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Ghamisi P.,University of Iceland | Couceiro M.S.,University of Coimbra | Couceiro M.S.,Polytechnic Institute of Coimbra | Martins F.M.L.,Polytechnic Institute of Coimbra | And 2 more authors.
IEEE Transactions on Geoscience and Remote Sensing | Year: 2014

Hyperspectral remote sensing images contain hundreds of data channels. Due to the high dimensionality of the hyperspectral data, it is difficult to design accurate and efficient image segmentation algorithms for such imagery. In this paper, a new multilevel thresholding method is introduced for the segmentation of hyperspectral and multispectral images. The new method is based on fractional-order Darwinian particle swarm optimization (FODPSO) which exploits the many swarms of test solutions that may exist at any time. In addition, the concept of fractional derivative is used to control the convergence rate of particles. In this paper, the so-called Otsu problem is solved for each channel of the multispectral and hyperspectral data. Therefore, the problem of n-level thresholding is reduced to an optimization problem in order to search for the thresholds that maximize the between-class variance. Experimental results are favorable for the FODPSO when compared to other bioinspired methods for multilevel segmentation of multispectral and hyperspectral images. The FODPSO presents a statistically significant improvement in terms of both CPU time and fitness value, i.e., the approach is able to find the optimal set of thresholds with a larger between-class variance in less computational time than the other approaches. In addition, a new classification approach based on support vector machine (SVM) and FODPSO is introduced in this paper. Results confirm that the new segmentation method is able to improve upon results obtained with the standard SVM in terms of classification accuracies. © 1980-2012 IEEE. Source


Couceiro M.S.,Polytechnic Institute of Coimbra | Couceiro M.S.,Ingeniarius Lda. | Clemente F.M.,College of Education of Coimbra | Clemente F.M.,University of Coimbra | And 3 more authors.
Entropy | Year: 2014

The game of football demands new computational approaches to measure individual and collective performance. Understanding the phenomena involved in the game may foster the identification of strengths and weaknesses, not only of each player, but also of the whole team. The development of assertive quantitative methodologies constitutes a key element in sports training. In football, the predictability and stability inherent in the motion of a given player may be seen as one of the most important concepts to fully characterise the variability of the whole team. This paper characterises the predictability and stability levels of players during an official football match. A Fractional Calculus (FC) approach to define a player's trajectory. By applying FC, one can benefit from newly considered modeling perspectives, such as the fractional coefficient, to estimate a player's predictability and stability. This paper also formulates the concept of attraction domain, related to the tactical region of each player, inspired by stability theory principles. To compare the variability inherent in the player's process variables (e.g., distance covered) and to assess his predictability and stability, entropy measures are considered. Experimental results suggest that the most predictable player is the goalkeeper while, conversely, the most unpredictable players are the midfielders. We also conclude that, despite his predictability, the goalkeeper is the most unstable player, while lateral defenders are the most stable during the match. © 2014 by the authors. Source


de Melo R.J.E.S.,College of Education of Coimbra | Gomes R.A.M.,University of Coimbra
Open Sports Sciences Journal | Year: 2016

Although Nature Sports are considered a growing phenomenon around the world, there is a lack of research and data about the organizations that are developing these activities. The purpose of this paper is to characterize the Nature Sports Organizations operating in mainland Portugal. Data was obtained through an online survey questionnaire applied to organizations which promoted Nature Sports in Portugal, both from private and public sectors, and 166 answers were obtained. Three main types of organizations were found based on their legal form and organizational vocation: sport tourism companies, sport clubs, and associations (environmentalists, cultural, sportive, recreational and others) which were further characterized by their organization profile, supply and demand. The results show significant statistical differences between the different types of organizations regarding their age, number of collaborators, and type of activities offered, as also the number and provenience of the practitioners. The data also enclose implications for the Nature Sports policies and Nature Sports Organizations management that will be discussed. © Melo and Gomes; Licensee Bentham Open. Source


Clemente F.M.,Polytechnic Institute of Coimbra | Clemente F.M.,College of Education of Coimbra | Couceiro M.S.,University of Coimbra | Martins F.M.L.,Polytechnic Institute of Coimbra | Mendes R.S.,Polytechnic Institute of Coimbra
Motriz. Revista de Educacao Fisica | Year: 2014

The aim of this pilot study was propose a set of network methods to measure the specific properties of football teams. These metrics were organized on "meso" and "micro" analysis levels. Five official matches of the same team on the First Portuguese Football League were analyzed. An overall of 577 offensive plays were analyzed from the five matches. From the adjacency matrices developed per each offensive play it were computed the scaled connectivity, the clustering coefficient and the centroid significance and centroid conformity. Results showed that the highest values of scaled connectivity were found in lateral defenders and central and midfielder players and the lowest values were found in the striker and goalkeeper. The highest values of clustering coefficient were generally found in midfielders and forwards. In addition, the centroid results showed that lateral and central defenders tend to be the centroid players in the attacking process. In sum, this study showed that network metrics can be a powerful tool to help coaches to understanding the specific team's properties, thus supporting decision-making and improving sports training based on match analysis. Source


Clemente F.M.,College of Education of Coimbra | Clemente F.M.,University of Coimbra | Couceiro M.S.,Ingeniarius Lda. | Martins F.M.L.,College of Education of Coimbra | And 2 more authors.
Journal of Human Kinetics | Year: 2015

The aim of this study was to propose a set of network methods to measure the specific properties of a team. These metrics were organised at macro-analysis levels. The interactions between teammates were collected and then processed following the analysis levels herein announced. Overall, 577 offensive plays were analysed from five matches. The network density showed an ambiguous relationship among the team, mainly during the 2nd half. The mean values of density for all matches were 0.48 in the 1st half, 0.32 in the 2nd half and 0.34 for the whole match. The heterogeneity coefficient for the overall matches rounded to 0.47 and it was also observed that this increased in all matches in the 2nd half. The centralisation values showed that there was no 'star topology'. The results suggest that each node (i.e., each player) had nearly the same connectivity, mainly in the 1st half. Nevertheless, the values increased in the 2nd half, showing a decreasing participation of all players at the same level. Briefly, these metrics showed that it is possible to identify how players connect with each other and the kind and strength of the connections between them. In summary, it may be concluded that network metrics can be a powerful tool to help coaches understand team's specific properties and support decision-making to improve the sports training process based on match analysis. © Editorial Committee of Journal of Human Kinetics. Source

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