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An iterative method for the computation of a non-negative sparse principal component basis is presented. The iterative nature of the proposed method enables online learning, useful for time-evolving data, such as stock price time series. This way, the computation of the covariance matrix is avoided whenever new data become available. Due to the non-negative constraints explicitly enforced, the extracted eigenvectors turn to be sparse without employing any sparsity controlling parameter. Accordingly, only one parameter has to be tuned, i.e., the learning rate. Also, the complexity of the method under study is approximately linear under plausible assumptions. To assess the method’s performance, comprehensive experimental evaluation is conducted on both image and financial data. The proposed method is thoroughly compared to state-of-the-art and yields promising experimental findings.

This article presents FORTIKA, an ongoing EU-funded project which is a resilient cyber-security solution that can easily be tailored and adjusted to the versatile and ever changing needs of small and medium-sized enterprises (SMEs). To fulfill its vision, FORTIKA adopts a security-by-design hybrid approach that adequately integrates hardware and software with business needs and behavioral patterns at the individual and organizational levels to introduce an FPGA-powered middleware security layer as an add-on to existing network gateways, orientating small business users to trusted cyber-security services (through FORTIKA's marketplace) packaged in tailored solutions for each enterprise, and further extended to accommodate security intelligence and encourage security-friendly behavioral and organizational changes. Finally, the introduction of a software-defined smart ecosystem in the FORTIKA marketplace provides the feature of a lightweight solution, which offers virtualized security services (with minimum downloading requirements). Users (i.e., SMEs) may utilize a variety of services and share profiling information with the service providers in return for tailored security services aligned with their actual needs. FORTIKA marketplace also functions as a single point of access for the profiling information for each SME. Preliminary evaluation results indicate that users can upload bundles to the marketplace repository in approximately 4 to 5 minutes, download bundles from the FORTIKA marketplace to the gateway in approximately 20 seconds, and deploy them in an average of 4.5 ms.

The reduction of road congestion requires intuitive urban congestion-control platforms that can facilitate transport stakeholders in decision making. Interactive ITS visual analytics tools can be of significant assistance, through their real-time interactive visualizations, supported by advanced data analysis algorithms. In this paper, an interactive visual analytics platform is introduced that allows the exploration of historical data and the prediction of future traffic through a unified interactive interface. The platform is backed by several data analysis techniques, such as road behavioral visualization and clustering, anomaly detection, and traffic prediction, allowing the exploration of behavioral similarities between roads, the visual detection of unusual events, the testing of hypotheses, and the prediction of traffic flow after hypothetical incidents imposed by the human operator. The accuracy of the prediction algorithms is verified through benchmark comparisons, while the applicability of the proposed toolkit in facilitating decision making is demonstrated in a variety of use case scenarios, using real traffic and incident data sets.

Mobile devices are evolving and becoming increasingly popular over the last few years. This growth, however, has exposed mobile devices to a large number of security threats. Malware installed in smartphones can be used for a variety of malicious purposes, including stealing personal data, sending spam SMSs, and launching Denial of Service (DoS) attacks against core network components. Authentication and access-control-based techniques, employed by network operators fail to provide integral protection against malware threats. In order to solve this issue, the activity of each mobile device in the network must be taken into account, and combined with the activities of all the other devices. The communication activity in the mobile network has a source, a destination, and possibly communication weights (e.g., the number of calls between two mobile devices). This relational nature of the communication activity is naturally represented with graphs. This indicates that graphs can be utilized in order to provide better representations of the entire network activity, and lead to better detection results when compared to methods that consider the activity of each mobile device individually. Towards this end, this paper proposes a novel graph-based descriptor for the detection of anomalies in mobile networks, using billing-related information. The graph-based descriptor represents the total activity in the network. Smaller graphs are afterwards extracted from the graph-based descriptor, each one representing the activity of one mobile device (e.g., Calls or SMSs), while multiple features are calculated for each such graph. These features are subsequently used for the supervised classification on network events, and the identification of anomalous mobile devices. Experimental results and comparison of the proposed anomaly detection method to the existing work, show that the graph-based descriptor has superior performance in a variety of scenarios.

