Out-of-Core Remeshing of Large Polygonal Meshes
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: Feature-based surface light field morphing
abstract: A surface light field is a function that gives the colors of each object point viewed from different directions. Object representation with a surface light field provides a nice structure for 3D photography. This paper presents a feature-based morphing technique for two objects equipped with surface light fields. The technique consists of geometry morphing and in-between light field mapping. Geometry morphing is accomplished by 3D mesh morphing, where we introduce a vertex merging technique to generate a simpler metamesh. In in-between light field mapping, an in-between object is rendered by extracting necessary fragments from input surface light fields. We also propose an acceleration technique for rendering an in-between object. Experimental results with real and synthetic data show natural and plausible morphing between objects with surface light fields. The proposed morphing technique can be used as an editing tool for 3D photography.

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: 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: 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: Light field morphing using 2D features
abstract: We present a 2D feature-based technique for morphing 3D objects represented by light fields. Existing light field morphing methods require the user to specify corresponding 3D feature elements to guide morph computation. Since slight errors in 3D specification can lead to significant morphing artifacts, we propose a scheme based on 2D feature elements that is less sensitive to imprecise marking of features. First, 2D features are specified by the user in a number of key views in the source and target light fields. Then the two light fields are warped view by view as guided by the corresponding 2D features. Finally, the two warped light fields are blended together to yield the desired light field morph. Two key issues in light field morphing are feature specification and warping of light field rays. For feature specification, we introduce a user interface for delineating 2D features in key views of a light field, which are automatically interpolated to other views. For ray warping, we describe a 2D technique that accounts for visibility changes and present a comparison to the ideal morphing of light fields. Light field morphing based on 2D features makes it simple to incorporate previous image morphing techniques such as nonuniform blending, as well as to morph between an image and a light field.

title: Calculative-based trust and social welfare
abstract: Trust building has been acknowledged as one of the critical factor for the success of e-commerce. However, few sources of trust were identified in online transaction. This paper tries to fill the gap by investigating calculative-based trust. Specifically, an infinitely repeated game model is built to illustrate how strangers in online transaction build trust relationship based on the calculation of their own benefits. Furthermore, we relate the strategies of trading parties to social welfare and provide policy implications on how to control the cheating behaviors without jeopardizing social welfare.

title: Responsive Multimedia System for Virtual Storytelling
abstract: In this paper, we propose Responsive Multimedia System (RMS) for a virtual storytelling. It consists of three key components; Multi-modal Tangible User Interface (MTUI), a Unified Context-aware Application Model for Virtual Environments (vr-UCAM), and Virtual Environment Manager (VEManager). MTUI allows users to interact with virtual environments (VE) through human’s senses by exploiting tangible, haptic and vision-based interfaces. vr-UCAM decides reactions of VE according to multi-modal input. VEManager generates dynamic VE by applying the reactions and display it through 3D graphics and 3D sounds, etc. To demonstrate an effectiveness of the proposed system, we implemented a virtual storytelling system which unfolds a legend of Unju Temple. We believe the proposed system plays an important role in implementing various entertainment applications.

title: Embedding Technical Analysis Into Neural Network Based Trading Systems
abstract: We have recently proposed a promising trading system for the S P 500 index which consists of a feature selection component and a simple filter for data preprocessing two specialized neural networks for return prediction and a rule base for prediction integration. The objective of this study is to explore if including additional knowledge for more sophisticated data filter ing and return integration leads to further improvements in the system. The new system uses a well known technical indicator to split the data and an additional indicator for reducing the number of unprofitable trades. Several system combinations are explored and tested over a 5 year trading period. The most promising system yielded an annual rate of return ARR of 15.99% with 54 trades. This compares favorably to the ARR for the buy and hold strategy 11.05% and to the best results obtained using the system with no technical analysis knowl edge embedded 13.35% with 126 trades.

title: Link-disjoint embedding of complete binary trees in meshes

title: Scalable resilient media streaming
abstract: We present a low-overhead media streaming system, called SRMS (Scalable Resilient Media Streaming) that can be used to scalably deliver streaming data to a large group of receivers. SRMS uses overlay multicast for data distribution. to a large group of users. SRMS leverages a probabilistic loss recovery technique to provide high data delivery guarantees even under large network losses and overlay node failures. The clients in the SRMS system are able to interoperate with existing media streaming servers that use RTP for data transport. One of the interesting features of SRMS is that it can simultaneously support clients with disparate access bandwidths. It enables the necessary bandwidth adaptations using standard Real-time Transport Protocol (RTP) mechanisms, e.g. RTP translators. We have implemented and evaluated the SRMS system in detail on an emulated network as well as on a wide-area testbed with up to 128 clients. Our results show that clients using SRMS achieve high (97%) data delivery ratios with low overheads (<5%) even for a very dynamic network (up to five membership changes per minute).