20 research outputs found
Deep Learning for Diagonal Earlobe Crease Detection
An article published on Medical News Today in June 2022 presented a
fundamental question in its title: Can an earlobe crease predict heart attacks?
The author explained that end arteries supply the heart and ears. In other
words, if they lose blood supply, no other arteries can take over, resulting in
tissue damage. Consequently, some earlobes have a diagonal crease, line, or
deep fold that resembles a wrinkle. In this paper, we take a step toward
detecting this specific marker, commonly known as DELC or Frank's Sign. For
this reason, we have made the first DELC dataset available to the public. In
addition, we have investigated the performance of numerous cutting-edge
backbones on annotated photos. Experimentally, we demonstrate that it is
possible to solve this challenge by combining pre-trained encoders with a
customized classifier to achieve 97.7% accuracy. Moreover, we have analyzed the
backbone trade-off between performance and size, estimating MobileNet as the
most promising encoder.Comment: Accepted at 12th International Conference on Pattern Recognition
Applications (ICPRAM 2023
An X3D Neural Network Analysis for Runner's Performance Assessment in a Wild Sporting Environment
We present a transfer learning analysis on a sporting environment of the
expanded 3D (X3D) neural networks. Inspired by action quality assessment
methods in the literature, our method uses an action recognition network to
estimate athletes' cumulative race time (CRT) during an ultra-distance
competition. We evaluate the performance considering the X3D, a family of
action recognition networks that expand a small 2D image classification
architecture along multiple network axes, including space, time, width, and
depth. We demonstrate that the resulting neural network can provide remarkable
performance for short input footage, with a mean absolute error of 12 minutes
and a half when estimating the CRT for runners who have been active from 8 to
20 hours. Our most significant discovery is that X3D achieves state-of-the-art
performance while requiring almost seven times less memory to achieve better
precision than previous work.Comment: Accepted to the 18th International Conference on Machine Vision
Applications (MVA 2023
A Large-Scale Re-identification Analysis in Sporting Scenarios: the Betrayal of Reaching a Critical Point
Re-identifying participants in ultra-distance running competitions can be
daunting due to the extensive distances and constantly changing terrain. To
overcome these challenges, computer vision techniques have been developed to
analyze runners' faces, numbers on their bibs, and clothing. However, our study
presents a novel gait-based approach for runners' re-identification (re-ID) by
leveraging various pre-trained human action recognition (HAR) models and loss
functions. Our results show that this approach provides promising results for
re-identifying runners in ultra-distance competitions. Furthermore, we
investigate the significance of distinct human body movements when athletes are
approaching their endurance limits and their potential impact on re-ID
accuracy. Our study examines how the recognition of a runner's gait is affected
by a competition's critical point (CP), defined as a moment of severe fatigue
and the point where the finish line comes into view, just a few kilometers away
from this location. We aim to determine how this CP can improve the accuracy of
athlete re-ID. Our experimental results demonstrate that gait recognition can
be significantly enhanced (up to a 9% increase in mAP) as athletes approach
this point. This highlights the potential of utilizing gait recognition in
real-world scenarios, such as ultra-distance competitions or long-duration
surveillance tasks.Comment: Accepted at 7th International Joint Conference on Biometrics (IJCB
2023
Communication, development, and social change in Spain: A field between institutionalization and implosion
This paper renders an account of the rapid institutionalization of the academic field of Communication for Development and Social Change (CDCS) in Spain in recent years following a period of neglect and marginalization. The ongoing expansion of the field of CDSC in the Spanish context is understood as a process of implosion, i.e. a collapse inwards, which results from the inconsistencies and weaknesses of fast and late institutionalization. The methodological approach for this inquiry is a documental review of both academic literature and research and institutional reports produced in Spain between 1980 and 2010. Based on this review, the paper contrasts the trajectory of the field in Spain with the debates at the international level, establishing relevant continuities and differences.This article is part of the Research Project (Ministry of Economy and Competitiveness,
Spain) CSO2014-52005-R titled ‘Evaluation and Monitoring of Communication for
Development and Social Change in Spain: design of indicators to measure its social
impact’ (2015–2017)17 página
Decontextualized I3D ConvNet for ultra-distance runners performance analysis at a glance
In May 2021, the site runnersworld.com published that participation in
ultra-distance races has increased by 1,676% in the last 23 years. Moreover,
nearly 41% of those runners participate in more than one race per year. The
development of wearable devices has undoubtedly contributed to motivating
participants by providing performance measures in real-time. However, we
believe there is room for improvement, particularly from the organizers point
of view. This work aims to determine how the runners performance can be
quantified and predicted by considering a non-invasive technique focusing on
the ultra-running scenario. In this sense, participants are captured when they
pass through a set of locations placed along the race track. Each footage is
considered an input to an I3D ConvNet to extract the participant's running gait
in our work. Furthermore, weather and illumination capture conditions or
occlusions may affect these footages due to the race staff and other runners.
To address this challenging task, we have tracked and codified the
participant's running gait at some RPs and removed the context intending to
ensure a runner-of-interest proper evaluation. The evaluation suggests that the
features extracted by an I3D ConvNet provide enough information to estimate the
participant's performance along the different race tracks.Comment: Accepted at 21st International Conference on Image Analysis and
Processing (ICIAP 2021
Deep learning for source camera identification on mobile devices
In the present paper, we propose a source camera identification method for
mobile devices based on deep learning. Recently, convolutional neural networks
(CNNs) have shown a remarkable performance on several tasks such as image
recognition, video analysis or natural language processing. A CNN consists on a
set of layers where each layer is composed by a set of high pass filters which
are applied all over the input image. This convolution process provides the
unique ability to extract features automatically from data and to learn from
those features. Our proposal describes a CNN architecture which is able to
infer the noise pattern of mobile camera sensors (also known as camera
fingerprint) with the aim at detecting and identifying not only the mobile
device used to capture an image (with a 98\% of accuracy), but also from which
embedded camera the image was captured. More specifically, we provide an
extensive analysis on the proposed architecture considering different
configurations. The experiment has been carried out using the images captured
from different mobile devices cameras (MICHE-I Dataset was used) and the
obtained results have proved the robustness of the proposed method.Comment: 15 pages single column, 9 figure
Deep learning for source camera identification on mobile devices
In the present paper, we propose a source camera identification (SCI) method for mobile devices based on deep learning. Recently, convolutional neural networks (CNNs) have shown a remarkable performance on several tasks such as image recognition, video analysis or natural language processing. A CNN consists on a set of layers where each layer is composed by a set of high pass filters which are applied all over the input image. This convolution process provides the unique ability to extract features automatically from data and to learn from those features. Our proposal describes a CNN architecture which is able to infer the noise pattern of mobile camera sensors (also known as camera fingerprint) with the aim at detecting and identifying not only the mobile device used to capture an image (with a 98% of accuracy), but also from which embedded camera the image was captured. More specifically, we provide an extensive analysis on the proposed architecture considering different configurations. The experiment has been carried out using the images captured from different mobile device cameras (MICHE-I Dataset) and the obtained results have proved the robustness of the proposed method