The exploration of the potential correlations of traffic conditions between roads in large urban networks, which is of profound importance for achieving accurate traffic prediction, often implies high computational complexity due to the implicated network topology. Hence, focal methods are required for dealing with the urban network complexity, reducing the performance requirements that are associated to the classical network search techniques (e.g., Breadth First Search). This paper introduces a graph-theory-based technique for managing spatial dependence between roads of the same network. In particular, after representing the traffic network as a graph, the local neighbors of each road are extracted using Breadth First Search graph traversal algorithm and a lower complexity variant of it. A Pearson product–moment correlation-coefficient-based metric is applied on the selected graph nodes for a prescribed number of level sets of neighbors. In order to evaluate the impact of the new method to the traffic prediction accuracy achieved, the most correlated roads are used to build a STARIMA model, taking also into account the possible time delays of traffic conditions between the interrelated roads. The proposed technique is benchmarked using traffic data from two different cities: Berlin, Germany, and Thessaloniki, Greece. Benchmark results not only indicate significant improvement on the computational time required for calculating traffic correlation metric values but also reveal that a different variant works better in different network topologies, after comparison to third-party approaches.

Using data visualization techniques can be of significant assistance in exploring multimedia databases. Data visualization is typically addressed as a unimodal learning task, where data are described with only one feature set, or modality. However, using multiple data modalities has been proved to increase the performance of learning methods. In this paper a novel approach for exploiting the multiple available modalities for visualization is proposed, motivated by the field of multi-objective optimization. Initially, each modality is considered separately. A graph of the dissimilarities among the data and the corresponding minimum spanning tree are formed. The suitability of a particular data placement is quantified using multiple cost functions, one for each modality. The utilized cost functions are defined in terms of graph aesthetic measures, computed for the unimodal minimum spanning trees. The cost functions are then used as the multiple objectives of a multi-objective optimization problem. Solving the problem results in a set of Pareto optimal placements, which represent different trade-offs among the various objectives. Experimental evaluation shows that the proposed method outperforms current multimodal visualization methods both in discovering more visualizations and in producing ones which are more aesthetically pleasing and easily perceivable.

Our proposed sketch-based search and retrieval system in map databases shows the promise that intuitive search can be possible even when searching in nonannotated conventional map data. We plan to perform further work in this area to allow for more intuitive and semantic queries, applications for mobile and ubiquitous computing, and integration to existing map searching applications.

This letter presents a novel probabilistic framework for augmenting the recognition performance of biometric systems with information from continuous soft biometric (SB) traits. In particular, by modelling the systematic error induced by the estimation of the SB traits, a modified efficient recognition probability can be extracted including information related both to the hard and SB traits. The proposed approach is applied without loss of generality in the case of gait recognition, where two state-of-the-art gait recognition systems are considered as hard biometrics and the height and stride length of the individuals are considered as SBs. Experimental validation on two known, large datasets illustrates significant advances in the recognition performance with respect to both identification and authentication rates.

The control plane of the Internet relies entirely on BGP as the interdomain routing protocol to maintain and exchange routing information between large network providers and their customers. However, an intrinsic vulnerability of the protocol is its inability to validate the integrity and correctness of routing information exchanged between peer routers. As a result, it is relatively easy for people with malicious intent to steal legitimate IP blocks through an attack known as prefix hijacking, which essentially consists of injecting bogus routing information into the system to redirect or subvert network traffic. In this article, we give a short survey of visualization methods that have been developed for BGP monitoring, in particular for the identification of prefix hijacks. Our goal is to illustrate how network visualization has the potential to assist an analyst in detecting abnormal routing patterns in massive amounts of BGP data. Finally, we present an analysis of a real validated case of prefix hijacking, which took place between April and August 2011. We use this hijack case study to illustrate the ongoing work carried out in VIS-SENSE, a European research project that leverages visual analytics to develop more effective tools for BGP monitoring and prefix hijack detection.

This paper presents work conducted toward the biosignals-based automatic recognition of boredom, induced during video-game playing. For this purpose, common biosignal feature extraction methods were exploited and their capability to identify boredom was assessed. Moreover, for the first time, Legendre and Krawtchouk moments, as well as novel moment variations, were extracted as biosignal features and their potential toward automatic affect recognition was examined using the specific application scenario. The present analysis was conducted with ECG and GSR data collected from 19 different subjects, while boredom was naturally induced during the repetitive playing of a 3D video game. Conventional biosignal features as well as moment-based ones were found to be effective for the automatic recognition of boredom by achieving classification accuracies around 85 percent. Then, the joint use of moments and moment variations with conventional features was found to significantly improve classification accuracy by producing a maximum correct classification ratio of 94.17 percent.

