Qualitative relations between moving objects in a network changing its topological relations

The Qualitative Trajectory Calculus on Networks (QTC_N) defines qualitative relations between two continuously moving point objects (MPOs) moving along a network. As prevailing in other research, this network is presumed static in QTC_N. Actually, in many cases, networks are dynamic entities. For example in a road network, the opening of a bridge can temporarily close the connection between two junctions; traffic jams and traffic lights increase the time needed to travel from A to B. Therefore, it is interesting to examine what happens with qualitative relations between two continuously moving point objects if there are changes in the network. In this paper, we introduce QTC_DN^′, being the Qualitative Trajectory Calculus on Changing Networks able to handle topological network changes. Potential applications of the calculus in transportation are highlighted, clearly illustrating the usefulness of the calculus.     

Coupled object detection and tracking from static cameras and moving vehicles

We present a novel approach for multi-object tracking which considers object detection and spacetime trajectory estimation as a coupled optimization problem. Our approach is formulated in an MDL hypothesis selection framework, which allows it to recover from mismatches and temporarily lost tracks. Building upon a multi-view/multi-category object detector, it localizes cars and pedestrians in the input images. The 2D object detections are converted to 3D observations, which are accumulated in a world coordinate frame. Trajectory analysis in a spacetime window yields physically plausible trajectory candidates. Tracking is achieved by performing model selection after every frame. At each time instant, our approach searches for the globally optimal set of spacetime trajectories which provides the best explanation for the current image and all evidence collected so far, while satisfying the constraints that no two objects may occupy the same physical space, nor explain the same image pixels at any time. Successful trajectory hypotheses are then fed back to guide object detection in future frames. The resulting approach can initialize automatically and track a large and varying number of objects from both static and moving cameras. We evaluate our approach on several challenging video sequences with both a surveillance-type scenario and a scenario where the input videos are taken from a moving vehicle.     

Tracing Field‐Coherent Quad Layouts

Given a cross field over a triangulated surface we present a practical and robust method to compute a field aligned coarse quad layout over the surface. The method works directly on a triangle mesh without requiring any parametrization and it is based on a new technique for tracing field‐coherent geodesic paths directly on a triangle mesh, and on a new relaxed formulation of a binary LP problem, which allows us to extract both conforming quad layouts and coarser layouts containing t‐junctions. Our method is easy to implement, very robust, and, being directly based on the input cross field, it is able to generate better aligned layouts, even with complicated fields containing many singularities. We show results on a number of datasets and comparisons with state‐of‐the‐art methods.     

A combined post-filtering method to improve accuracy of variational optical flow estimation

We present a novel combined post-filtering (CPF) method to improve the accuracy of optical flow estimation. Its attractive advantages are that outliers reduction is attained while discontinuities are well preserved, and occlusions are partially handled. Major contributions are the following: First, the structure tensor (ST) based edge detection is introduced to extract flow edges. Moreover, we improve the detection performance by extending the traditional 2D spatial edge detector into spatial-scale 3D space, and also using a gradient bilateral filter (GBF) to replace the linear Gaussian filter to construct a multi-scale nonlinear ST. GBF is useful to preserve discontinuity but it is computationally expensive. A hybrid GBF and Gaussian filter (HGBGF) approach is proposed by means of a spatial-scale gradient signal-to-noise ratio (SNR) measure to solve the low efficiency issue. Additionally, a piecewise occlusion detection method is used to extract occlusions. Second, we apply a CPF method, which uses a weighted median filter (WMF), a bilateral filter (BF) and a fast median filter (MF), to post-smooth the detected edges and occlusions, and the other flat regions of the flow field, respectively. Benchmark tests on both synthetic and real sequences demonstrate the effectiveness of our method.     

Trust and online information-sharing in close relationships: a cross-cultural perspective

Culture has an essential influence on online trust building in close relationships. The objective of this study was to investigate the effect of culture on interpersonal trust and online information-sharing in close relationships. A social dilemma game experiment was conducted by inviting close friend dyads from interdependent (that is, Chinese) or independent cultures (that is, German). Their trust and information-sharing performance in either online text chatting or face-to-face communication were examined. The results showed that in close relationships, Chinese participants had higher interpersonal trust and objective-sharing performance than German participants; Chinese trust and self-evaluated performance were not influenced by communication media; in comparison, German participants’ trust and self-evaluated performance significantly dropped online. The results confirmed that interdependent people are more relation-oriented in building their trust than independent people. Implications for online information-sharing management and the interface design were provided.     

An interactive local flattening operator to support digital investigations on artwork surfaces

Analyzing either high-frequency shape detail or any other 2D fields (scalar or vector) embedded over a 3D geometry is a complex task, since detaching the detail from the overall shape can be tricky. An alternative approach is to move to the 2D space, resolving shape reasoning to easier image processing techniques. In this paper we propose a novel framework for the analysis of 2D information distributed over 3D geometry, based on a locally smooth parametrization technique that allows us to treat local 3D data in terms of image content. The proposed approach has been implemented as a sketch-based system that allows to design with a few gestures a set of (possibly overlapping) parameterizations of rectangular portions of the surface. We demonstrate that, due to the locality of the parametrization, the distortion is under an acceptable threshold, while discontinuities can be avoided since the parametrized geometry is always homeomorphic to a disk. We show the effectiveness of the proposed technique to solve specific Cultural Heritage (CH) tasks: the analysis of chisel marks over the surface of a unfinished sculpture and the local comparison of multiple photographs mapped over the surface of an artwork. For this very difficult task, we believe that our framework and the corresponding tool are the first steps toward a computer-based shape reasoning system, able to support CH scholars with a medium they are more used to.     

