Browsing by Author "Pardo, David"
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Item Automatic Red-Channel underwater image restoration(2015-01) Galdran, Adrian; Pardo, David; Picon, Artzai; Alvarez-Gila, Aitor; Tecnalia Research & Innovation; COMPUTER_VISION; VISUALUnderwater images typically exhibit color distortion and low contrast as a result of the exponential decay that light suffers as it travels. Moreover, colors associated to different wavelengths have different attenuation rates, being the red wavelength the one that attenuates the fastest. To restore underwater images, we propose a Red Channel method, where colors associated to short wavelengths are recovered, as expected for underwater images, leading to a recovery of the lost contrast. The Red Channel method can be interpreted as a variant of the Dark Channel method used for images degraded by the atmosphere when exposed to haze. Experimental results show that our technique handles gracefully artificially illuminated areas, and achieves a natural color correction and superior or equivalent visibility improvement when compared to other state-of-the-art methods.Item Bearing assessment tool for longitudinal bridge performance(2020-11-01) Garcia-Sanchez, David; Fernandez-Navamuel, Ana; Sánchez, Diego Zamora; Alvear, Daniel; Pardo, David; Tecnalia Research & Innovation; E&I SEGURAS Y RESILIENTESThis work provides an unsupervised learning approach based on a single-valued performance indicator to monitor the global behavior of critical components in a viaduct, such as bearings. We propose an outlier detection method for longitudinal displacements to assess the behavior of a singular asymmetric prestressed concrete structure with a 120 m high central pier acting as a fixed point. We first show that the available long-term horizontal displacement measurements recorded during the undamaged state exhibit strong correlations at the different locations of the bearings. Thus, we combine measurements from four sensors to design a robust performance indicator that is only weakly affected by temperature variations after the application of principal component analysis. We validate the method and show its efficiency against false positives and negatives using several metrics: accuracy, precision, recall, and F1 score. Due to its unsupervised learning scope, the proposed technique is intended to serve as a real-time supervision tool that complements maintenance inspections. It aims to provide support for the prioritization and postponement of maintenance actions in bridge management.Item Pectoral Muscle Segmentation in Mammograms Based on Cartoon-Texture Decomposition(Springer, 2015) Galdran, Adrian; Picon, Artzai; Garrote, Estibaliz; Pardo, DavidPectoral muscle segmentation on medio-lateral oblique views of mammograms represents an important preprocessing step in many mammographic image analysis tasks. Although its location can be per- ceptually obvious for a human observer, the variability in shape, size, and intensities of the pectoral muscle boundary turns its automatic segmen- tation into a challenging problem. In this work we propose to decompose the input mammogram into its textural and structural components at di erent scales prior to dynamically thresholding it into several levels. The resulting segmentations are re ned with an active contour model and merged together by means of a simple voting scheme to remove possible outliers. Our method performs well compared to several other state-of- the-art techniques. An average DICE similarity coe cient of 0:91 and mean Hausdor distance of 3:66 3:23 mm. validate our approach.Item Supervised Deep Learning with Finite Element simulations for damage identification in bridges(2022-04-15) Fernandez-Navamuel, Ana; Zamora-Sánchez, Diego; Omella, Ángel J.; Pardo, David; Garcia-Sanchez, David; Magalhães, Filipe; Tecnalia Research & Innovation; E&I SEGURAS Y RESILIENTESThis work proposes a supervised Deep Learning approach for damage identification in bridge structures. We employ a hybrid methodology that incorporates Finite Element simulations to enrich the training phase of a Deep Neural Network with synthetic damage scenarios. The neural network is based on autoencoders and its particular architecture allows to activate or deactivate nonlinear connections under need. The methodology intends to contribute to the progress towards the applicability of Structural Health Monitoring practices in full-scale bridge structures. The ultimate goal is to estimate the location and severity of damage from measurements of the dynamic response of the structure. The damages we seek to detect correspond to material degradations that affect wide areas of the structure by reducing its stiffness properties. Our method allows a feasible adaptation to large systems with complex parametrizations and structural particularities. We investigate the performance of the proposed method on two full-scale instrumented bridges, obtaining adequate results for the testing datasets even in presence of measurement uncertainty. Besides, the method successfully predicts the damage condition for two real damage scenarios of increasing severity available in one of the bridges.Item Vibration-Based SHM Strategy for a Real Time Alert System with Damage Location and Quantification(Springer Science and Business Media Deutschland GmbH, 2021-01-11) Fernández-Navamuel, Ana; Zamora-Sánchez, Diego; Varona-Poncela, Tomás; Jiménez-Fernández, Carlos; Díez-Hernández, Jesús; García-Sánchez, David; Pardo, David; Rizzo, Piervincenzo; Milazzo, Alberto; E&I SEGURAS Y RESILIENTES; Tecnalia Research & InnovationWe present a simple and fully automatable vibration-based Structural Health Monitoring (SHM) alert system. The proposed method consists in applying an Automated Frequency Domain Decomposition (AFDD) algorithm to obtain the eigenfrequencies and mode shapes in real time from acceleration measurements, allowing to provide a diagnosis based on a Support Vector Machine algorithm trained with a database of the modal properties in undamaged and damaged scenarios accounting for temperature variability. The result is an alert system for controlling the correct performance of the structure in real time with a simple but efficient approach. Once the alert is triggered, the undamaged mode shapes (which could be previously stored in a database of modal parameters classified by temperature) and the current (damaged) mode shapes, can provide guidance for further application of Finite Element Model Updating (FEMU) techniques. The method is trained and validated with simulations from a FE model that is calibrated employing a genetic algorithm with real data from a short-term vibration measurement campaign on a truss railway bridge in Alicante (Spain).