Using sign language, speech, and haptics as communication modalities, a virtual treasure-hunting game serves as an entertainment and educational tool for visually and hearing impaired users. In recent years, there has been an increasing interest in human-computer interaction (HCI) for multimodal interfaces. Since Sutherland's SketchPad in 1961 and Xerox's Alto in 1973, computer users have long been acquainted with technol ogies other than the traditional keyboard for interacting with a system. Recently, with the desire for increased productivity, seamless in teraction, immersion, and e-inclusion of people with disabilities, along with progress in fields such as multimedia, multimodal signal analy sis, and HCI, multimodal interaction has emerged as an active field of research.

This paper presents a novel framework for partial matching and retrieval of 3-D models based on a query-by-range-image approach. Initially, salient features are extracted for both the query range image and the 3-D target model. The concept behind the proposed algorithm is that, for a 3-D object and a corresponding query range image, there should be a virtual camera with such intrinsic and extrinsic parameters that would generate an optimum range image, in terms of minimizing an error function that takes into account the salient features of the objects, when compared to other parameter sets or other target 3-D models. In the context of the developed framework, a novel method is also proposed to hierarchically search in the parameter space for the optimum solution. Experimental results illustrate the efficiency of the proposed approach even in the presence of noise or occlusion.

This letter presents a novel framework for gait recognition augmented with soft biometric information. Geometric gait analysis is based on Radon transforms and on gait energy images. User height and stride length information is extracted and utilized in a probabilistic framework for the detection of soft biometric features of substantial discrimination power. Experimental validation illustrates that the proposed approach for integrating soft biometric features in gait recognition advances significantly the identification and authentication performance.

In this paper, a novel approach for 3-D Shape description and retrieval based on the theory of ellipsoidal harmonics is presented. Four novel descriptors are introduced: the surface ellipsoidal harmonics descriptor, which concerns 3-D objects that are described as polygonal surfaces; the volumetric ellipsoidal harmonics descriptor, which is applicable to volumetric 3-D objects; the generalized ellipsoidal harmonics descriptor that is applied to any local 3-D object descriptors; and, finally, the combined ellipsoidal-spherical harmonics descriptor, which leads to a compact and powerful descriptor that inherits the advantages of both approaches: the rotation invariance properties of the spherical harmonics and the directional information enclosed in ellipsoidal harmonics. Experimental results performed using well-known 3-D object databases prove the retrieval efficiency of the proposed approach.

Search for and delivery of multimedia content will be of great importance in the years to come. Common data formats and appropriate architectures that will pave the way toward this challenging task must be defined. In this article a framework enabling mobile device users to search for and retrieve multimedia objects over a distributed P2P-based network is presented. The framework was an outcome of the research that took place within the VICTORY European project. The proposed concept is to develop a P2P-based application that, having as a basis the state of the art in the field of 3D search algorithms, would be able to deliver search and retrieval functionalities to users on the move through mobile devices. Following a service-oriented approach, a framework able to deliver these functionalities to mobile user agents in a device- and application-independent way has been implemented.

Human authentication using biometric traits has become an increasingly important issue in a large range of applications. In this paper, a novel channel coding approach for biometric authentication based on distributed source coding principles is proposed. Biometric recognition is formulated as a channel coding problem with noisy side information at the decoder and error correcting codes are employed for user verification. It is shown that the effective exploitation of the noise channel distribution in the decoding process improves performance. Moreover, the proposed method increases the security of the stored biometric templates. As a case study, the proposed framework is employed for the development of a novel gait recognition system based on the extraction of depth data from human silhouettes and a set of discriminative features. Specifically, gait sequences are represented using the radial and the circular integration transforms and features based on weighted Krawtchouk moments. Analytical models are derived for the effective modeling of the correlation channel statistics based on these features and integrated in the soft decoding process of the channel decoder. The experimental results demonstrate the validity of the proposed method over state-of-the-art techniques for gait recognition.

