Intelligent mesh scissoring using 3D snakes
Mesh partitioning and parts extraction have become key ingredients for many mesh manipulation applications both manual and automatic. In this paper, we present an intelligent scissoring operator for meshes which supports both automatic segmentation and manual cutting. Instead of segmenting the mesh by clustering, our approach concentrates on finding and defining the contours for cutting. This approach is based on the minima rule, which states that human perception usually divides a surface into parts along the contours of concave discontinuity of the tangent plane. The technique uses feature extraction to find such candidate feature contours. Subsequently, such a contour can be selected either automatically or manually, or the user may draw a 2D line to start the scissoring process. The given open contour is completed to form a loop around a specific part of the mesh, and this loop is used as the initial position of a 3D geometric snake. The snake moves by relaxation until it settles to define the final scissoring position. This process uses several fundamental geometric mesh attributes, such as curvature and centricity, and enables both automatic segmentation and an easy-to-use intelligent-scissoring operator.

title: Out-of-Core Remeshing of Large Polygonal Meshes
abstract: We propose an out-of-core method for creating semi-regular surface representations from large input surface meshes. Our approach is based on a streaming implementation of the MAPS remesher of Lee et al. Our remeshing procedure consists of two stages. First, a simplification process is used to obtain the base domain. During simplification, we maintain the mapping information between the input and the simplified meshes. The second stage of remeshing uses the mapping information to produce samples of the output semi-regular mesh. The out-of-core operation of our method is enabled by the synchronous streaming of a simplified mesh and the mapping information stored at the original vertices. The synchronicity of two streaming buffers is maintained using a specially designed write strategy for each buffer. Experimental results demonstrate the remeshing performance of the proposed method, as well as other applications that use the created mapping between the simplified and the original surface representations

title: Motion retargeting and evaluation for VR-based training of free motions
abstract: Virtual reality (VR) has emerged as one of the important and effective tools for education and training. Most VR-based training systems are situation based, where the trainees are trained for discrete decision making in special situations presented by the VR environments. In contrast, this paper discusses the application of VR to a different class of training, for learning free motion, often required in sports and the arts. We propose a VR-based motion-training framework that contains an intuitive motion-guiding interface, posture-oriented motion retargeting, and an evaluation and advice scheme for corrective feedback. Applications of the proposed framework to simple fencing training and a dance imitation game are demonstrated .

title: Motif analysis of noisy regular textures
abstract: A regular texture contains a periodic arrangement of a determinable pattern. Motifs of a regular texture are the minimal parallelogrammic sub-regions that can reproduce the texture by tiling. A motif extracted from a regular texture can be used for texture reproduction, texture recognition, and texture compression. However, it is not simple to accurately capture a motif of a regular texture, as the motif may be non-rectangular or arranged in an arbitrary direction. Moreover, when the texture contains additive noises, such as are created by the scanning process, motif analysis becomes more difficult. In this paper, we present an algorithm to obtain a motif of a noisy regular texture in O( mn log mn ) time, where m × n is the size of the texture. The analysis examples demonstrate that the proposed algorithm can robustly derive motifs of noisy regular textures.

title: Mesh parameterization with a virtual boundary
abstract: Parameterization of a 3D triangular mesh is a fundamental problem in various applications of meshes. The convex combination approach is widely used for parameterization because of its good properties, such as fast computation and one-to-one embedding. However, the approach has a drawback: most boundary triangles have high distortion in the embedding compared with interior ones. In this paper, we present an extension of the convex combination approach that resolves the drawback by using a virtual boundary. Since the virtual boundary is fixed onto a given convex polygon instead of the real boundary, the real boundary triangles can better reflect the shape of the corresponding 3D triangles. The proposedapproach obtains a parameterization of a 3D mesh with less d istortion than with the original convex combination approach.

title: Feature sensitive mesh segmentation with mean shift
abstract: Feature sensitive mesh segmentation is important for many computer graphics and geometric modeling applications. In this paper, we develop a mesh segmentation method, which is capable of producing high-quality shape partitioning. It respects fine shape features and works well on various types of shapes, including natural shapes and mechanical parts. The method combines a procedure for clustering mesh normals with a modification of the mesh clarification technique. For clustering of mesh normals, we adopt Mean Shift, a powerful general purpose technique for clustering scattered data. We demonstrate advantages of our method by comparing it with two state-of-the-art mesh segmentation techniques.

title: Cooperative peer groups in NICE
abstract: A distributed scheme for trust inference in peer-to-peer networks is presented. Our work is in context of the NICE system, which is a platform for implementing cooperative applications over the Internet. We describe a technique for efficiently storing user reputation information in a completely decentralized manner, and show how this information can be used to efficiently identify noncooperative users in NICE. We present a simulation based study of our algorithms, in which we show our scheme scales to thousands of users using modest amounts of storage, processing, and bandwidth at any individual node. Lastly, we show that our scheme is robust and can form cooperative groups in systems where the vast majority of users are malicious.