Application-aware scheduling for VoIP in Wireless Mesh Networks

Wireless Mesh Networks (WMNs) are seen as a means to provide last mile connections in Next Generation Networks (NGNs). Because of their auto-configuration capabilities and the low deployment cost WMNs are considered to be an efficient solution for the support of multiple voice, video and data services in NGNs. This paper looks at the optimal provision of resources in WMNs for Voice over IP (VoIP) traffic, which has strict performance requirements in terms of delay, jitter and packet loss. In WMNs, because of the challenges introduced by wireless multi-hop transmissions and limited resources, providing performance quality for VoIP comparable to the voice quality in the traditional circuit-switched networks is a major challenge.This paper analyses different scheduling mechanisms for TDMA-based access control in mesh networks as specified in the IEEE 802.16-2004 WiMAX standard. The performance of the VoIP applications when different scheduling mechanisms are deployed is analysed on a variety of topologies using ns-2 simulation and mathematical analysis. The paper concludes that on-demand scheduling of VoIP traffic – typically deployed in 802.11-based WMNs – is not able to provide the required VoIP quality in realistic mesh WiMAX network scenarios and is therefore not optimal from a network operator’s point of view. Instead, it is shown, that continuous scheduling is much better suited to serve VoIP traffic. The paper then proposes a new VoIP-aware resource coordination scheme and shows, through simulation, that the new scheme is scalable and provides good quality for VoIP service in a wide range of network scenarios. The results shown in the paper prove that the new scheme is resilient to increasing hop count, increasing number of simultaneous VoIP sessions and the background traffic load in the network. Compared to other resource coordination schemes the VoIP-aware scheduler significantly increases the number of supported calls.     

The importance of Monte Carlo simulations in modeling detectors for Nuclear Medicine

Nowadays, Monte Carlo techniques are very common for the development of Nuclear Medicine systems. Simulations can be very helpful for the optimization of SPECT and PET cameras, and for investigating the importance of several physical effects involved in image formation. In this paper, a simulation study for evaluating various aspects inuencing image formation in detectors for Nuclear Medicine is presented. To this end, the EGSnrc Monte Carlo code has been used, which transports photons and electrons in any material and handling various physical phenomena. Here, some detector systems are simulated, consisting of a parallel-hole collimator and a pixellated scintillator. Various effects are investigated, such as electron transport, uorescence photons, collimator septa penetration. Results are evaluated by means of energy spectra, photon uxes, uniformity of response, SNR and spatial resolution.     

Methodology for evaluating automated map generalization in commercial software

This paper presents a methodology developed for a study to evaluate the state of the art of automated map generalization in commercial software without applying any customization. The objectives of this study are to learn more about generic and specific requirements for automated map generalization, to show possibilities and limitations of commercial generalization software, and to identify areas for further research. The methodology had to consider all types of heterogeneity to guarantee independent testing and evaluation of available generalization solutions. The paper presents the two main steps of the methodology. The first step is the analysis of map requirements for automated generalization, which consisted of sourcing representative test cases, defining map specifications in generalization constraints, harmonizing constraints across the test cases, and analyzing the types of constraints that were defined. The second step of the methodology is the evaluation of generalized outputs. In this step, three evaluation methods were integrated to balance between human and machine evaluation and to expose possible inconsistencies. In the discussion the applied methodology is evaluated and areas for further research are identified.     

Spatio-temporal random fields: compressible representation and distributed estimation

Modern sensing technology allows us enhanced monitoring of dynamic activities in business, traffic, and home, just to name a few. The increasing amount of sensor measurements, however, brings us the challenge for efficient data analysis. This is especially true when sensing targets can interoperate—in such cases we need learning models that can capture the relations of sensors, possibly without collecting or exchanging all data. Generative graphical models namely the Markov random fields (MRF) fit this purpose, which can represent complex spatial and temporal relations among sensors, producing interpretable answers in terms of probability. The only drawback will be the cost for inference, storing and optimizing a very large number of parameters—not uncommon when we apply them for real-world applications. In this paper, we investigate how we can make discrete probabilistic graphical models practical for predicting sensor states in a spatio-temporal setting. A set of new ideas allows keeping the advantages of such models while achieving scalability. We first introduce a novel alternative to represent model parameters, which enables us to compress the parameter storage by removing uninformative parameters in a systematic way. For finding the best parameters via maximum likelihood estimation, we provide a separable optimization algorithm that can be performed independently in parallel in each graph node. We illustrate that the prediction quality of our suggested method is comparable to those of the standard MRF and a spatio-temporal k-nearest neighbor method, while using much less computational resources.