This paper presents a novel framework for 3-D object content-based search and retrieval, appropriate for both partial and global matching applications. The framework is based on a graph representation of a 3-D object which is enhanced by local geometric features. The 3-D object is decomposed into meaningful parts and an attributed graph is constructed based on the connectivity of the parts. Every 3-D part is approximated with a suitable superellipsoid and a novel 3-D shape descriptor, called a 3-D distance field descriptor, is computed and associated to the corresponding graph nodes. The matching process used is based on attributed graph matching algorithm appropriate for this application. The proposed method not only provides successful retrieval results in terms of geometric similarity but also is invariant to rotation, translation and scaling of an object as well as to the different poses of articulated objects. Finally, it can be effectively used for partial and global 3-D object retrieval.

This paper proposes an innovative gait identification and authentication method based on the use of novel 2-D and 3-D features. Depth-related data are assigned to the binary image silhouette sequences using two new transforms: the 3-D radial silhouette distribution transform and the 3-D geodesic silhouette distribution transform. Furthermore, the use of a genetic algorithm is presented for fusing information from different feature extractors. Specifically, three new feature extraction techniques are proposed: the two of them are based on the generalized radon transform, namely the radial integration transform and the circular integration transform, and the third is based on the weighted Krawtchouk moments. Extensive experiments carried out on USF ldquoGait Challengerdquo and proprietary HUMABIO gait database demonstrate the validity of the proposed scheme.

We introduce a force-field haptic rendering method for converting visual data to haptic data. The method includes a novel framework to convert specialized 3D map models into force fields. We generate the final force fields by using structure from motion and implicit surface approximation algorithms. Visually impaired people then can learn to navigate with these maps, using off-the-shelf haptic

Measuring the similarity between three-dimensional (3-D) objects is a challenging problem, with applications in computer vision, molecular biology, computer graphics, and many other areas. This paper describes a novel method for 3-D model content-based search based on the 3-D Generalized Radon Transform and a querying by-3-D-model approach. A set of descriptor vectors is extracted using the Radial Integration Transform (RIT) and the Spherical Integration Transform (SIT), which represent significant shape characteristics. After the proper alignment of the models, descriptor vectors are produced which are invariant in terms of translation, scaling and rotation. Experiments were performed using three different databases and comparing the proposed method with those most commonly cited in the literature. Experimental results show that the proposed method is adequately satisfactory in terms of both precision versus recall and time needed for retrieval, and that it can be used for 3-D model search and retrieval in a highly efficient manner.

This paper presents a novel object-based method for the generation of a stereoscopic image sequence from a monoscopic video, using bidirectional two-dimensional motion estimation for the recovery of rigid motion and structure and a Bayesian framework to handle occlusions. The latter is based on extended Kalman filters and an efficient method for reliably tracking object masks. Experimental results show that the layered object scene representation, combined with the proposed algorithm for reliably tracking object masks throughout the sequence, yields very accurate results.

The present paper proposes a novel method for content-based search in a database of VRML three-dimensional (3-D) models. The proposed technique is based on a querying-by-3-D-model approach. A set of shape-based descriptors are extracted from the reference 3-D model and compared to the corresponding descriptors of the VRML models contained in the database. The descriptors used vary from simple geometric measurements such as the aspect ratio or a binary 3-D shape mask to more complex and sophisticated shape-based criteria such as the edge paths of each 3-D model. Similarity measures are then introduced for the specific descriptors and introduced into a 3-D model-matching algorithm. Experimental results are presented, evaluating the performance of the proposed method.

A novel frequency-domain technique for image blocking artifact detection and reduction is presented. The algorithm first detects the regions of the image which present visible blocking artifacts. This detection is performed in the frequency domain and uses the estimated relative quantization error calculated when the discrete cosine transform (DCT) coefficients are modeled by a Laplacian probability function. Then, for each block affected by blocking artifacts, its DC and AC coefficients are recalculated for artifact reduction. To achieve this, a closed-form representation of the optimal correction of the DCT coefficients is produced by minimizing a novel enhanced form of the mean squared difference of slope for every frequency separately. This correction of each DCT coefficient depends on the eight neighboring coefficients in the subband-like representation of the DCT transform and is constrained by the quantization upper and lower bound. Experimental results illustrating the performance of the proposed method are presented and evaluated.

