ROSy+: 3D Object Pose Normalization Based on PCA and Reflective Object Symmetry with Application in 3D Object Retrieval

A novel pose normalization method based on 3D object reflective symmetry is presented. It is a general purpose global pose normalization method; in this paper it is used to enhance the performance of a 3D object retrieval pipeline. Initially, the axis-aligned minimum bounding box of a rigid 3D object is modified by requiring that the 3D object is also in minimum angular difference with respect to the normals to the faces of its bounding box. To estimate the modified axis-aligned bounding box, a set of predefined planes of symmetry are used and a combined spatial and angular distance, between the 3D object and its symmetric object, is calculated. By minimizing the combined distance, the 3D object fits inside its modified axis-aligned bounding box and alignment with the coordinate system is achieved. The proposed method is incorporated in a hybrid scheme, that serves as the alignment method in a 3D object retrieval system. The effectiveness of the 3D object retrieval system, using the hybrid pose normalization scheme, is evaluated in terms of retrieval accuracy and demonstrated using both quantitative and qualitative measures via an extensive consistent evaluation on standard benchmarks. The results clearly show performance boost against current approaches.     

Introducing web service accessibility assessment techniques through a unified quality of service context

This paper introduces novel web service (WS) accessibility assessment techniques through a unified Quality of Services (QoS) context. The goal is to enable future QoS-aware service selection systems to select and provide accessible web services, ones that are properly designed so as to allow their consumption from end-user applications, used from people with disabilities. In this line, a WS accessibility assessment Framework (WSaaF) has been developed, on the basis of WS accessibility guidelines, dealing with accessibility issues that can appear both on the presentation level of content delivered through WSs and on the content level itself. The WSaaF and its guidelines follow the rationale behind W3C WCAG 2.0-based accessibility standardization of web content. It provides the basis toward building future accessible WSs, a task that can be further facilitated by the use of an appropriate Tool (WSaaT), developed with the aim to provide automatic assessment of services, against guidelines of the proposed framework. Then, the WS accessibility attribute is introduced, as a metric that can be used in conjunction to ones typically utilized so far, within QoS-aware service selection systems. As a result, a novel unified QoS framework is proposed, incorporating the notion of accessibility in the service selection process. The proposed unified QoS framework can eventually lead to the provision of services, which are selected from appropriate repositories and better suit the special needs of people with disabilities.     

A Semi-Parametric Non-linear Neural Network Filter: Theory and Empirical Evidence

In this work, we decompose a time series into trend and cycle by introducing a novel de-trending approach based on a family of semi-parametric artificial neural networks. Based on this powerful approach, we propose a relevant filter and show that the proposed trend specification is a global approximation to any arbitrary trend. Furthermore, we prove formally a famous claim by Kydland and Prescott (1981, 1997) that over long time periods, the average value of the cycles is zero. A simple procedure for the econometric estimation of the model is developed as a seven-step algorithm, which relies on standard techniques, where all relevant measures may be computed routinely. Next, using relevant DGPs, we compare and show by means of Monte Carlo simulations that our approach is superior to Hodrick–Prescott (HP) and Baxter and King (BK) regarding the generated distortionary effects and the ability to operate in various frequencies, including changes in volatility, amplitudes and phase. In fact, while keeping the structure of the model relatively simple, our approach is perfectly capable of addressing the case of stochastic trend, in the sense that the generated distortionary effects in the near unit root case are minimal and, by all means, considerably fewer than those generated by HP and BK. Application to EU15 business cycles clustering is presented and the empirical results are consistent with the rigorous theoretical framework developed in this work.     

Towards a detection of grassland cutting practices with dual polarimetric TerraSAR-X data

In this study, polarimetric synthetic aperture radar (SAR) parameters are analysed and compared with in situ measurements in order to develop a methodology for detecting cutting practices within grassland areas. The grasslands were monitored with TerraSAR-X radar imaging in dual polarization HH/VV mode and are located near the banks of the Kasari River, close to the Baltic Sea coast of Estonia. The parameters analysed include HH, VV, HH + VV, and HH – VV backscatter, HH/VV polarimetric coherence magnitude and phase, T₁₂ polarimetric coherence magnitude and phase, and also dual polarimetric entropy, alpha, and alpha dominant parameters. Using these parameters derived from the dual polarimetric TerraSAR-X data set, it was virtually impossible to distinguish tall grass (height >30 cm) from short grass (height <30 cm). On the other hand, it proved feasible to detect areas where grass had been cut and left on the ground. Several parameters showed specific behaviour for the state of grassland and the most notable change was found in the dual polarimetric dominant scattering alpha angle. This angle changed from 10° to 25° after tall grass had been cut and left on the ground. This behaviour of the dominant scattering alpha angle can effectively be described using a particle scattering model for vegetation backscattering.     

