Investigating fuzzy DLs-based reasoning in semantic image analysis

Recent advances in semantic image analysis have brought forth generic methodologies to support concept learning at large scale. The attained performance however is highly variable, reflecting effects related to similarities and variations in the visual manifestations of semantically distinct concepts, much as to the limitations issuing from considering semantics solely in the form of perceptual representations. Aiming to enhance performance and improve robustness, we investigate a fuzzy DLs-based reasoning framework, which enables the integration of scene and object classifications into a semantically consistent interpretation by capturing and utilising the underlying semantic associations. Evaluation with two sets of input classifiers, configured so as to vary with respect to the wealth of concepts’ interrelations, outlines the potential of the proposed approach in the presence of semantically rich associations, while delineating the issues and challenges involved.     

Multi-view human movement recognition based on fuzzy distances and linear discriminant analysis

In this paper, a novel multi-view human movement recognition method is presented. A novel representation of multi-view human movement videos is proposed that is based on learning basic multi-view human movement primitives, called multi-view dynemes. The movement video is represented in a new feature space (called dyneme space) using these multi-view dynemes, thus producing a time invariant multi-view movement representation. Fuzzy distances from the multi-view dynemes are used to represent the human body postures in the dyneme space. Three variants of Linear Discriminant Analysis (LDA) are evaluated to achieve a discriminant movement representation in a low dimensionality space. The view identification problem is solved either by using a circular block shift procedure followed by the evaluation of the minimum Euclidean distance from any dyneme, or by exploiting the circular shift invariance property of the Discrete Fourier Transform (DFT). The discriminant movement representation combined with camera viewpoint identification and a nearest centroid classification step leads to a high human movement classification accuracy.     

Online learning and fusion of orientation appearance models for robust rigid object tracking

We introduce a robust framework for learning and fusing of orientation appearance models based on both texture and depth information for rigid object tracking. Our framework fuses data obtained from a standard visual camera and dense depth maps obtained by low-cost consumer depth cameras such as the Kinect. To combine these two completely different modalities, we propose to use features that do not depend on the data representation: angles. More specifically, our framework combines image gradient orientations as extracted from intensity images with the directions of surface normals computed from dense depth fields. We propose to capture the correlations between the obtained orientation appearance models using a fusion approach motivated by the original Active Appearance Models (AAMs). To incorporate these features in a learning framework, we use a robust kernel based on the Euler representation of angles which does not require off-line training, and can be efficiently implemented online. The robustness of learning from orientation appearance models is presented both theoretically and experimentally in this work. This kernel enables us to cope with gross measurement errors, missing data as well as other typical problems such as illumination changes and occlusions. By combining the proposed models with a particle filter, the proposed framework was used for performing 2D plus 3D rigid object tracking, achieving robust performance in very difficult tracking scenarios including extreme pose variations.     

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.     

Blind robust watermarking schemes for copyright protection of 3D mesh objects

In this paper, two novel methods suitable for blind 3D mesh object watermarking applications are proposed. The first method is robust against 3D rotation, translation, and uniform scaling. The second one is robust against both geometric and mesh simplification attacks. A pseudorandom watermarking signal is cast in the 3D mesh object by deforming its vertices geometrically, without altering the vertex topology. Prior to watermark embedding and detection, the object is rotated and translated so that its center of mass and its principal component coincide with the origin and the z-axis of the Cartesian coordinate system. This geometrical transformation ensures watermark robustness to translation and rotation. Robustness to uniform scaling is achieved by restricting the vertex deformations to occur only along the r coordinate of the corresponding (r, /spl theta/, /spl phi/) spherical coordinate system. In the first method, a set of vertices that correspond to specific angles /spl theta/ is used for watermark embedding. In the second method, the samples of the watermark sequence are embedded in a set of vertices that correspond to a range of angles in the /spl theta/ domain in order to achieve robustness against mesh simplifications. Experimental results indicate the ability of the proposed method to deal with the aforementioned attacks.     

A review of current research in manufacturing shop design integration

The importance of the manufacturing shop design in the successful operation of a production system is well documented and, as a result, significant work has been devoted to this area. Due to the inherent complexity of the global shop design problem, much research has focused on individual sub-problems, including the layout of manufacturing resources, the design of the shop aisle network, and the selection of the material handling equipment, rather than addressing the integrated problem directly. This paper reviews literature contributions to shop design integration, as well as partial integration schemes such as the design for operation and control of material handling systems. Furthermore, it examines the ability of models and solution approaches for individual sub-problems of shop design to support integration schemes.     

The impact of computer self-efficacy,situational interest and academic self-concept in virtual communities of inquiry during the distance learning procedures through Second Life

The current study investigates a case where the online learning procedure in three-dimensional (3D) technologically-advanced environments of the Web 2.0 is growing at an exponential rate. In this occasion it is highly imperative need to understand students’ interactions in this innovative mode of e-Education that requires from educators and scholars not only analysis conceptually, but also an empirically-driven optimization. The community of inquiry (CoI) model (or framework) consists to be as one of the most prominent multi-dimensional constructs that it is widely used to represent several distinct dimensions of social presence, teaching presence and cognitive presence, as a unique and fundamental theoretical concept to measure students’ interactions in contemporary electronic environments. Although, the effectiveness of these multi-dimensional constructs creates a dilemma to researchers who want the breadth and comprehensiveness of this model for the precision and clarity of users’ (instructors and students) dimensions with other motivational and learning variables. To address this dilemma, the current empirical study presents statistical analyses from the “trinity” constructs of the CoI model by utilizing correlation and hierarchical regression analyses with two fundamental motivational (computer self-efficacy and situational interest) and another one learning (academic self-concept) variables. This study goes one step further and introduces the conspicuously indisputable intervention of a virtual (V)CoI and its utilization in multi-user virtual worlds, like Second Life (SL). The study findings of one hundred thirty-five (135) participants who enrolled in several online sessions unveiled that the situational interest was the only significant predictor of social presence. The computer self-efficacy was not a significant predictor of the CoI model, while on the other hand academic self-concept was a significant predictor in a revamped attempt to validate the strong relationship among constructs within it. According to the aforementioned reasons, it can be surmised that the successful combination of the VCoI in Second Life, surpassing irrefutable and inherent shortcomings to a future-driven sustainable use and growth.     

Enabling customers engagement and collaboration for small and medium-sized enterprises in ubiquitous multi-channel ecosystems

Over the last few years, we have encountered an exponential growth in online communication opportunities. Organizations have more and more ways to connect and engage with their current or future customers. The existence of more opportunities in connecting to people can be both an enabler and a burden. Being present at a multitude of different channels requires the effective management of a very large number of adapted contents, formats, and interaction patterns fulfilling the communication and cooperation needs of distributed target groups. In this respect, we integrate existing fragmented communication and monitoring approaches into a full-fledged communication model as a basis for an adequate engagement approach. We describe applications of our approach in both the eTourism and manufacturing domain. In this paper, we introduce an approach that will enable communication, collaboration and value exchange of users through a multitude of online interaction possibilities based on the use of semantic technology. Finally, we also compare our approach with existing solutions with respect to the identified challenges in this subject.