This paper presents a novel procedure for the representation and coding of three-dimensional (3-D) surfaces using hierarchical adaptive triangulation. The proposed procedure is based on pyramidal analysis using the quincunx sampling minimum variance interpolation (QMVINT) filters. These are reduced pyramids with quincunx sampling applied to the parametric representation of the surface, chosen so as to minimize the variance of the interpolation error, and thus, when combined with the appropriate encoding of the coefficients, optimize the compression of the mesh information transmitted. At the same time, it produces a hierarchy of meshes based on quincunx sampling where coarse meshes are as similar to their finer versions as possible. This is very much desirable in progressive transmission. Depending on its interpolation error and the available bitrate, each filtered sample is a candidate for becoming a vertex of the mesh. The result is a progressive sequence of meshes consisting of more triangles wherever large variations exist and fewer in uniform regions. Complete correspondence between triangles at each level is identified, resulting in an efficient hierarchical representation of the mesh. The algorithm can be also used for the triangulation of a specific region of interest. Experimental results demonstrate that the proposed scheme provides an improvement in quality (MSE) by a factor of two when compared with other well known adaptive triangulation schemes.

Efficient techniques are introduced in this paper for the identification of the occlusion and visible background and foreground areas in a noisy stereoscopic image pair. Three different Bayes decision methods are tested for this purpose. The first, and uses three hypotheses for the formulation of the Bayes decision rules, adopting the right image as a reference. After performing a dual-Bayes decision test having each time as a different image of the stereo pair as reference, consistency checking is added to these tests to form the second method. Finally, four compound hypotheses are used in the third method, which is the most accurate but also the more detailed and computationally involved of three. Experimental results illustrating the performance of the techniques are presented and evaluated.

In this paper, a procedure is described for deformable boundary detection of medical tools, called stents, in angiographic images. A stent is a surgical stainless steel coil that is placed in the artery in order to improve blood circulation in regions where a stenosis has appeared. Assuming initially a set of three-dimensional (3-D) models of stents and using perspective projection of various deformations of the 3-D model of the stent, a large set of synthetic two-dimensional (2-D) images of stents is constructed. These synthetic images are then used as a training set for deriving a multivariate Gaussian density estimate based on eigenspace decomposition and formulating a maximum-likelihood estimation framework in order to reach an initial rough estimate for automatic object recognition. The silhouette of the detected stent is then refined by using a 2-D active contour (snake) algorithm integrated with a novel iterative initialization technique, which takes into consideration the geometry of the stent. The algorithm is experimentally evaluated using real angiographic images containing stents.

An object-based coding scheme is proposed for the coding of a stereoscopic image sequence using motion and disparity information. A hierarchical block-based motion estimation approach is used for initialization, while disparity estimation is performed using a pixel-based hierarchical dynamic programming algorithm. A split-and-merge segmentation procedure based on three-dimensional (3-D) motion modeling is then used to determine regions with similar motion parameters. The segmentation part of the algorithm is interleaved with the estimation part in order to optimize the coding performance of the procedure. Furthermore, a technique is examined for propagating the segmentation information with time. A 3-D motion-compensated prediction technique is used for both intensity and depth image sequence coding. Error images and depth maps are encoded using discrete cosine transform (DCT) and Huffman methods. Alternately, an efficient wireframe depth modeling technique may be used to convey depth information to the receiver. Motion and wireframe model parameters are then quantized and transmitted to the decoder along with the segmentation information. As a straightforward application, the use of the depth map information for the generation of intermediate views at the receiver is also discussed. The performance of the proposed compression methods is evaluated experimentally and is compared to other stereoscopic image sequence coding schemes.

A rate-distortion framework is used to define a very low bit-rate coding scheme based on quadtree segmentation and optimized selection of motion estimators. This technique achieves maximum reconstructed image quality under the constraint of a target bit rate for the coding of the vector field and segmentation information. First, a complete scheme is proposed for hybrid two-dimensional (2-D) and three-dimensional (3-D) motion estimation and compensation. The quadtree object segmentation is optimized for hybrid motion estimation in the rate-distortion sense. This scheme adapts to the depth of the quadtree and the technique used for motion estimation for each leaf of the tree. A more sophisticated technique, adapted to the requirements of a very low bit-rate coder, is also proposed which also considers the transmission of the prediction error corresponding to the particular choice of the motion estimator. Based on these coding schemes, two versions of a very low bit-rate image sequence coder are developed. Experimental results illustrating the performance of the proposed techniques in very low bit-rate image sequence coding application areas are presented and evaluated.

In the above letter (ibid., vol. 17, no. 4, pp. 367-370, Apr. 10), equation (5) requires modification. The revised equation is presented here.