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Meysam Madadi, Sergio Escalera, Jordi Gonzalez, Xavier Roca, & Felipe Lumbreras. (2015). Multi-part body segmentation based on depth maps for soft biometry analysis. PRL - Pattern Recognition Letters, 56, 14–21.
Abstract: This paper presents a novel method extracting biometric measures using depth sensors. Given a multi-part labeled training data, a new subject is aligned to the best model of the dataset, and soft biometrics such as lengths or circumference sizes of limbs and body are computed. The process is performed by training relevant pose clusters, defining a representative model, and fitting a 3D shape context descriptor within an iterative matching procedure. We show robust measures by applying orthogonal plates to body hull. We test our approach in a novel full-body RGB-Depth data set, showing accurate estimation of soft biometrics and better segmentation accuracy in comparison with random forest approach without requiring large training data.
Keywords: 3D shape context; 3D point cloud alignment; Depth maps; Human body segmentation; Soft biometry analysis
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J. Pladellorens, Joan Serrat, A. Castell, & M.J. Yzuel. (1993). Using mathematical morphology to determine left ventricular contours. Physics in Medicine and Biology., 1877––1894.
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Hannes Mueller, Andre Groeger, Jonathan Hersh, Andrea Matranga, & Joan Serrat. (2021). Monitoring war destruction from space using machine learning. PNAS - Proceedings of the National Academy of Sciences of the United States of America, 118(23), e2025400118.
Abstract: Existing data on building destruction in conflict zones rely on eyewitness reports or manual detection, which makes it generally scarce, incomplete, and potentially biased. This lack of reliable data imposes severe limitations for media reporting, humanitarian relief efforts, human-rights monitoring, reconstruction initiatives, and academic studies of violent conflict. This article introduces an automated method of measuring destruction in high-resolution satellite images using deep-learning techniques combined with label augmentation and spatial and temporal smoothing, which exploit the underlying spatial and temporal structure of destruction. As a proof of concept, we apply this method to the Syrian civil war and reconstruct the evolution of damage in major cities across the country. Our approach allows generating destruction data with unprecedented scope, resolution, and frequency—and makes use of the ever-higher frequency at which satellite imagery becomes available.
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Miguel Oliveira, Victor Santos, Angel Sappa, P. Dias, & A. Moreira. (2016). Incremental Scenario Representations for Autonomous Driving using Geometric Polygonal Primitives. RAS - Robotics and Autonomous Systems, 83, 312–325.
Abstract: When an autonomous vehicle is traveling through some scenario it receives a continuous stream of sensor data. This sensor data arrives in an asynchronous fashion and often contains overlapping or redundant information. Thus, it is not trivial how a representation of the environment observed by the vehicle can be created and updated over time. This paper presents a novel methodology to compute an incremental 3D representation of a scenario from 3D range measurements. We propose to use macro scale polygonal primitives to model the scenario. This means that the representation of the scene is given as a list of large scale polygons that describe the geometric structure of the environment. Furthermore, we propose mechanisms designed to update the geometric polygonal primitives over time whenever fresh sensor data is collected. Results show that the approach is capable of producing accurate descriptions of the scene, and that it is computationally very efficient when compared to other reconstruction techniques.
Keywords: Incremental scene reconstruction; Point clouds; Autonomous vehicles; Polygonal primitives
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Angel Sappa, Cristhian A. Aguilera-Carrasco, Juan A. Carvajal Ayala, Miguel Oliveira, Dennis Romero, Boris X. Vintimilla, et al. (2016). Monocular visual odometry: A cross-spectral image fusion based approach. RAS - Robotics and Autonomous Systems, 85, 26–36.
Abstract: This manuscript evaluates the usage of fused cross-spectral images in a monocular visual odometry approach. Fused images are obtained through a Discrete Wavelet Transform (DWT) scheme, where the best setup is empirically obtained by means of a mutual information based evaluation metric. The objective is to have a flexible scheme where fusion parameters are adapted according to the characteristics of the given images. Visual odometry is computed from the fused monocular images using an off the shelf approach. Experimental results using data sets obtained with two different platforms are presented. Additionally, comparison with a previous approach as well as with monocular-visible/infrared spectra are also provided showing the advantages of the proposed scheme.
Keywords: Monocular visual odometry; LWIR-RGB cross-spectral imaging; Image fusion
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