An Overview of Contour Detection Approaches

Object contour plays an important role in fields such as semantic segmentation and image classification. However, the extraction of contour is a difficult task, especially when the contour is incomplete or unclosed. In this paper, the existing contour detection approaches are reviewed and roughly divided into three categories: pixel-based, edge-based, and region-based. In addition, since the traditional contour detection approaches have achieved a high degree of sophistication, the deep convolutional neural networks (DCNNs) have good performance in image recognition, therefore, the DCNNs based contour detection approaches are also covered in this paper. Moreover, the future development of contour detection is analyzed and predicted.     

The crowd as a cameraman: on-stage display of crowdsourced mobile video at large-scale events

Multimedia Tools and Applications - Recording videos with smartphones at large-scale events such as concerts and festivals is very common nowadays. These videos register the atmosphere of the event...     

Web-scale provenance reconstruction of implicit information diffusion on social media

Fast, massive, and viral data diffused on social media affects a large share of the online population, and thus, the (prospective) information diffusion mechanisms behind it are of great interest to researchers. The (retrospective) provenance of such data is equally important because it contributes to the understanding of the relevance and trustworthiness of the information. Furthermore, computing provenance in a timely way is crucial for particular use cases and practitioners, such as online journalists that promptly need to assess specific pieces of information. Social media currently provide insufficient mechanisms for provenance tracking, publication and generation, while state-of-the-art on social media research focuses mainly on explicit diffusion mechanisms (like retweets in Twitter or reshares in Facebook).The implicit diffusion mechanisms remain understudied due to the difficulties of being captured and properly understood. From a technical side, the state of the art for provenance reconstruction evaluates small datasets after the fact, sidestepping requirements for scale and speed of current social media data. In this paper, we investigate the mechanisms of implicit information diffusion by computing its fine-grained provenance. We prove that explicit mechanisms are insufficient to capture influence and our analysis unravels a significant part of implicit interactions and influence in social media. Our approach works incrementally and can be scaled up to cover a truly Web-scale scenario like major events. We can process datasets consisting of up to several millions of messages on a single machine at rates that cover bursty behaviour, without compromising result quality. By doing that, we provide to online journalists and social media users in general, fine grained provenance reconstruction which sheds lights on implicit interactions not captured by social media providers. These results are provided in an online fashion which also allows for fast relevance and trustworthiness assessment.     

Enhanced injection method for synthetically generated turbulence within the flow domain of eddy-resolving simulations

The quality of eddy-resolving turbulence simulations strongly depends on appropriate inflow conditions. In most cases they have to be time-dependent and satisfy certain conditions for the first (mean velocities) and second-order moments (Reynolds stresses) as well as concerning suitable length scales. To mimic a physically realistic incoming flow, synthetically generated turbulent velocity fluctuations superimposed on the mean velocity field are a valuable solution. However, the resolution of the grid near the inlet has to be sufficiently fine to avoid excessive damping of the turbulence intensity. In order to circumvent this problem, the injection of synthetically generated inflow data not at the inlet itself but inside the flow domain near the area of interest, where the grid is typically much finer, is an elegant loophole. In the present study two different injection techniques based on a source-term formulation are analyzed and evaluated. In addition to these techniques the injected data are weighted by a Gaussian distribution defining the influence area. In the recent work the definition of the influence area is enhanced compared to the initial version of Schmidt and Breuer (2017) extending the application range. The case of a rather small influence area in comparison with the grid cell size is now tackled which is often relevant for industrial applications.The flow past a wall-mounted hemisphere is chosen as test case. The bluff body is exposed to a thick turbulent boundary layer at Re $=$ 50,000. The generation of the turbulent velocity fluctuations in the present investigation relies on the digital filter concept, but the injection techniques evaluated are not restricted to this inflow generator. The synthetic turbulent velocity fluctuations are injected about one diameter upstream of the hemisphere. Wall-resolved large-eddy simulations are carried out for two grid resolutions and the corresponding results are analyzed and compared with the reference measurements by Wood et al. (2016). Finally, one injection technique is found to be clearly superior to the other, since it guarantees the correct level of the velocity fluctuations and the reproduction of the autocorrelations.     

