Stand-Alone Objective Segmentation Quality Evaluation

The identification of objects in video sequences, that is, video segmentation, plays a major role in emerging interactive multimedia services, such as those enabled by the ISO MPEG-4 and MPEG-7 standards. In this context, assessing the adequacy of the identified objects to the application targets, that is, evaluating the segmentation quality, assumes a crucial importance. Video segmentation technology has received considerable attention in the literature, with algorithms being proposed to address various types of applications. However, the segmentation quality performance evaluation of those algorithms is often ad hoc, and a well-established solution is not available. In fact, the field of objective segmentation quality evaluation is still maturing; recently, some more efforts have been made, mainly following the emergence of the MPEG object-based coding and description standards. This paper discusses the problem of objective segmentation quality evaluation in its most difficult scenario: standalone evaluation, that is, when a reference segmentation is not available for comparative evaluation. In particular, objective metrics are proposed for the evaluation of standalone segmentation quality for both individual objects and overall segmentation partitions

Supervised Crack Detection and Classification in Images of Road Pavement Flexible Surfaces

Non

A Double-Deep Spatio-Angular Learning Framework for Light Field based Face Recognition

Face recognition has attracted increasing attention due to its wide range of applications, but it is still challenging when facing large variations in the biometric data characteristics. Lenslet light field cameras have recently come into prominence to capture rich spatio-angular information, thus offering new possibilities for advanced biometric recognition systems. This paper proposes a double-deep spatio-angular learning framework for light field based face recognition, which is able to learn both texture and angular dynamics in sequence using convolutional representations; this is a novel recognition framework that has never been proposed before for either face recognition or any other visual recognition task. The proposed double-deep learning framework includes a long short-term memory (LSTM) recurrent network whose inputs are VGG-Face descriptions that are computed using a VGG-Very-Deep-16 convolutional neural network (CNN). The VGG-16 network uses different face viewpoints rendered from a full light field image, which are organised as a pseudo-video sequence. A comprehensive set of experiments has been conducted with the IST-EURECOM light field face database, for varied and challenging recognition tasks. Results show that the proposed framework achieves superior face recognition performance when compared to the state-of-the-art.Comment: Submitted to IEEE Transactions on Circuits and Systems for Video Technolog

Discriminating factors of the kinanthropometric profile of young athletes from different sports

Background: Body morphology, due to its simple applicability, is used to help coaches make decisions during the process of identifying and selecting talent in sports. Objective: to analyze the discriminative kinanthropometric patterns of young Brazilian athletes in different sports. Methods: We evaluated 83 young males (age: 13.1 ± 2.4), 60 of whom were athletes (16-soccer, 11-tennis, 20-swimming, and 13-rowing) and 23 non-athletes (Control group). We evaluated the kinanthropometric profile by dual-energy x-ray emission absorptiometry and by anthropometry. Subsequently, through algorithms programmed in “R” language, a discriminant model was created based on the circumference variables: biceps, hips, waist, and leg; the bone diameters of the humerus and femur, and the components of body composition: total lean mass, total fat mass, bone mineral density, bone mineral content, triceps skinfold, and body adiposity index. Results: Discriminant model was able to discriminate soccer athletes in 93.8% (F:32.098; p=0.000), tennis athletes in 81.8% (F:24.060; p=0.0004), rowing athletes in 80% (F:28.031; p=0.0001), swimming at 100% (F:41.899; p<0.000) and the control group at 91.3% (F:30.132; p<0.0001). In addition, the high bone mineral density was important for the discrimination of soccer athletes (p<0.001), the low body adiposity index for the discrimination of swimming athletes (p<0.001), and the high levels of lean mass for the discrimination of rowers (p<0.001). Conclusion: We conclude that morphological patterns can be used safely, helping to discriminate young athletes from different sports; thus, one more tool to be used in the processes of detection and guidance of young people with talent in the sport

Inquérito Alimentar Nacional e de Atividade Física, IAN-AF 2015-2016: relatório de resultados

Consórcio: Faculdade de Medicina da Universidade do Porto (Carla Lopes, Milton Severo, Andreia Oliveira); Instituto de Saúde Publica da Universidade do Porto (Elisabete Ramos, Sofia Vilela); Faculdade de Ciências da Nutrição e Alimentação da Universidade do Porto (Duarte Torres, Sara Rodrigues); Instituto Nacional de Saúde Doutor Ricardo Jorge (Sofia Guiomar, Luísa Oliveira);: AIDFM - Faculdade de Medicina da Universidade de Lisboa (Paulo Nicola, Violeta Alarcão); Faculdade de Desporto da Universidade do Porto (Jorge Mota) Faculdade de Motricidade Humana da Universidade de Lisboa (Pedro J. Teixeira); SilicoLife (Simão Soares); Faculdade de Medicina da Universidade de Oslo, Noruega (Lene Andersen)Este relatório foi realizado com informação recolhida no âmbito do Inquérito Alimentar Nacional e de Atividade Física (IAN-AF 2015-2016), desenvolvido por um Consórcio que tem como Promotor a Universidade do Porto. O IAN-AF recebeu financiamento do Espaço Económico Europeu concedido pela Islândia, Liechtenstein e Noruega através do Programa EEA Grants - Iniciativas de Saúde Pública, área dos Sistemas de Informação em Saúde (PT06 - 000088SI3). O IAN-AF teve o apoio institucional da Direção-Geral da Saúde, da Administração Central do Sistema de Saúde, das Administrações Regionais de Saúde, das Secretarias Regionais de Saúde dos Açores e da Madeira e da Autoridade Europeia para a Segurança dos Alimentos.O IAN-AF foi financiado pelo Programa Iniciativas em Saúde Pública, EEA-Grants. Este programa resulta do Memorando de Entendimento celebrado entre o Estado português e os países doadores (Islândia, Liechtenstein e Noruega) do Mecanismo Financeiro do Espaço Europeu.info:eu-repo/semantics/publishedVersio