Comprehensive comparison between vision transformers and convolutional neural networks for face recognition tasks.

This paper presents a comprehensive comparison between Vision Transformers and Convolutional Neural Networks for face recognition related tasks, including extensive experiments on the tasks of face identification and verification. Our study focuses on six state-of-the-art models: EfficientNet, Inception, MobileNet, ResNet, VGG, and Vision Transformers. Our evaluation of these models is based on five diverse datasets: Labeled Faces in the Wild, Real World Occluded Faces, Surveillance Cameras Face, UPM-GTI-Face, and VGG Face 2.

A fully automatic method for segmentation of soccer playing fields.

This paper proposes a strategy to segment the playing field in soccer images, suitable for integration in many soccer image analysis applications. The combination of a green chromaticity-based analysis and an analysis of the chromatic distortion using full-color information, both at the pixel-level, allows segmenting the green areas of the images. Then, a fully automatic post-processing block at the region-level discards the green areas that do not belong to the playing field.

A New Technique Based on Voronoi Tessellation to Assess the Space-Dependence of Categorical Variables.

Based on a sample of geolocated elements, each of them labeled with a (not necessarily ordered) categorical feature, several indexes for assessing the relationship between the geolocation variables (latitude and longitude) and the categorical variable are evaluated. Among these indexes, a new one based on a Voronoi tessellation presents several advantages since it does not require a variable transformation or a previous discretization; in addition, simulations show that this index is considerably robust when compared with the previously known ones. Finally, the use of the presented indexes is also illustrated by analyzing the geolocation of communities in some communication networks derived from Call Detail Records.

Analysis of Basic Features in Dynamic Network Models.

Time evolving Random Network Models are presented as a mathematical framework for modelling and analyzing the evolution of complex networks. This framework allows the analysis over time of several network characterizing features such as link density, clustering coefficient, degree distribution, as well as entropy-based complexity measures, providing new insight on the evolution of random networks. First, some simple dynamic network models, based only on edge density, are analyzed to serve as a baseline reference for assessing more complex models.

Augmented Reality Tool for the Situational Awareness Improvement of UAV Operators.

Unmanned Aerial Vehicles (UAVs) are being extensively used nowadays. Therefore, pilots of traditional aerial platforms should adapt their skills to operate them from a Ground Control Station (GCS). Common GCSs provide information in separate screens: one presents the video stream while the other displays information about the mission plan and information coming from other sensors.