and erosion risks is established using methods, data and tools from across a range of disciplines, including topographic and bathymetric data, climate data from observation, hindcast and model projections, statistical modelling of current and future climates and integrated risk analysis tools. Uncertainties in the estimation of future coastal system dynamics are considered, as are the consequences for the inland systems. Different implementations of the framework are applied to the study sites which have different wave, tidal and surge climate conditions. These sites are: Santander, Spain—the Atlantic Ocean; Bellocchio, Italy—the Adriatic Sea; Varna, Bulgaria—the Black Sea; and the Teign Estuary, UK—the northern Atlantic Ocean. The complexity of each system is first simplified by sub-division into coastal "impact units" defined by homogeneity in the local key forcing parameters: wave, wind, tide, river discharge, run-off, etc. This reduces the simulation to that of a number of simpler linear problems which are treated by applying the first two components of the Source–Pathway–Receptor–Consequence (S–P–R–C) approach. The case studies reveal the flexibility of this approach, which is found useful for the rapid assessment of the risks of flooding and erosion for a range of scenarios and the likely effectiveness of flood defences

UPDATE OF THE EUROTOP MANUAL: NEW INSIGHTS ON WAVE OVERTOPPING

Quite some new insights on wave overtopping were achieved since the first submission of the EurOtop Manual in 2007, which have now resulted in a second edition of this Manual. A major improvement has been made on the understanding of wave by wave overtopping and tolerable wave overtopping that is connected to it. Many videos are available on the overtopping website that show all kind of overtopping discharges and volumes and may give guidance for the user of the Manual. The EurOtop Neural Network and the EurOtop database are improved and extended versions of the earlier NN and CLASH database. New insights and prediction formulae have been developed for very low freeboards; for very steep slopes up to vertical walls; for run-up on steep slopes; for overtopping on storm walls on a promenade; and for overtopping on vertical walls, where overtopping has been divided in situations with and without an influencing foreshore and where the first situation may be divided in non-impulsive and impulsive overtopping

Methodology for integrated socio-economic assessment of offshore platforms : towards facilitation of the implementation of the marine strategy framework directive

In this paper a Methodology for Integrated Socio-Economic Assessment (MISEA) of the viability and sustainability of different designs of Multi-Use Offshore Platforms (MUOPs) is presented. MUOPs are designed for multi-use of ocean space for energy extraction (wind power production and wave energy), aquaculture and transport maritime services. The developed methodology allows identification, valuation and assessment of: the potential range of impacts of a number of feasible designs of MUOP investments, and the likely responses of those impacted by the investment project. This methodology provides decision-makers with a valuable decision tool to assess whether a MUOP project increases the overall social welfare and hence should be undertaken, under alternative specifications regarding its design, the discount rate and the stream of net benefits, if a Cost-Benefit Analysis (CBA) is to be followed or sensitivity analysis of selected criteria in a Multi-Criteria Decision Analysis (MCDA) framework. Such a methodology is also crucial for facilitating of the implementation of the Marine Strategy Framework Directive (MSFD adopted in June 2008) that aims to achieve good environmental status of the EU's marine waters by 2020 and to protect the resource base upon which marine-related economic and social activities depend. According to the MSFD each member state must draw up a program of cost-effective measures, while prior to any new measure an impact assessment which contains a detailed cost-benefit analysis of the proposed measures is required

Analytical model for the calculation of lateral velocity distributions in potential cross-sections

[EN] The hydraulic modeling of water depth and flow velocities in open channel flows that were fitted by power-law cross-section stand out for their versatility, allowing their use in numerous practical applications, both in natural and artificial channels. The determination of the hydraulic variables of depth and average velocity has been widely studied in potential cross-sections; however, the variation seen in these variables along the cross-section was not found in the literature. Knowledge of this variation allows the development of studies (e.g. to know the approximate damage in different areas of the cross-section, to analyse sediment transport, or other applications in river hydraulics). This paper presents a methodology which allows calculation of the hydraulic variables in any area of a power-law cross-section. The methodology is applied to symmetrical cross-sections, comparing its generated results with the obtained values by different computational hydraulic codes, which are thoroughly accepted by scientific community, such as CES, HEC-RAS and IBER. The obtained predictions of hydraulic parameters (using the explicit formulation described in this research) present very low errors when compared with results of other models, with great computational cost. These errors reach a root mean square error (RMSE) of 0.13 and 0.05 in the determination of velocities' lateral distribution and the ratio between velocity and average velocity. These values indicate a very successful validation for the analysed symmetrical sections.[ES] La modelización hidráulica de calados y velocidades de flujo, en cauces con secciones que admiten una representación de tipo potencial, se destaca por su versatilidad, permitiendo su utilización en numerosas aplicaciones prácticas tanto en canales naturales como artificiales. El cálculo de las variables hidráulicas (calado y velocidad media) ha sido ampliamente estudiado para este tipo de secciones. Sin embargo, en la literatura técnica no se han encontrado estudios que muestren la variación de estas magnitudes a lo largo de la sección transversal. El conocimiento de esta variación permite desarrollar estudios (ejemplo: conocer de manera aproximada los daños en diferentes zonas de la sección, analizar el transporte de sedimentos, estudiar los procesos de erosión u otras aplicaciones en hidráulica fluvial). Presentamos una metodología que permite el cálculo de las variables hidráulicas en cualquier zona de una sección tipo potencial. La metodología es aplicada a secciones simétricas, comparando los resultados generados con los obtenidos por diferentes códigos hidráulicos computacionales ampliamente aceptados por la comunidad científica (p-e- CES, HECRAS e IBER). Las predicciones de los parámetros hidráulicos obtenidas (usando la formulación explícita descrita en este artículo) presentan errores muy bajos, en comparación con otros modelos con mayor costo computacional. Estos errores alcanzan un valor promedio para la raíz del error cuadrático medio (RMSE) en el cálculo de la distribución lateral de velocidades de 0.13 y de 0.05, en el cálculo de la relación de velocidades respecto a la velocidad media. Estos valores indican una validación muy satisfactoria para las secciones simétricas analizadas.Sánchez-Romero, F.; Pérez-Sánchez, M.; López Jiménez, PA. (2018). Modelo analítico para el cálculo de distribuciones de velocidad laterales en secciones tipo potencial-ley. RIBAGUA - Revista Iberoamericana del Agua. 5(1):29-47. doi:10.1080/23863781.2018.1442189S29475

Segregation process of water-granular mixtures released down a steep chute

The objectives of this contribution are to analyse and quantify the segregation process of water saturated mixtures flowing down steep chutes. Experiments were carried out using two granular mixtures, a steep chute of variable length and a collecting box divided by sectors, in which the flowing material freely falls down at the chute outlet. The flow characteristics, the filling in time of the box itself, the grain-size composition of the box content with varying the distance from the chute outlet are examined by means of laser level transducers, image analysis and sieving. Segregation is effective and rapid along the flow direction, showing a modest dependence on mixture grain-size composition. The grain-size composition profile in time is reconstructed, showing coarser particles concentrated at the flow front, on the flow surface and at the sides, whereas fine fractions prevail at the tail and at the bottom