Browsing by Author "Sierra E."

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A structure-from-motion pipeline for topographic reconstructions using unmanned aerial vehicles and open source software
(Springer Verlag, 2018) Meza J.; Marrugo A.G.; Sierra E.; Guerrero M.; Meneses J.; Romero L.A.; Serrano C. J.E.; Martínez-Santos, Juan Carlos
In recent years, the generation of accurate topographic reconstructions has found applications ranging from geomorphic sciences to remote sensing and urban planning, among others. The production of high resolution, high-quality digital elevation models (DEMs) requires a significant investment in personnel time, hardware, and software. Photogrammetry offers clear advantages over other methods of collecting geomatic information. Airborne cameras can cover large areas more quickly than ground survey techniques, and the generated Photogrammetry-based DEMs often have higher resolution than models produced with other remote sensing methods such as LIDAR (Laser Imaging Detection and Ranging) or RADAR (radar detection and ranging). In this work, we introduce a Structure from Motion (SfM) pipeline using Unmanned Aerial Vehicles (UAVs) for generating DEMs for performing topographic reconstructions and assessing the microtopography of a terrain. SfM is a computer vision technique that consists in estimating the 3D coordinates of many points in a scene using two or more 2D images acquired from different positions. By identifying common points in the images both the camera position (motion) and the 3D locations of the points (structure) are obtained. The output from an SfM stage is a sparse point cloud in a local XYZ coordinate system. We edit the obtained point in MeshLab to remove unwanted points, such as those from vehicles, roofs, and vegetation. We scale the XYZ point clouds using Ground Control Points (GCP) and GPS information. This process enables georeferenced metric measurements. For the experimental verification, we reconstructed a terrain suitable for subsequent analysis using GIS software. Encouraging results show that our approach is highly cost-effective, providing a means for generating high-quality, low-cost DEMs. © Springer Nature Switzerland AG 2018.
A vision-based system for the dynamic measurement of in-plane displacements
(Institute of Electrical and Electronics Engineers Inc., 2014) Marrugo W.; Sierra E.; Marrugo J.; Camacho C.; Romero L.A.; Marrugo A.G.; Marrugo A.G.
Computer vision has advanced markedly in the last decade and has had new applications such as the analysis of the behavior of structures. The analysis of displacement and deformation of structures is an important process in Structural Health Monitoring (SHM). There are different techniques and devices for measuring strains and displacements, such as linear-variable-differential-transducers (LVDTs) and the global position system (GPS), which can be expensive and may not provide sufficient accuracy. This paper proposes vision-based methods for non-contact measurement of displacements and deformations. These methods allow for accurate non-contact measurements at low cost using off-the-shelf components, basically a camera, a computer, and a target. In this work, we test propose a vision based method for the measurement of displacements and we discuss the trade-offs in terms of robustness, computational complexity and accuracy. Encouraging results show that the displacement of a structure can be both determined accurately and fast enough in real time. © 2014 IEEE.
Robust detection and removal of dust artifacts in retinal images via dictionary learning and sparse-based inpainting
(SPIE, 2019) Sierra E.; Barrios E.; Marrugo A.G.; Millán M.S.; Alam M.S.
Retinal images are acquired with eye fundus cameras which, like any other camera, can suffer from dust particles attached to the sensor and lens. These particles impede light from reaching the sensor, and therefore they appear as dark spots in the image which can be mistaken as small lesions like microaneurysms. We propose a robust method for detecting dust artifacts from more than one image as input and, for the removal, we propose a sparse-based inpainting technique with dictionary learning. The detection is based on a closing operation to remove small dark features. We compute the difference with the original image to highlight the artifacts and perform a filtering approach with a filter bank of artifact models of different sizes. The candidate artifacts are identified via non-maxima suppression. Because the artifacts do not change position in the images, after processing all input images, the candidate artifacts which are not in the same approximate position in different images are rejected and kept unchanged in the image. The experimental results show that our method can successfully detect and remove artifacts, while ensuring the continuity of retinal structures, such as blood vessels. © 2019 SPIE. Downloading of the abstract is permitted for personal use only.