A Report on the First IFIP/IEEE Workshop on Quality of Experience Centric Management (QCMan 2013)

The first IFIP/IEEE international workshop on quality of experience (QoE) centric management (QCMan 2013) was held on May 31, 2013 in Ghent, Belgium. This report summarizes the keynotes, presentations and discussions in QCMan 2013 and provides a high-level view of ideas, challenges, strategies and the current state of the research activities in the field of QoE management.     

A Comparison Between the Interpolated Bounce-Back Scheme and the Immersed Boundary Method to Treat Solid Boundary Conditions for Laminar Flows in the Lattice Boltzmann Framework

In this paper, the interpolated bounce-back scheme and the immersed boundary method are compared in order to handle solid boundary conditions in the lattice Boltzmann method. These two approaches are numerically investigated in two test cases: a rigid fixed cylinder invested by an incoming viscous fluid and an oscillating cylinder in a calm viscous fluid. Findings in terms of velocity profiles in several cross sections are shown. Differences and similarities between the two methods are discussed, by emphasizing pros and cons in terms of stability and computational effort of the numerical algorithm.     

Integration of XML Schemas at various “severity” levels

This paper presents a novel approach for the integration of a set of eXtensible Markup Language (XML) Schemas. The proposed approach is specialized for XML, almost automatic, semantic and “light”. Further, an original peculiarity is that it is parametric w.r.t. a “severity” level against which the integration task is performed. The paper describes the approach in all details, illustrates various theoretical results, presents the experiments we have performed for testing it and, finally, compares it with various related approaches already proposed in the literature.     

Detecting change-points in multidimensional stochastic processes

A general test statistic for detecting change-points in multidimensional stochastic processes with unknown parameters is proposed. The test statistic is specialized to the case of detecting changes in sequences of covariance matrices. Large-sample distributional results are presented for the test statistic under the null hypothesis of no-change. The finite-sample properties of the test statistic are compared with two other test statistics proposed in the literature. Using a binary segmentation procedure, the potential of the various test statistics is investigated in a multidimensional setting both via simulations and the analysis of a real life example. In general, all test statistics become more effective as the dimension increases, avoiding the determination of too many “incorrect” change-point locations in a one-dimensional setting.     

Discrete image modelling for piecewise constant segmentation by artificial neural networks

The problem of an image best approximation within the class of piecewise constant functions is considered. This allows a simpler data representation with a lower number of grey levels while retaining all information relevant to the particular application considered. The approximant can be found by solving a segmentation problem. The search for a solution is solved efficiently by training an artificial neural network (ANN) on a suitable set of templates by a standard procedure. The samples of the training alphabet fit the signal's local behaviour in the homogeneous image subregions and in the regions crossed by the edges. Therefore the original image domain is partitioned into disjoint 2D intervals (tiling), and for each one of them, the network selects the alphabet element closest to the corresponding image component. The main motivation of this work consists in devising a methodology suitable for real-time applications; indeed, the ANN tool is attractive for a hardware implementation.     

Emotionally charged earcons reveal affective congruency effects

In the present study, the affective impact of earcons on stimulus classification is investigated. We show, using a picture-categorization task, that the affective connotation of earcons in major and minor mode (representing positive and negative valence, respectively) can be congruent or incongruent with response valence. Twenty participants classified pictures of animals and instruments in 256 trials, using positive and negative Yes or No responses. Together with the pictures, either a chord in major mode or minor mode was played. The affective valence of the chords either did or did not match the valence of responses. Response-time latencies show congruency effects of the matching and non matching sound and response valences, indicating that it is important to carefully investigate human-computer interfaces for potential affective congruency effects, as these can either facilitate or inhibit user performance.