Despite the steady growth of e-commerce communities in the past two decades, little has changed in the way these communities manage reputation for building trust and for protecting their member's financial interests against fraud. As these communities mature and the defects of their reputation systems are revealed, further potential for deception against their members is created, that pushes the need for novel reputation mechanisms. Although a high volume of research works has explored the concepts of reputation and trust in e-communities, most of the proposed reputation systems target decentralized e-communities, focusing on issues related with the decentralized reputation management; they have not thus been integrated in e-commerce platforms. This work's objective is to provide an attackresilient feedback-based reputation system for modern e-commerce platforms, while minimizing the incurred financial burden of potent security schemes. Initially, we discuss a series of attacks and issues in reputation systems and study the different approaches of these problems from related works, while also considering the structural properties, defense mechanisms and policies of existing platforms. Then we present our proposition for a robust reputation system which consists of a novel reputation metric and attack prevention mechanisms. Finally, we describe the simulation framework and tool that we have implemented for thoroughly testing and evaluating the metric's resilience against attacks and present the evaluation experiments and their results. We consider the presented simulation framework as the second contribution of our article, aiming at facilitating the simulation and elaborate evaluation of reputation systems which specifically target e-commerce platforms by thoroughly presenting it, exhibiting its usage and making it available to the research community.

The cast shadows in an image provide important information about illumination and geometry. In this paper, we utilize this information in a novel framework in order to jointly recover the illumination environment, a set of geometry parameters, and an estimate of the cast shadows in the scene given a single image and coarse initial 3D geometry. We model the interaction of illumination and geometry in the scene and associate it with image evidence for cast shadows using a higher order Markov Random Field (MRF) illumination model, while we also introduce a method to obtain approximate image evidence for cast shadows. Capturing the interaction between light sources and geometry in the proposed graphical model necessitates higher order cliques and continuous-valued variables, which make inference challenging. Taking advantage of domain knowledge, we provide a two-stage minimization technique for the MRF energy of our model. We evaluate our method in different datasets, both synthetic and real. Our model is robust to rough knowledge of geometry and inaccurate initial shadow estimates, allowing a generic coarse 3D model to represent a whole class of objects for the task of illumination estimation, or the estimation of geometry parameters to refine our initial knowledge of scene geometry, simultaneously with illumination estimation.

In this paper, we propose a novel framework to jointly recover the illumination environment and an estimate of the cast shadows in a scene from a single image, given coarse 3D geometry. We describe a higher-order Markov Random Field (MRF) illumination model, which combines low-level shadow evidence with high-level prior knowledge for the joint estimation of cast shadows and the illumination environment. First, a rough illumination estimate and the structure of the graphical model in the illumination space is determined through a voting procedure. Then, a higher order approach is considered where illumination sources are coupled with the observed image and the latent variables corresponding to the shadow detection. We examine two inference methods in order to effectively minimize the MRF energy of our model. Experimental evaluation shows that our approach is robust to rough knowledge of geometry and reflectance and inaccurate initial shadow estimates. We demonstrate the power of our MRF illumination model on various datasets and show that we can estimate the illumination in images of objects belonging to the same class using the same coarse 3D model to represent all instances of the class.

Illuminant estimation from shadows typically relies on accurate segmentation of the shadows and knowledge of exact 3D geometry, while shadow estimation is difficult in the presence of texture. These can be onerous requirements; in this paper we propose a graphical model to estimate the illumination environment and detect the shadows of a scene with textured surfaces from a single image and only coarse 3D information. We represent the illumination environment as a mixture of von Mises-Fisher distributions. Then, each shadow pixel becomes the combination of samples generated from this illumination environment. We integrate a number of low-level, illumination-invariant 2D cues in a graphical model to detect and estimate cast shadows on textured surfaces. Both 2D cues and approximate 3D reasoning are combined to infer a set of labels that identify the shadows in the image and estimate the positions, shapes and intensities of the light sources. Our results demonstrate that the probabilistic combination of multiple cues, unlike prior approaches, manages to differentiate both hard and soft shadows from the underlying surface texture even when we can only coarsely anticipate the effect of 3D geometry. We also experimentally demonstrate how correct estimation of the sharpness and shape of the light sources improves the augmented reality results.

Abstract In this paper, we present a framework that integrates three‐dimensional (3D) mesh streaming and compression techniques and algorithms into our EVE‐II networked virtual environments (NVEs) platform, in order to offer support for large‐scale environments as well as highly complex world geometry. This framework allows the partial and progressive transmission of 3D worlds as well as of separate meshes, achieving reduced waiting times for the end‐user and improved network utilization. We also present a 3D mesh compression method focused on network communication, which is designed to support progressive mesh transmission, offering a fast and effective means of reducing the storage and transmission needs for geometrical data. This method is integrated in the above framework and utilizes prediction to achieve efficient lossy compression of 3D geometry. Copyright © 2006 John Wiley & Sons, Ltd.

This paper presents the current state of the art in Web3d multi-user technologies and more specifically networked virtual environment platforms that support the X3D standard, which are currently limited in number, and also the existing X3D browsers that enable these applications. Furthermore, this paper presents the architecture of a platform, called EVE, which was developed to support multi-user virtual environments and was initially based on VRML, as well as the implementation regarding the process of upgrading this platform in order to fully support the X3D standard, using the open-source Xj3D library. Finally, the benefits of shifting to the X3D standard are presented, and some of the problems that currently exist are discussed.