title: Out-of-Vocabulary Rejection based on Selective Attention Model
abstract: Based on a psychological selective attention theory a new algorithm is developed to provide reliable out-of-vocabulary (OOV) rejection for speech recognition systems in noisy environments. The developed attention model is based on Broadbent's ‘early filtering’ theory, and the attention adaptation process utilizes a gradient-descent error minimization algorithm with error backpropagation rule. The developed model is applied to isolated-word recognition tasks, and much higher in-vocabulary recognition rates are achieved with the same out-of-vocabulary rejection rates.

title: Blind signal separation and independent component analysis
abstract: Independent component analysis (ICA) is emerging as a new paradigm in signal processing and data analysis. ICA has been proposed as a solution to the blind source separation problem (BSS) in which sensory signals are given as mixtures of unknown source signals. Since neither the sources nor the mixing environment are known a priori there is no general analytical solution to this problem. However, since the last decade researchers have found good approximate solutions based on assumptions about the source statistics. They were able to formulate solutions or algorithms that were derived from well-known cost functions including maximum likelihood estimation, information maximization and higher-order cumulants. The ICA framework touches many theoretical research areas, and therefore receives attention in several research communities including machine learning, neural networks, statistical signal processing and Bayesian modeling. More recently, there have been numerous applications of ICA in adaptive 0ltering, speech signal processing, biomedical signal processing, computational neuroscience, image coding, text data modeling, and 0nancial data analysis. The standard ICA formulation has, in general, strong assumptions or simpli0cations such as: the number of sensors has to be equal or greater than the number of sources, there is no additive noise signal in the sensors, and the sources are modeled as random variables. However, when applying ICA to real-world problems it becomes evident that those restrictions are cumbersome and although the standard ICA algorithms give good approximate solutions, there is great need in going beyond the standard assumptions and develop new algorithms that can relax some of the assumptions. This special issue on ICA and BSS is a timely publication covering important recent 0ndings in ICA theory as well as new applications of ICA in data analysis. Leading researchers present their work to advance this 0eld beyond traditional borders and show how this 0eld can bridge disciplines and yield new methods that lead to mutual bene0ts and advances in science and engineering. This issue is composed of a review article and two parts: the 0rst part presents new theoretical 0ndings while the second part presents novel applications. Often, there is no such sharp division into these two parts since illustrative simulations support theoretical 0ndings and practical methods include algorithmic modi0cations to design and evaluate the application-speci0c methods. In “Frontiers of research in BSS=ICA”, V. David S: anchez A. formulates some of the basic concepts and problems in the areas of blind signal separation and independent component analysis. He then moves to a concise description of key methods of data preprocessing and algorithms that provide solutions to the instantaneous mixture and the

title: Designing Semistructured Databases: A Conceptual Approach
abstract: Semistructured data has become prevalent with the growth of the Internet. The data is usually stored in a database system or in a specialized repository. Many information providers have presented their databases on the web as semistructured data, while others are developing repositories for new applications. Designing a "good" semistructured database is important to prevent data redundancy and updating anomalies. In this paper, we propose a conceptual approach to design semistructured databases. A conceptual layer based on the Entity-Relationship model is used to remove redundancies at the semantic level. An algorithm to map an ER diagram involving composite attributes weak entity types, recursive, n-ary and ISA relationship sets, and aggregations to a semistructured schema graph (S3-Graph) is also given.

title: Performance of Group Key Agreement Protocols over Multiple Operations
abstract: ∗Shanyu Zheng and Jim Alves-FossCenter for Secure and Dependable SystemsUniversity of IdahoMoscow, ID 83844,  LeeDepartment of StatisticsUnversity of IdahoMoscow, ID 83844,  stevel@uidaho.eduABSTRACTA few group key protocols are analyzed, implemented anddeployed, but the costs associated with them have beenpoorly understood. Their analysis of group key agreementsperformance is based on the cost of performing a single op-eration. In this paper we extend this analysis to examine theperformance behavior of ﬁve group key protocols after ex-ecution of multiple operation. We report our experimentalresults for 100 operations consist of combinations of join,leave, mass join, mass leave, merge, and partition. In orderto thoroughly compare the performance of ﬁve protocols,we simulate three group operations: join-leave-mass join-mass leave, merge-partition, and join-leave-mass join-massleave-merge-partition to observe what is not apparent fromthe theoretical analysis.KEY WORDSgroup key management, group communications, crypto-graphic protocols