User Interface Design Principles for Interactive Television Applications

Interactive Television (ITV) applications gratify entertainment needs and leisure activities in a relaxed domestic context. Then, the mentality of efficiency and task completion implied by many user interface heuristics may not be suitable for design and expert evaluation of ITV. In line with the contemporary movement towards the design of the user experience of computing systems, a set of design principles for ITV applications has been developed. Instead of information technology, the field of communication science was explored, which has accumulated an extensive theory of TV usage and viewer behavior. For the empirical validation, the principles were employed in the design of an interactive music TV application. The results suggest that a small set of generic principles could support the systematic design of ITV applications and produce a design rationale that is traceable and comparable to alternative solutions, during the process of early product development. In addition, the principles could be refined into specific guidelines and further employed to support expert evaluation techniques.     

Towards the automatic detection of spontaneous agreement and disagreement based on nonverbal behaviour: A survey of related cues,databases, and tools

While detecting and interpreting temporal patterns of nonverbal behavioural cues in a given context is a natural and often unconscious process for humans, it remains a rather difficult task for computer systems. Nevertheless, it is an important one to achieve if the goal is to realise a naturalistic communication between humans and machines. Machines that are able to sense social attitudes like agreement and disagreement and respond to them in a meaningful way are likely to be welcomed by users due to the more natural, efficient and human‐centred interaction they are bound to experience. This paper surveys the nonverbal behavioural cues that could be present during displays of agreement and disagreement; discusses a number of methods that could be used or adapted to detect these suggested cues; lists some publicly available databases these tools could be trained on for the analysis of spontaneous, audiovisual instances of agreement and disagreement, it examines the few existing attempts at agreement and disagreement classification, and finally discusses the challenges in automatically detecting agreement and disagreement.     

Local community detection with hints

Local community detection is a widely used method for identifying groups of nodes starting from seeding nodes. The seed(s) are usually selected either randomly or based only on structural properties of the network. However, in many cases the choice of seed(s) incorporates external knowledge that attaches to these nodes an additional importance for their community. This knowledge, may be derived from an expert on the domain, or may arise from the network’s side information and it constitutes our motivation for the present work; this additional information about the importance of seed(s) can be exploited for detection of better and more relevant communities. We call such biased seed(s), hint(s). Our approach, is to reflect the importance of hints by changing appropriately the network in their vicinity. To the best of our knowledge, no such viewpoint of the seeding nodes in local community detection has been considered before. The aim of this study is to identify a single community which contains the hint(s). Our key contribution is the proposed Hint Enhancement Framework(HEF) that applies a two-step procedure to discover the community of the hint(s): 1) it changes the network by amplifying the hint(s) using re-weighting or re-wiring strategies so as to materialize the bias towards them and 2) it applies local community detection algorithms on the altered network of step 1. We experimentally evaluate HEF in synthetic and real datasets, and demonstrate the positive aspects of the framework in identifying better communities, in comparison with plain local community detection algorithms as well as a global one.

     

3D measures exploitation for a monocular semi-supervised fall detection system

Falls have been reported as the leading cause of injury-related visits to emergency departments and the primary etiology of accidental deaths in elderly. Thus, the development of robust home surveillance systems is of great importance. In this article, such a system is presented, which tries to address the fall detection problem through visual cues. The proposed methodology utilizes a fast, real-time background subtraction algorithm, based on motion information in the scene and pixels intensity, capable to operate properly in dynamically changing visual conditions, in order to detect the foreground object. At the same time, it exploits 3D space’s measures, through automatic camera calibration, to increase the robustness of fall detection algorithm which is based on semi-supervised learning approach. The above system uses a single monocular camera and is characterized by minimal computational cost and memory requirements that make it suitable for real-time large scale implementations.     

Algorithm visualization using tree graphs

Recent advances in graphics workstations allow the development of improved visualization tools for algorithm and program development. Algorithm visualization permits better analysis, development, and presentation of the algorithm characteristics. In this paper, we present a simple algorithm visualization technique using tree graphs. The technique is applied to the visualization of three sorting algorithms: the bubble sort, the quicksort, and the merge and sort, and one matrix algorithm, the Gaussian elimination. Key states of the data are displayed on the nodes, while the graph itself represents the underlying structure of the algorithm. All graphics are displayed under the X Window environment using simple graphics and window programming techniques.